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  • Published: 23 December 2022

Data analytics for crop management: a big data view

  • Nabila Chergui 1 &
  • Mohand Tahar Kechadi 1 , 2  

Journal of Big Data volume  9 , Article number:  123 ( 2022 ) Cite this article

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Recent advances in Information and Communication Technologies have a significant impact on all sectors of the economy worldwide. Digital Agriculture appeared as a consequence of the democratisation of digital devices and advances in artificial intelligence and data science. Digital agriculture created new processes for making farming more productive and efficient while respecting the environment. Recent and sophisticated digital devices and data science allowed the collection and analysis of vast amounts of agricultural datasets to help farmers, agronomists, and professionals understand better farming tasks and make better decisions. In this paper, we present a systematic review of the application of data mining techniques to digital agriculture. We introduce the crop yield management process and its components while limiting this study to crop yield and monitoring. After identifying the main categories of data mining techniques for crop yield monitoring, we discuss a panoply of existing works on the use of data analytics. This is followed by a general analysis and discussion on the impact of big data on agriculture.

Introduction

DA, (also called digital farming or smart farming) Footnote 1 [ 78 , 105 , 130 ], is a modern approach that uses digital and smart devices [sensors, cameras, satellite, drones, the Global Positioning System (GPS)] in conjunction with Data Mining (or data analytics) to improve productivity and to optimise the use of resources. Digital Agriculture (DA) comes as a response to the increasing demand for improving productivity while reducing farming operational costs. Moreover, the improvement of productivity should not be done at any cost, e.g., overuse of natural resources and chemical products. DA can, for example, manage crop growth by finding appropriate fertilisation program for each farming field and can help farmers to reduce their operational costs and respect the environment by refining their farming operations based on the needs of each part of the farming field.

Since agriculture has a direct and significant impact on the population and therefore its economic environment, DA in its turn should be viewed as the next natural step to respond to the world population’s needs while protecting the environment, by taking advantage of the recent technological advances in digital devices, communications systems, and artificial intelligence. These allow us to construct multidimensional domains, where the farms and farmers are their central subjects. Figure  1 shows the agriculture ecosystem and its direct impact on other sectors of the economy.

figure 1

The interlocked sectors involved in DA

Besides, since DA involves the development, adoption and iteration with digital technologies [ 39 ], and Artificial Intelligence (data analytics, ...), these developments and interactions should be well-defined (laws, regulations and policies) to guarantee rights and benefits of all the involved actors (farmers, farm holders’, data owners’, developers and analysts, technology vendors’,...) [ 70 , 77 , 78 , 92 , 113 , 146 ].

DA can be regarded as a data driven form of farming, in which decision-making processes are based on explicit information derived from data collected through various sources [ 148 ]. DA and Precision Agriculture (PA) seem to refer to the same thing, however, as stated in [ 148 ], DA involves the development and adoption of modern technologies in both collecting the data and its analysis in various farming contexts, while PA takes into account only the in-field variability [ 147 ]. DA aims to exploit advanced digital devices, ranging from a simple sensor to complex robots, to offer the required farmland treatment with high accuracy. DA can be applied in almost all agricultural fields. For instance, in crop production: DA allows accurate management of crops, which includes fields, wasteland, crop, pest, and irrigation management, soil classification, etc. In Animal production: DA allows monitoring the animal over its whole life cycle, its food quantity, health control and protection from diseases, and so on. Fishery, animal Husbandry, livestock and dairy farming are some examples [ 14 ]. In Forestry: We can efficiently manage forests by supporting the environmental and sustainable decision [ 36 ]. DA can help in detecting unhealthy trees, air pollution, discriminate different tree species, protect the wildlife, etc. From the economy point of view, the application of DA for forest management enhances the wood quality and its production, which can augment profits; reduce waste and maintain the environment [ 138 ].

Addressing DA from all the above mentioned views is a challenging task and cannot be achieved without the participation of specialists from all these sectors. In this study, we focus on the use of Big Data in crop management, it is, not only one of the pillars in agriculture but also it can profoundly affect biodiversity. Moreover, crop growth is a very complex process involving various endogenous and exogenous factors. Recent advances in digital technologies allow us to collect data about all these factors. DA has the ability to elucidate the correlations and interactions of these factor to help farmers and agronomists optimise the productivity while reducing the side effects on the environment. DA exhibits several benefits to agriculture as shown in Figure   2 . These benefits were discussed in [ 10 , 13 , 70 , 98 , 104 , 112 , 113 , 130 , 135 , 148 ] and summarised in the following:

figure 2

Role of DA in crop production process

DA provides a farmer with useful information to support their decision-making processes, such as soil and weather monitoring and prediction, weed and pest monitoring, crop yield dynamic predictions, etc.

DA can sustain the environment and improve the products’ quality, since it provides high quality information and measurements for optimal farming operations on each field.

DA can provide farmers advanced management methods against climate change and other environmental challenges. The farmer can continuously monitor crop growth and protect them against diseases.

DA offers valuable feedback to farmers and good assessment of risks, to minimise microbiological or disaster-related risks.

DA can provide prediction and assistance to farmers against adverse weather incidence, disasters and market instability by assessing the loss at the farm level.

Farmers/agronomists can benefit from advanced models to understand the market and forecast which products could be more profitable.

The contributions of this study are in the investigation of big data analytics applications to crop production. Crop farming is a complex task, and it depends on many factors that should be taken into account. To optimise the operational cost and reduce the impact on the environment, the big data analytics emerges as one of the most cost effective approaches nowadays. The contributions, therefore, include the following:

A comprehensive overview of Digital Agriculture big-data with a presentation of the conceptual-layered framework to show the effectiveness of data analytics on Digital Agriculture, when some necessary steps have been implemented. For instance, large-scale data analytics can only be effective if the historical data is available, carefully collected, and it is of high quality.

A highlight of the different types of data used in the existing studies, and a presentation of the classification of different techniques applied to crop yield monitoring and their effectiveness of the overall results.

A review and analytical studies of the most widely used data mining techniques to crop farming, with a report of their advantages and shortcomings.

A discussion on the advantages of big data in agriculture, and how this can be used efficiently for crop farming and its extension to the agricultural field in general.

A discussion on Digital Agriculture applications for crop management in small and large scale holders.

A discussion on Digital Agriculture challenges and potential paths for future research.

Methodology

To study the impact of data analytics and big data on DA based on previous works, we conducted a systematic review approach that consists of three steps: (1) collection of related work, (2) selection of relevant work, and (3) examination and analysis of the filtered related work.

In the first step, we performed keyword-based research and We gathered a large number of studies from well-known and popular online sources (Web of Science, Scopus, IEEEXplore, ACM, etc.). We used a combination of keywords from the two sets (Big data, data mining, data analytics, machine learning, Internet Of Things, sensors) and (Digital agriculture, smart farming, precision agriculture, agriculture, farming). We gathered more than 327 articles. In the next steps, We selected a small number of articles, which are considered relevant for further analysis, based on their ideas, methods, data types and sources, addressed problems, proposed solutions, tools used and quality of the results.

Through the literature analysis, the study aims to find responses to the following research questions and discuss findings in the following sections.

What is the process of DA for crop management?

What are the various data types generated by farms and used in DA applications for crop management?

What role does big data analytics play in DA?

How are big data analytics used for crop management?

What are the influences of the farm’s scale on the application of DA?

How big is the data used in the proposed DA solutions’?

What are the challenges facing the DA?

Figure  3 summarises the overall approach, adopted from the PRISMA Footnote 2 flow diagram.

figure 3

The research methodology flowchart

Related work

Despite that DA and Big Data being relatively recent research fields, their scientific literature is rich and covers several concepts. As DA is at the cross boundaries between agriculture and ICT, three major dimensions have emerged as of a very high importance; technology, social economics and ethics, and decision-making based on Machine Learning. The first dimension focuses on the use of advanced technologies to improve practices and productivity [ 56 , 124 ]. In Ref. [ 124 ], the authors studied the impact of sensor networks in agriculture, including remote sensing technologies, wireless devices, and other IoT devices. Ref. [ 56 ] reviewed some developments in remote sensing within Big Data processing and management in agriculture. The second dimension concerns legal, ethics, social and economic factors of DA, to provide insights into the impact of digitised information and its analysis on the farm management; farmer identity, skills, privacy, production, and value chains in food systems [ 39 , 70 , 77 , 78 , 92 , 113 , 146 , 148 ]. The third dimension focuses on the application of big data analysis and machine learning (ML), to optimise and forecast the production and the use of resources. In this paper, we only consider this dimension.

Various studies have been conducted on the application of data analytics to crop yield management. For instance, [ 71 ] presented a systematic review on crop yield prediction using ML techniques, and extracted major ML algorithms, features and evaluation metrics used in those studies. Ref.[ 35 ] discussed the yield estimation by integrating agrarian factors in ML techniques. This allowed them to show a strong relationship between crop yield and climatic factors. Ref. [ 103 ] Provided a systematic review on the use of computer vision and AI to enhance the grain quality of five crops (maize, rice, wheat, soybean and barley), disease detection and phenotyping. Ref. [ 64 ] reviewed the application of big data analysis in some fields of agriculture. It highlighted solutions to some key well-known problems, used tools and algorithms, along with input datasets. The authors concluded that big data analytics in agriculture is still at its early stage, and many barriers need to be overcome, despite the availability of the data and tools to analyse it. To measure the level of usage of big data in DA, the authors defined big data metrics (low, medium, high) for each of its dimensions (volume, velocity, and variety). However, while it is a very simple model, it is not easy to specify thresholds, as some dimensions, such as volume and velocity depend on technological advances. Ref. [ 12 ] presented a review on the use of ML methods to detect biotic stress in crop protection. The authors analysed the potential of these techniques and their suitability to deal with crop protection from weeds, diseases and insects. In addition, they provided very good instructive examples from different fields of DA. An earlier similar study was presented in [ 89 ], where the authors studied four very popular learning approaches; Artificial Neural Network (ANN), Support Vector Machine (SVM), K-means, and K-Nearest Neighbour (KNN). Ref. [ 25 ] presented a survey on data mining clustering methods applied to food and agricultural domains. It first described major techniques of unsupervised classification, then it examined some existing techniques applied to agriculture products; like fruit classification, wine classification, analysis of remote sensing in forest images and machine vision.

This study is not just an update of previous surveys. The main objective is to examine the effectiveness of big data analytics in crop yield monitoring and discuss the challenges of such paradigm shift in the agriculture domain. Moreover, It is important to understand the sources of datasets, their types, and which ML techniques are more suitable to analyse them.

DA: it’s all about data

Digital Agriculture (DA) relies heavily on the data sources and techniques used to collect it. This data is then organised in agricultural data warehouses and analysed [ 93 ]. The results of this data analysis provide significant insights to farmers and agronomists about how to improve the production, minimise the farming operational costs, manage risks, and protect the environment. The process of deploying DA is derived from data science.

Digital agriculture process

Figure   4 , adopted from the knowledge pyramid DIKW, shows a data-driven process, which is at the heart of DA. This usually shows how data from past experiences and models serve as input to techniques of mining and analysis to help in future decisions and acting accordingly. The newly collected data will be used to further refine the process and adapt it to an ever-evolving agricultural world.

This is a data-driven methodology derived from the overall knowledge discovery process. The first phase, data collection, is crucial to the validity of the whole analysis. One needs to carefully identify the type of data that should be collected and the approach of gathering it and maintain it through its whole life cycle. This is even more complex in DA, as the data is issued from various and heterogeneous sources, and contains a number of factors of uncertainties. The second phase, data representation and analysis, is very sophisticated, as there is no common standards in the way the data should be integrated, consolidated, to derive a unified representation that is suitable for its analysis, and in the choice of the analysis techniques. Finally, the decision-making is a laborious task, where the extracted knowledge will be associated to the expertise of farmers and agronomists, farming constraints and regulations to derive new management processes with the view to improve productivity and quality of products, reduce and their impact on the environment. Figure   5 depicts a diagram presenting the DA process for crop yield monitoring, as explained below.

figure 4

DA– a data-driven process

Data collection and preparation It is important to identify the data types and attributes based on the problem at hand (e.g., crop management), and the level of granularity of the data. The required data sources should also be identified and assessed for their data quality. As mentioned above, the data is then prepared for analysis. This includes data integration, representation, selection, transformation, etc.

Data analysis the complex nature of the agricultural data requires an elaborative analysis approach, ranging from methods of feature selection or extraction to various learning algorithms to discover models, patterns (or knowledge in general term) for data analysis. These will be evaluated against the expected quality of results and their suitability to a decision-making process.

Decision-making The main goal of the DA process is the decision-making. Any decision should follow the state-of-the-art practice, be justifiable and scientifically sound.

figure 5

Big Data Analytics system architecture for crop yield monitoring

Digital agriculture data

In agriculture, Very large amounts of data can be collected from various sources. These include sensors, weather stations, satellite imagery, drone imagery, and many other instruments. The datasets include weather data, farm records, environmental conditions, soil parameters (nutrients, texture, moisture, and so on. The data is usually rich, large, very complex, and heterogeneous. Therefore, its analysis is not straightforward.

The heterogeneity is not only expressed by the data types and formats, but it can be collected using different equipment of different quality. In addition, historical data may be described with different sets of attributes compared to very recent data. This can present inconsistencies in naming conventions and measures when the data is collected from different locations and times. Moreover, the data can be static and historical, which is considered as offline data, and can be online weather data collected at regular intervals (streams of data values), such as weather data (e.g., every 15 minutes), satellite imagery, which is characterised of being spatio-temporal, such as Geo-spatial data, Moderate-Resolution Imaging Spectroradiometer (MODIS) images, etc.

As mentioned earlier, the data collection is not well tackled in the literature. Most of the studies assume that the data is known already, and the experimental setup was already in place. Therefore, more effort is allocated to the data analysis and interpretation rather than on the complete environmental parameters and conditions. In the following sections, we discuss the data analysis process. This discussion is structured based on the main categories of the data analysis; classification, and clustering [ 24 ]. Note that, for high quality results, the data needs to be pre-processed, as discussed in the previous section. The pre-processing includes cleaning (dealing with missing values, redundant data, noise and outliers), data transformation, dimensionality or data reduction, and so on.

Classification for crop monitoring

Big Data analytics system architecture is depicted in Fig.   5 . While this system is targeted specifically to crop yield management, it can be adapted to any data-driven application. This architecture implements faithfully what we have highlighted in the previous sections. In this section, we will focus on the data analysis layer of the architecture, moreover, we will pay attention to the data types and their sources, techniques of data acquisition, the learning algorithms. The main objective of the crop management data analysis is to get some insights about the crop monitoring problems and show the potential of DA through big data analytics, also called data mining. Data mining and its techniques are involved in several roles in crop production. Farmers may want to know the future yield of their crop, specific areas of their farms suffer from the spread of weeds or under-nutrition. Researchers can look for information such as plant growth patterns, optimum growing conditions, best pest and disease control environment and so on. Data mining offers panoply of sophisticated techniques required to meet all of these needs.

There are two major categories of data analysis: Classification and Clustering. In the work of [ 24 ], authors studied applications of data mining techniques in crop management and proposed a classification of these applications. They found that the classification and clustering are the main used categories, where the classification includes prediction, detection, protection, and categorisation). The choice between classification or clustering analysis is very simple. If the models or classes we are looking for were known in advance and we have an annotated data to support the training of the learning algorithms, then classification is the right choice. However, the annotated data is not always available and easy to generate, and in many cases we do not know even which models or patterns we are looking for. In these situations, clustering analysis is the right alternative.

In this section, we focus on the studies that use classification methods for their data analysis. Clustering analysis will be covered in the next section. We structure these classification studies based on the application objectives or targets which are categorisation , prediction , detection , and protection .

Categorisation

While the classification main objective is to assign a given object into one of the predetermined classes, in the agricultural world, the use of classification process may vary depending on the stakeholders interests. In this study, we report four different applications (or targets) which are widely used in agriculture categorisation , prediction , detection , and protection .

Categorisation aims at defining the classes (or class labels) based on the simple recognition of similarities that exist across a set of entities. For example, categorisation can be used to classify small fruit from fruit with normal to big size, to make an estimation of yields; which may have an economic impact if the farmer wants to make different packages or prices for each type of fruit separately. It can also be used to classify damaged crops from good ones in order to estimate losses, or to prepare for the harvest and marketing. Categorisation can also be applied for crop mapping (e.g., poor, average, high yield), which aims to provide information on farmed fields given a specific type of crops, or to identify a type of crops that are more suitable for a particular field. Based on the input data, categorisation can help improve the farming operations based on the meaningful categories (classes) predefined in advance.

Producing accurate crop maps is essential for effective agricultural monitoring [ 131 ]. Categorisation approaches can be applied to study regional crop distribution within or post growing season. For this purpose, it can offer:

A good understanding of how crops are distributed at early stage of their development; allowing for an opportune decision making and management, as well as adjusting crop planting structure, is crucial. Besides, the timely available of (spatial or maps) distribution of crop types is required for statistical and economic purposes [ 131 ].

The availability of crops maps is critical for the diverse agricultural monitoring activities, such as crop acreage estimation, yield modelling, harvest operation schedules [ 131 , 144 ], etc.

Moreover, categorisation has been applied for agricultural field mapping [ 31 ], to quantify the cropping intensity for small-scale farms [ 58 ], to identify and map crops and to retrieve the area of major cultivation [ 100 ] and to classify land-cover and crop [ 76 ]. Table   1 highlights the major fields, ideas and tools used for crops categorisation. We can see that data issued from satellites and remote sensing, and the features with vegetation indices especially NDVI and EVI, the RGB colours, are the most used.

Crop yield prediction

The estimation of crop yield is crucial in DA, as it enables efficient planning of resources. Economically, an early and accurate prediction of yields can help decision-makers to react to the crops market. Moreover, crop yield prediction permits the study of factors that influence and affect the production, such as climate and weather, natural soil fertility and its physical structure and topography, crop stress, the incidence of pests and diseases, etc.

The prediction of crop yields has been the subject of many studies. Ref. [ 71 ] presented a literature review on crop forecasting, where the authors highlighted the most used machine learning algorithms along with the applied metrics and measures. In this section, we examine the learning algorithms that have been used in crop yield prediction from different views: data types, the pre-processing methods, and features or the predictor variables used in each study. Tables   2 and   3 summarise some relevant studies.

The crop yield forecasting approaches follow two major types of sources of data. The first type is related to the sources that have direct impact on the crops. These sources are soil data, weather data, environmental parameter data. These are usually used to predict crop yield [ 27 , 34 , 42 , 46 , 51 , 73 ]. The second type of sources are the use of advanced technologies and tools like satellite multi/hyper spectral images, remote sensing and sensors to collect the data [ 62 , 83 , 102 , 114 , 152 ]. Some advanced studies use both types of data sources [ 1 , 40 , 54 , 59 , 65 , 67 , 68 , 97 , 120 , 121 ].

The forecasting models based on the first type of data sources provides a pre-season estimation of the yield, even before the beginning of the crop season. This allows farmers to decide which strategy to both optimise the farming operations and crop production. These decisions include choosing seeds and crop type, type of fertiliser and its applications. Moreover, This data can also be used for some crop monitoring during the growing season.

The monitoring systems based on the second type of data analysis - data imagery obtained from satellite, cameras, scanner, sensors - allow for on-season estimation (emergence, detect stress conditions of crop, harvest dates, ...). These models are complex since they have to analyse the data that consists of both spatio-temporal and non-spacial. While the spatial data is of high resolution, some images can be of very poor quality, (e.g., images with lot of clouds). Features or predictor variables used in this kind of applications depend on the type of data sources, NDVI and EVI are the most used vegetation indices for satellite and remote/approximate sensors’ data source, min/max temperature and precipitation for weather data source, soil moisture and nitrogen fertiliser for soil based data source.

Crop protection

Crop disease is considered as a major menace for food security in many regions of the world since it causes serious crops losses. While the detection of crop diseases correctly and timely when they first appear is crucial in crop monitoring, this remains a difficult task. One of the solutions to deal with this issue is to use data analytics approach. This will reduce yield losses and prevent farmers to take effective reactive actions. Forewarning can be seen as the outputs of data mining process. Usually, this consists of examining the features of a newly presented case and assigning it to a predefined class.

Several interesting efforts have been developed to prevent crops losses due to diseases, Tables   4 and 5 summarise some major studies. Ref. [ 7 ] presented an overview of ML techniques for crop disease classification. In addition, it presented to a case study where a deep learning algorithm was successfully used. Ref. [ 45 ] provided a review on advanced ANN techniques to process hyper-spectral data for plant disease detection. Recently, deep learning approaches have been emerged and widely used for plant disease detection and classification, with a variety of network architectures (CNN, AlexNet, googLeNet, CaffeNet, DenseNet, Inception, LeNet, VGGNet,...) and training methods (shallow, deep, from scratch) [ 9 , 16 , 21 , 28 , 38 , 63 , 79 , 82 , 125 , 139 , 143 , 150 , 155 ]. Moreover, [ 127 ] presented an interesting study on the potential of the use of deep learning for plant stress phenotyping.

Crop protection, that consists of disease, stress, and weed detection, aims to offer tools that detect plants disease caused by various biotic (pathogen, insect, pest, and weed) or abiotic (temperature stress, nutrient deficiency, toxicity, herbicide) variables [ 126 ]. The earlier the stress, disease or their symptoms are detected, the greater the chance of reducing the disease spread within a field. This has gained significant advantage from the advances in image collection and processing and their analysis using ML algorithm. The state-of-the-art is very rich. The large majority of studies carried out so far were using image processing, consequently image-based data and classification techniques. These are capable of detecting disease at the scale of leaf, canopy or field [ 126 ].

Disease detection at a leaf level uses images collected using digital cameras, which are stored in data warehouses. For instance, PlantVillage database [ 6 , 9 , 21 , 28 , 63 , 79 , 88 , 106 , 125 , 129 , 150 ] is created for this purpose. The objective of this repository is to build classifiers with high accuracy. The basic classifiers can simply assign to an unseen image a label healthy or infected , while more elaborated classifiers can identify the disease - in other words, classify unseen images to disease classes. However, this approach has some limitations. First, it depends on the quality of the images, as when taken in natural environment, these images are subject to different degrees of light, shadow, dust and leaves overlapping and requires sophisticated image processing, which is not an easy task. Second, usually the datasets sizes are small, which affect the learning phase of the classifiers and more importantly the potential of some advanced learning algorithms such as deep learning. Data augmentation (rotation, light shade’s variation, colour inversion, translation and changes in intensity and so on) is one of the methods used to overcome this problem to artificially increase the number of images [ 6 , 9 , 21 , 63 , 79 , 129 , 150 ], but it does not always work. Transfer learning is another solution to scarce/small data-set, where the knowledge obtained from solving a task in a given domain is transferred to the target domain in which the dataset is small [ 6 , 11 , 28 ]. The transfer learning can only be efficient if the source and target domains share some similarities in terms of diseases and their symptoms, for example. Moreover, it is very challenging to transfer knowledge from representations learned using RGB images to a target task using multi-spectral images from UAV or satellite [ 126 ].

Third, this approach cannot detect more than a single disease at a time, and the detection of diseases if the symptoms are manifested in another area than leaves. Plant canopy based-image was proposed as a solution to this problem. The idea is to collect data relative to disease in situations where single-leaf phenotypes alone would not provide sufficient information. Such features include the size, the height, the structure, and branching of canopy [ 126 ]. The canopy-based detection uses UAV equipped with (multi/ hyper) spectral cameras and sensors to collect the data [ 32 , 49 , 80 , 82 , 136 , 143 , 153 , 155 ]. Then data needs to be processed to extract features which are usually related to vegetation indices like NDVI and EVI or colours like RGB and NIR. The benefit from UAV images comes with cost on complexity of analysis since images taken by UAV are susceptible to occlusion, overlapping, and atmospheric effects. Also, UAV is not able to fly at higher altitudes, which decreases the quality of the collected images. To cover larger zones and fields, satellite-based remote sensing and images has been proposed as a very good alternative [ 15 , 81 , 109 , 156 ]. However, the problem with satellite remote sensing is the revisit time, which is 16 days on average, which makes protection applications difficult, and some diseases can spread rapidly in fields before they are detected. Moreover, passive sensors cannot penetrate clouds [ 149 ]. The integration of these data with additional data sources like field surveys, contextual information of field and crop rotation can improve the accuracy [ 15 , 81 , 109 ].

Detecting diseases only from one data source based on digital images or sensor data is not sufficient. Besides, variations in symptoms may lead to false positives due to dynamic nature of plant changes [ 126 ]. Consequently, the appearance-based identification of diseases is not reliable enough to accurately detect unhealthy plants, especially in the early growth stages. The use of multi-data sources can improve the accuracy of the detection. For instance, the use of physiological features and morphological characteristics (growth attributes, yield-related features, soil) [ 66 ], or the employment of satellite-based images and canopy-based images [ 156 ], where the disease can be identified at the plant canopy level and at the field level.

Crop maturity monitoring

Crop maturity is a kind of crop yield prediction, but it is based on image data. This technique has been used in fruit detection, like apples, tomatoes, oranges, etc, and provides an early estimation of yield. It is also used for crop monitoring to provide information to farmers with the view to plan their farming operations, adjust management practices before harvesting, etc. Such intelligent systems for monitoring crop implement the data mining process incorporating machine vision and image processing methods among with advanced learning algorithms, such as CNN, SVM and ANN. Unlike crop yield prediction process described above, this process is based on a single-data source; digital images [ 5 , 23 , 52 , 75 , 108 , 122 ] or sensor based-images [ 117 , 123 , 153 ]. Table   6 summarises such techniques. The challenges of these systems are more or less the same as those of systems for crop disease detection and protection. For instance, images with different illumination and lighting angles, complex surroundings and backgrounds, noise, the presence of clouds, etc.

Clustering for crop monitoring

Clustering techniques are not widely employed in DA, few efforts have been deployed to investigate the potential of these techniques for zones’ delineation within a field. There are several reasons for splitting an agricultural field into zones. Some traditional reasons include crop diversification within a field, crop-rotation, facilitating the management tasks, and more recently we defined the zones based on yield maps. This usually helps to improve the overall crop yield of the field, by managing the zones more effectively. Therefore, delineation of Management zones (DMZ) is a very important task for farming operations since determining zones of low-or-high yields, and understanding the reasons behind low yields, can help come up with specific solution for each zone with the view to increase the yields. In addition, it has other economic benefits, because we can target each zone with the right amount of fertilisers, water, and other nutrients.

According to [ 69 ], delineation of management zones is an effective way to manage the variability of soil within a field, such that each zone will receive specific management. In [ 145 ], a management zone is defined as a subregion of a field that has a relatively homogeneous combination of yield-limiting factors, for which a single rate of a specific crop input is appropriate to reach maximum efficiency of farm inputs. In [ 53 ], it is defined as a subregion of a field that is relatively homogeneous with regard to soil attributes.

DMZ is a complex spatial problem, which is addressed in the literature from several perspectives. This has attracted interest from many researchers [ 61 , 85 , 87 , 110 , 140 ]. A literature review has been presented in [ 90 ], where the authors discussed the delineation of soil management zones from the variable-rate fertilisation point of view. many other studies presented the delineation based on various criteria. Some techniques that have been used include topographic maps, direct soil sampling, non-invasive soil sampling by electrical conductivity equipment, soil organic matter or organic estimated by remote sensing, and yield maps built using data collected over several seasons/years [ 99 ].

Figure 6 depicts the general process of delineation of management zones designed according to methodologies followed by the majority of the literature.

figure 6

The delineation management zones process

The majority of problems that are related to crop management imply the management of fields and zones. Therefore, the collected data is usually characterised by geographic coordinates and time associated with each sample, which leads to the use of data mining techniques that are more suitable for spatial and temporal datasets. It is well recognised that agricultural datasets are typically spatio-temporal, as the data is always associated with location and time. However, these datasets contain a significant amount of noise, outliers, and even missing values. For instance, GPS capture devices introduce some noise, imprecisions, and even outliers in the data. Satellite imagery also faces huge imprecision and noise (such as clouds, ...).

Because of the type of the datasets, which is spatio-temporal, it is not surprising to notice that the majority of the clustering algorithms used are of type partitional. K-means and Fuzzy C-Mean (FCM) are considered among the most popular clustering techniques and heavily used to cluster agricultural data [ 17 , 18 , 84 , 134 , 137 , 142 , 151 , 154 ]. The FCM approach has an advantage over K-means, as it deals better with imprecision and noisy data. Moreover, other types of clustering algorithms have also been proven to be efficient in DA, such as density-based and hierarchical-based clustering techniques applied to DMZ [ 48 , 116 ].

As mentioned above, besides its huge importance in crop management, delineation of management zone (DMZ) has received much attention, as the data is now available not only from traditional sources but also from refined sources, including advanced data pre-processing techniques. In addition, the recently collected data integrates knowledge of experts and farmers experiences on their fields, which improves significantly the quality of the data [ 84 , 141 ]. Advanced imaging enhancement techniques improve further the data quality, and they offer the ability to track the development of crops and provide a Geo-referenced data that can describe the spatial and the temporal variability of soil and crops variables at high resolution, covering large areas [ 17 , 84 , 101 , 132 , 133 , 141 , 151 ].

Systematic analysis

In the following we will explore the application of data analytics in DA and its extension to big data, and illustrate the practical challenges that hinder the full adoption of DA by farmers.

DA in (small /large) scale farming

Farming can be carried out on a small or large-scale fields depending on several factors like land size, capital, farmer skills, level of use of machinery and technology, etc. According to FAO Footnote 3 and Grain Footnote 4 , over 90% of all farms worldwide are of small-scale holding on average 2.2 hectares (from 0.6 to 10 hectares), except for Northern America where small farms have an average size of 67.7 hectares Footnote 5 . Small-scale farms represent 25% of the world’s farmland today, where 73.12% are located in developing countries.

In [ 10 ] the authors described three categories of smart farming technology, which are complementary:

Data acquisition technologies: they are used to acquire the data that is related to the farm. These include remote sensing, weather data, etc.;

Data analysis and evaluation technologies: these technologies usually take as input the data that has been collected so far and deliver insight to the farmer. These include computer-based visualisation and decision models, farm management and information systems;

Precision application technologies: these are focusing on variable-rate application and guidance technologies.

The application of smart technologies and data analytics for crop management are not restricted to one kind of farm. Nowadays, every farm should adopt smart technologies, as they are needed for variable rates applications (irrigation, pesticides, fertilisers) [ 72 , 102 , 154 ] while protecting the environment.

The size of the farm determines how these technologies will be used. Large farms tend to develop their smart technology to monitor their farming land, or to afford some of the existing sophisticated systems like CropX as they hold the scale and margins. While small farms tend to rent sophisticated machinery and smart applications on demand, especially with the proliferation of cloud technologies that makes these smart applications reasonable, the work of [ 30 ] is an example among others, of a smart irrigation system designed for smallholders. Besides, some technologies are more suitable for large-scale farms like drones and aerial vehicles used to monitor crops which are not as profitable or efficient for small scales because they have less difficulty visualising their crops. On the other side, large-scale farms are responsible for 70% of current deforestation Footnote 6 , the largest share of agriculture-related greenhouse-gazes emissions, agricultural water use and habitat disruption resulting in biodiversity loss. Generally, small-scale farms require considerably fewer external inputs and cause minor damage to the environment.

Table   7 summarises the main differences between small and large-scale farming from several perspectives. However, DA can be applied to any kind of farm without restriction. Yet, we have found that the number of papers that addressed large-scale farms is almost the same as works on large-scale farms.

Technologies for data acquisition Table   7 can be used to all types of farms, such as remote sensing, imagery data systems, and so on. The acquired data, over the years, can lead to the phenomenon of Big Data. If pre-processed and stored properly, this will give a significant competitive advantage to farms that collected them, whether they are small or large. Some of the applications and data analyses that can be performed of the collected are summarised in the Tables   1 ,   2 ,   3 ,  4 and   5 ,   6 .

DA and big data

Big data is not just characterised by the volume, but also by velocity, variety, and others [ 86 ]. These are enough to challenge the existing data mining techniques, as trying to develop techniques to deal with large volumes of data (volume), various types of data attributes (variety or heterogeneity), and be able to analyse the new data as soon as it is collected (velocity) are extremely challenging tasks. Moreover, many other characteristics can be found in some big data-driven applications, these include veracity, value, viscosity, veracity, visualisation, etc. In this study, we added veracity, as the data, collected by various instruments and sensors, is of different quality, which creates a huge challenge to the data pre-processing task, and therefore its analysis. In the following, we discuss the impact of Big Data challenges on DA.

Velocity: many studies that have been examined do not consider the data velocity during their data collection. In DA, the frequency of collecting data depends on its source and the problem for which the data was collected. Some applications need real-time data and others do not. For instance, crop yield prediction does not need real-time data or data streams. It is performed at ad-hoc, while crop protection and disease detection require high quality sensors and imagery data connected to efficient methods of data analysis, which need continuous control.

Variety: this is very common in agricultural datasets, as multiple sources were used to collect all the necessary information about the farm and farming operations. The data values can be a simple number such as temperatures to more elaborated such as imagery data, NDVI, soil texture, etc. This makes the definition of distance measures and other parameters of the learning algorithms very difficult.

Veracity: Agricultural data contains many missing values and collected from various sources of varying quality. The data is very noisy, and more importantly it contains many missing values. Therefore, it is very challenging to clean and prepare it for the analysis. This was the case in the work conducted by [ 37 ], and also in [ 93 , 94 , 95 , 96 , 107 ] where data was collected from very large farming areas.

Table   8 , summarises a set of representative papers reported in the paper according to their usage of big data. For each paper, we identify the type, the size, the heterogeneity of data used, and the frequency of its collection. Also, we consider the number and type of ML algorithms used, the complexity of the proposed analysis algorithms and devices used to collect data, data analysis applied to a given crop and problem to solve. One can notice while the data analysis algorithms and techniques were heavily used and varied, the rigorous process of knowledge discovery was not followed, usually the data is relatively small either in size (small observations) or the data has few dimensions (for instance, considering only weather data, or fertiliser, without taking into account other factors).

From Table   8 , we can extract three classes of applications according to their usage of big data: Full usage (the data contains all the characteristics of big data), light usage (the data contains some characteristics), non-usage (the data does not contain any characteristic of big data).

To examine the degree of use of the big data concept and to figure out which of its dimension is more present, we conducted a statistical study where we classify works according to their employment of the 4Vs of big data.

figure 7

Distribution of works according to the used Big Data dimensions’

Figures   7 and   8 show that no work has a full employment of big data (4Vs). One can notice that the agricultural data is multidimensional and heterogeneous (variety). Moreover, we have found that the prediction applications display more use of big data, there exist studies that have used three dimensions such as DMZ applications. It is worth noting that these applications, either prediction or delineation of zones, have the potential to use big data to provide stable and accurate results.

figure 8

Percent of employment of big data dimensions

If we put aside the volume dimension (V1) (see Figure   7 , only 7% of the reviewed studies used (V2, V3 and V4), and 32% of studies just employed data mining techniques for agriculture problems. The most employed data mining techniques are for prediction, including yield prediction, forecasting, prediction of fertiliser applications, etc.

DA practical challenges

There exist a number of challenges and obstacles impeding the potential benefit of DA. In [ 104 ], the authors studied the barriers that prevent the adoption of smart farming in their country, Brazil. Some of these barriers include lack of integration and compatibility between different agriculture systems, lack of advanced data manipulation of data obtained from different equipment, poor telecommunications infrastructure on rural areas, and finally, the lack of training in deploying and using new technologies. These barriers are common to the majority of countries in the world.

From the Table   7 , we can see that over 73% of crop farms are located in developing countries. So that, the investment in high and sophisticated DA technologies is not there. Most of the main technologies used in DA systems (GPS, UAV, auto-steering and variable rate technology) are designed for relatively large-scale farms located in developed countries [ 10 ] or designed by developed countries. Some of these technologies are becoming available recently. For instance, since 2018 African scientists can have access to free and open-source satellite data as a result of a deal signed by the African Union with the European Commission’s Copernicus programme.

As DA is relatively new technology, there is a lack of standards and common solutions for data collection, preparation and storage. In addition, there is a lack of data for many reasons, farmers did not record their data and it takes time to build significant historical datasets [ 20 , 39 , 77 , 78 , 92 , 146 ]. Another major barrier is that many farmers are relying more on their expertise and refusing to adopt these new and complex technologies [ 10 ]. Moreover, the transition from their traditional practices and farming habits to these technologies comes with a cost and energy (training and learning new skills).

[ 20 ] States that the legal and regulatory frameworks around the collection, sharing and use of agricultural data contributes to a range of challenges. Many laws potentially influence the ownership, control of and data access. Ref. [ 74 ] presented a set of socio-ethical imperatives associated with the use of data in agriculture, including dependency risks, data concentration, potential lock-in effects, and the peril of transformation of farmers into information tools, in addition to the sustainability challenges.

Finally, according to [ 47 ], the real economic value of the use of big data in farming is still unknown, especially for small-scale farming. Consequently, it will be hard to convince them to switch from process-driven towards data and machine learning driven. This is reaffirmed in [ 20 ], where the authors stated that on one side, farmers are enticed with promises of increased profits and farming efficiency, on the other hand the proofs are not there yet.

Digital agriculture (DA) is a data-driven approach that exploits the hidden information within the collected data to gain new insights; transforming the farming practices from intuitive-based decision-making to informed-based decision-making. DA relies on efficient data collection practices, efficient data preparation and storage techniques, efficient data analytics, and efficient deployment and exploitation of the gained insights to make optimal farming decisions.

In this study, we presented a systematic review of the potential use of the data mining process in crop production and management and highlighted serious gaps which can be considered in future studies. The majority of the current practices were dominated by statistical analyses and small machine learning systems. However, these can only give some ideas within a very limited view of the overall system. Agricultural data-driven applications collect a significant amount of data from various sources. This constitutes an excellent opportunity to the field to answer numerous research and practical questions that were not possible before. Nevertheless, despite all the advantages that can be gained from DA, there are several other challenges and obstacles that need to be addressed, among them lack of data, lack of skills, and lack of maturity and standards so that it can be adopted and deployed quickly and easily.

In this study, we cover approaches that deal the entire process of data mining; from data collection to knowledge deployment. We cover this process from big data view, with more focus on crop monitoring and management in an attempt to understand the challenges that DA is currently facing. We defined the research questions addressed by the study and provided a classification of data mining techniques used in the field. For each class, a set of representative existing works have been reviewed, and an analytical study has been provided to highlight the category of machine learning method applied and for which purpose. We discussed the big data concepts and its current impact on DA, and showed that from the data analyst’s view, the transition towards DA is ready to embrace big data analytics concepts. This provides new opportunities of investment into these challenges and allows for a efficient ways of managing crops. Besides, it will provide farmers with new insights into how they can grow crops more efficiently, while minimising the impact on the environment. It also promises new levels of scientific discovery and innovative solutions to more complex problems.

Availability of data and materials

Not applicable.

European Commission. Brussels. Preparing for Future AKIS in Europe, 2019.

http://www.prisma-statement.org/ .

http://faostat3.fao.org/faostat-gateway/go/to/home/ .

https://grain.org .

According to the criterion put forward by Lincoln University in Nebraska, which defines a small farm in the US as one with an annual turnover of less than US$50,000)

IPBES, 2019: Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.

Abbreviations

Artificial Neural Network

Bayesian classifier

Convolution Neural Network

Decision tree

Delineation of management zones

Deep Neural Network

Extreme learning machine

Enhanced Vegetation Index

Fuzzy C-means

Geographical information system

Global positioning system

Inertial navigation system

K-Nearest Neighbour

Long/Short Term Memory Network

Multi-layer perceptron

Moderate-resolution imaging spectro-radiometer

Normalised difference vegetation index

Optimised soil adjusted vegetation index

Random forest

Radial basis function

RedGreenBlue

Recurrent neural network

Ratio Vegetation Index

Support vector machine

Spectral vegetation index

Support vector regression

Unmanned aerial vehicle

Unmanned ground vehicles

Weighted dynamic ranged vegetation index

Abbas F, Afzaal H, Farooque A, Tang S. Crop yield prediction through proximal sensing and machine learning algorithms. Agronomy. 2020. https://doi.org/10.3390/agronomy10071046 .

Article   Google Scholar  

Ahmed F, Al-Mamun H, Bari H, Hossain E, Kwan P. Classification of crops and weeds from digital images: a support vector machine approach. Crop Prot. 2012;40:98–104. https://doi.org/10.1016/j.cropro.2012.04.024 .

Akbarzadeh S, Paap A, Ahderom S, Apopei B, Alameh K. Plant discrimination by support vector machine classifier based on spectral reflectance. Comput Electron Agric. 2018;148:250–8. https://doi.org/10.1016/j.compag.2018.03.026 .

Alibabaei K, Gaspar P, Lima T. Crop yield estimation using deep learning based on climate big data and irrigation scheduling. Energies. 2021;14:3004. https://doi.org/10.3390/en14113004 .

Amatya S, Karkee M, Gongal A, Zhang Q, Whiting M. Detection of cherry tree branches with full foliage in planar architecture for automated sweet-cherry harvesting. Biosyst Eng. 2015;146:3–15. https://doi.org/10.1016/j.biosystemseng.2015.10.003 .

Aravind K, Raja P. Automated disease classification in (selected) agricultural crops using transfer learning. Autom J Control Meas Electron Comput Commun. 2020;62:260–72. https://doi.org/10.1080/00051144.2020.1728911 .

Aravind K, Maheswari P, Raja P, Szczepanski C. Crop disease classification using deep learning approach: an overview and a case study. In: Das H, Pradhan C, Dey N, editors. Deep learning for data analytics foundations, biomedical applications, and challenges. Cambridge: Academic Press; 2020. p. 173–95. https://doi.org/10.1016/b978-0-12-819764-6.00010-7 .

Arribas J, Sanches-Ferrero G, Ruiz-Ruiz G, Gomez-Gil J. Leaf classification in sunflower crops by computer vision and neural networks. Comput Electron Agric. 2011;78:9–18. https://doi.org/10.1016/j.compag.2011.05.007 .

Arsenovic M, Karanovic M, Sladojevic S, Anderla A, Stefanovic D. Solving current limitations of deep learning based approaches for plant disease detection. Symmetry. 2019. https://doi.org/10.3390/sym11070939 .

Balafoutis AT, Beck B, Fountas S, Tsiropoulos Z, Vangeyte J, van der Wal T, Soto-Embodas I, Gomez-Barbero M, Pedersen S,. Smart farming technologies–description taxonomy and economic impact. In: Pedersen SM, Lind K, editors. Precision agriculture: technology and economic perspectives, progress in precision agriculture, chapter 2. Cham: Springer; 2017. p. 21–78. https://doi.org/10.1007/978-3-319-68715-5 .

Barbedo JA. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Comput Electron Agric. 2018;153:46–53. https://doi.org/10.1016/j.compag.2018.08.013 .

Behmann J, Mahlein AK, Rumpf T, Romer C, Plumer L. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. J Precis Agric. 2014;16:239–60. https://doi.org/10.1007/s11119-014-9372-7 .

Bendre M, Thool R, Thool V. Big data in precision agriculture through ICT: rainfall prediction using neural network approach. In: Satapathy S, Bhatt Y, Joshi A, Mishra D, editors. Proceedings of the International congress on information and communication technology. Singapore: Springer; 2016. p. 165–75.

Berckmans D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev Sci. 2014;33:189–96.

Google Scholar  

Bi L, Hu G, Raza M, Kandel Y, Leandro L, Mueller D. A gated recurrent units (gru)-based model for early detection of soybean sudden death syndrome through time-series satellite imagery. Remote Sens. 2020. https://doi.org/10.3390/rs12213621 .

Brahimi M, Arsenovic M, Laraba S, Sladojevic S, Boukhalfa K, Moussaoui A. Deep learning for plant diseases: detection and saliency map visualisation. In: Zhou J, Chen F, editors. Human and machine learning. Cham: Springer; 2018. p. 93–117. https://doi.org/10.1007/978-3-319-90403-0_6 .

Breunig F, Galvao L, Dalagnol R, Dauve C, Parraga A, Santi A, Flora DD, Chen S. Delineation of management zones in agricultural fields using cover-crop biomass estimates from planetscope data. Int J Appl Earth Obs Geoinf. 2020. https://doi.org/10.1016/j.jag.2019.102004 .

Brock A, Brouder S, Blumhoff G, Hofmann B. Defining yield-based management zones for corn-soybean rotations. Agron J. 2005;97:1115–28. https://doi.org/10.2134/agronj2004.0220 .

Cao J, Zhao Z, Luo Y, Zhang L, Zhang J. ZLi, Tao F, Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine. Eur J Agron. 2021;123: 126204. https://doi.org/10.1016/j.eja.2020.126204 .

Carolan M. Acting like an algorithm: digital farming platforms and the trajectories they (need not) lock-in. Agric Hum Values. 2020;37:1041–53. https://doi.org/10.1007/s10460-020-10032-w .

Chen J, Liu Q, Gao L. Visual tea leaf disease recognition using a convolutional neural network model. Symmetry. 2019. https://doi.org/10.3390/sym11030343 .

Chen N, Yu L, Zhang X, Shen Y, Zeng L, Hu Q, Niyogi D. Mapping paddy rice fields by combining multi-temporal vegetation index and synthetic aperture radar remote sensing data using google earth engine machine learning platform. Remote Sens. 2020;2020. https://doi.org/10.3390/rs12182992 .

Cheng H, Damerow L, Sun Y, Blanke M. Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks. J Imaging. 2017. https://doi.org/10.3390/jimaging3010006 .

Chergui N, Kechadi T, McDonnell M, The impact of data analytics in digital agriculture: a review. In: the 2020 IEEE International multi-conference on: organization of knowledge and advanced technologies (OCTA). Isko-Maghreb: ’International society for knowledge organization’. February 6-8, 2020 Tunis (Tunisia). 2020. https://doi.org/10.1109/OCTA49274.2020.9151851

Chinchuluun R, Lee W, Bhorania J, Pardalos P. Clustering and classification algorithms in food and agricultural applications: a survey. In: Papajorgji PJ, Pardalos PM, editors. Advances in modelling agricultural systems springer optimisation and its applications. Boston: Springer; 2008. p. 433–54.

Contiu S, Groza A. Improving remote sensing crop classification by argumentation-based conflict resolution in ensemble learning. Expert Syst Appl. 2016;64:269–86. https://doi.org/10.1016/j.eswa.2016.07.037 .

Crane-Droesch A. Machine learning methods for crop yield prediction and climate change impact assessment in agriculture. Environ Res Lett. 2018. https://doi.org/10.1088/1748-9326/aae159 .

Cruz A, Luvisi A, Bellis LD, Ampatzidis Y. X-fido: an effective application for detecting olive quick decline syndrome with deep learning and data fusion. Front Plant Sci. 2017. https://doi.org/10.3389/fpls.2017.01741 .

Dadashzadeh M, Abbaspour-Gilandeh Y, Mesri-Gundoshmian T, Sabzi S, Hernández-Hernández J, Hernández-Hernández M, Arribas J. Weed classification for site-specific weed management using an automated stereo computer-vision machine-learning system in rice fields. Plants. 2020;5:22–36. https://doi.org/10.3390/plants9050559 .

Dahane A, Benameur R, Kechar B. An IoT low-cost smart farming for enhancing irrigation efficiency of smallholders farmers. Wirel Pers Commun. 2022. https://doi.org/10.1007/s11277-022-09915-4 .

Debats S, Luo D, Estes L, Fuchs T, Caylor K. A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes. Remote Sens Environ. 2016;179:210–21. https://doi.org/10.1016/j.rse.2016.03.010 .

Du CJ, Kechadi M, Zhang YB, Huang BQ. A hybrid HMM-SVM method for online handwriting symbol recognition. Intell Syst Des Appl. 2006;3:887–91. https://doi.org/10.1109/ISDA.2006.61 .

Dyrmann M, Karstoft H, Midtiby H. Plant species classification using deep convolutional neural network. Biosyst Eng. 2016;151:72–80. https://doi.org/10.1016/j.biosystemseng.2016.08.024 .

Ehret D, Hill B, Helmer T, Edwards D. Neural network modeling of greenhouse tomato yield, growth and water use from automated crop monitoring data. Comput Electron Agric. 2011;79:82–9. https://doi.org/10.1016/j.compag.2011.07.013 .

Elavarasan D, Vincent D, Sharma V, Zomaya A, Srinivasan K. Forecasting yield by integrating agrarian factors and machine learning models: A survey. Comput Electron Agric. 2018;155:257–82. https://doi.org/10.1016/j.compag.2018.10.024 .

Fardusi MJ, Chianucci F, Barbati A. Concept to practice of geospatial-information tools to assist forest management and planning under precision forestry framework a review. Ann Silvic Res. 2017;41:3–14. https://doi.org/10.12899/asr-1354 .

Feldman B, Martin E, Skotnes T. Big data in healthcare hype and hope, october 2012.dr. bonnie 2012;360, 2012. Http://www.westinfo.eu/files/big-data-inhealthcare

Ferentinos PK. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. 2018;145:311–8. https://doi.org/10.1016/j.compag.2018.01.009 .

Fielke S, Taylor B, Jakku E. Digitalisation of agricultural knowledge and advice networks: a state-of-the art. Agric Syst. 2020. https://doi.org/10.1016/j.agsy.2019.102763 .

Filippi P, Jones E, Bishop T, Acharige N, Dewage S, Johnson L, Ugbaje S, Jephcott T, Paterson S, Whelan B. A big data approach to predicting crop yield. In: Proceedings of the 7th Asian-Australasian Conference on Precision Agriculture 16-18 October 2017. Hamilton; 2017. https://doi.org/10.5281/zenodo.893668

Formaggio A, Vieira M, Renno C. Object based image analysis (obia) and data mining (dm) in landsat time series for mapping soybean in intensive agricultural regions. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium. 22-27 July 2012. Munich; 2012. p. 2257–2260. https://doi.org/10.1109/IGARSS.2012.6351047

Fukuda S, Spreer W, Yasunaga E, Yuge K, Sardsud V, Muller J. Random forests modelling for the estimation of mango (Mangifera indica l. cv.chok anan) fruit yields under different irrigation regimes. J Agric Water Manag. 2013;116:142–50. https://doi.org/10.1016/j.agwat.2012.07.003 .

Galambosova J, Rataj V, Prokeinova R, Presinska J. Determining the management zones with hierarchic and non-hierarchic clustering methods. Res Agric Eng. 2014;60:44–51. https://doi.org/10.17221/34/2013-RAE .

Gao J, Nuyttens D, Lootens P, He Y, Pieters J. Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosyst Eng. 2018;170:30–50. https://doi.org/10.1016/j.biosystemseng.2018.03.006 .

Golhani K. KBalasundram S, Vadamalai G, Pradhan B, A review of neural networks in plant disease detection using hyperspectral data. Inf Proc Agric. 2018;5:354–71. https://doi.org/10.1016/j.inpa.2018.05.002 .

Gonzalez-Sanchez A, Frausto-Solis J, Ojeda-Bustamante W. Predictive ability of machine learning methods for massive crop yield prediction. Spanish J Agric Res. 2014;12:313–28. https://doi.org/10.5424/sjar/2014122-4439 .

Griffin T, Mark T, Ferrell S, Janzen T, Ibendahl G, Bennett J, Maurer J, Shanoyan A. Big data considerations for rural property professionals. Am Soc Farm Manage Rural Appraisers. 2016;79:167–80.

Guastaferro F, Castrignano A, Benedetto DD, Sollitto D, Troccoli A, Cafarelli B. A comparison of different algorithms for the delineation of management zones. Precis Agric. 2010;11:600–20. https://doi.org/10.1007/s11119-010-9183-4 .

Guo A, Huang W, Dong Y, Ye H, Ma H, Liu B, Wu W, Ren Y, Ruan C, Geng Y. Wheat yellow rust detection using UAV-based hyperspectral technology. Remote Sensing. 2021. https://doi.org/10.3390/rs13010123 .

Guo Y, Fu Y, Hao F, Zhang X, Wu W, Jin X, Bryant C, Senthilnath J. Integrated phenology and climate in rice yields prediction using machine learning methods. Ecol Indic. 2021;120: 106935. https://doi.org/10.1016/j.ecolind.2020.106935 .

Gyamerah S, Ngare P, Ikpe D. Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov Kernel function. Agric For Meteorol. 2020. https://doi.org/10.1016/j.agrformet.2019.107808 .

Habaragamuwa H, Ogawa Y, Suzuki T, Masanori T, Kondo O. Detecting greenhouse strawberries (mature and immature), using deep convolutional neural network. Eng Agric Environ Food. 2018;11:127–38. https://doi.org/10.1016/j.eaef.2018.03.001 .

Haghverdi A, Leib B, Washington-Allen R, Ayers P, Buschermohle M. Perspectives on delineating management zones for variable rate irrigation. Comput Electron Agric. 2015;117:154–67. https://doi.org/10.1016/j.compag.2015.06.019 .

Han J, Zhang Z, Cao J, Luo Y, Zhang L, Li Z, Zhang J. Prediction of winter wheat yield based on multi-source data and machine learning in china. Remote Sensing. 2020. https://doi.org/10.3390/rs12020236 .

Huang K. Application of artificial neural network for detecting phalaenopsis seedling diseases using color and texture features. Comput Electron Agric. 2007;57:3–11. https://doi.org/10.1016/j.compag.2007.01.015 .

Huang Y, Chen Z, Yu T, Huang X, Gu X. Agricultural remote sensing big data: Management and applications. J Integr Agric. 2018;17:1915–31. https://doi.org/10.1016/S2095-3119(17)61859-8 .

Ingeli M, Galambosova J, Prokeinova R, Rataj V. Application of clustering method to determine production zones of field. Acta Technol Agric. 2015;18:42–5. https://doi.org/10.1515/ata-2015-0009 .

Jain M, Mondal P, DeFries R, Small C, Galford G. Mapping cropping intensity of smallholder farms: a comparison of methods using multiple sensors. Remote Sensing Environ. 2013;134:210–23. https://doi.org/10.1016/j.rse.2013.02.029 .

Jeong J, Resop J, Mueller N, Fleisher D, Yun K, Butler E, Timlin D, Shim K, Gerber J, Reddy V, Kim S. Random forests for global and regional crop yield predictions. PLoS ONE. 2016. https://doi.org/10.1371/journal.pone.0156571 .

Ji Z, Pan Y, Zhu X, Wang J, Li Q. Prediction of crop yield using phenological information extracted from remote sensing vegetation index. Sensors. 2021;4:1406. https://doi.org/10.3390/s21041406 .

Jiang Q, Wang QFZ. Study on delineation of irrigation management zones based on management zone analyst software. In: Jiang Q, editor. Computer and computing technologies in agriculture IV. CCTA 2010 IFIP advances in information and communication technology, vol. 346. Berlin: Springer; 2011. p. 4559–66. https://doi.org/10.1007/978-3-642-18354-6_50

Johnson D. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the united states. Remote Sensing Environ. 2014;141:116–28. https://doi.org/10.1016/j.rse.2013.10.027 .

Kamal K, Yin Z, Wu M, Wu Z. Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric. 2019. https://doi.org/10.1016/j.compag.2019.104948 .

Kamilaris A, Kartakoullis A, Prenafeta-Boldú F. A review on the practice of big data analysis in agriculture. Comput Electron Agric. 2017;143:23–37. https://doi.org/10.1016/j.compag.2017.09.037 .

Kamir E, Waldner F, Hochman Z. Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods. ISPRS J Photogramm Remote Sens. 2020;160:124–35. https://doi.org/10.1016/j.isprsjprs.2019.11.008 .

Khalili E, Kouchaki S, Ramazi S, Ghanati F. Machine learning techniques for soybean charcoal rot disease prediction. Front Plant Sci. 2021. https://doi.org/10.3389/fpls.2020.590529 .

Kim N, Lee Y. Machine learning approaches to corn yield estimation using satellite images and climate data: a case of Lowa state. J Korean Soc Surv Geod Photogramm Cartogr. 2016;34:383–90. https://doi.org/10.7848/ksgpc.2016.34.4.383 .

Kim N, Ha K, Park N, Cho J, Hong S, Lee Y. A comparison between major artificial intelligence models for crop yield prediction: case study of the midwestern united states, 2006–2015. ISPRS Int J Geoinform. 2019. https://doi.org/10.3390/ijgi8050240 .

Kitchen N, Sudduth K, Myers D, Drummond S, Hong S. Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity. Comput Electron Agric. 2005;46:285–308. https://doi.org/10.1016/j.compag.2004.11.012 .

Klerk L, Jakku E, Labarthe P. A review of social science on digital agriculture, smart farming and agriculture 4.0: new contributions and a future research agenda. NJAS Wageningen J Life Sci. 2019. https://doi.org/10.1016/j.njas.2019.100315 .

Klompenburg T, Kassahun A, Catal C. Crop yield prediction using machine learning: a systematic literature review. Comput Electron Agric. 2020. https://doi.org/10.1016/j.compag.2020.105709 .

Koch B, Khosla R, Frasier W, Westfall D, Inman D. Economic feasibility of variable-rate nitrogen application utilizing site-specific management zones. Agron J. 2004;96:1572–80. https://doi.org/10.2134/agronj2004.1572 .

Kouadio L, Deo R, Byrareddy V, Adamowski J, Mushtaq S, Nguyen VP. Artificial intelligence approach for the prediction of robusta coffee yield using soil fertility properties. Comput Electron Agric. 2018;155:324–38. https://doi.org/10.1016/j.compag.2018.10.014 .

Kritikos M. Precision agriculture in europe: legal, social and ethical considerations. science and technology options assessment. Scientific foresight unit (STOA) of the European parliament, brussels pe 603.207. 2017.

Kurtulmus F, Lee W, Vardar A. Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network. Precis Agric. 2014;15:57–79. https://doi.org/10.1007/s11119-013-9323-8 .

Kussul N, Lavreniuk M, Skakun S, Shelestov A. Deep learning classification of land cover and crop types using remote sensing data. Geosci Remote Sens Lett. 2017;14:778–82. https://doi.org/10.1109/LGRS.2017.2681128 .

Lioutas E, Charatsari C. Big data in agriculture: does the new oil lead to sustainability? Geoforum. 2020;109:1–3. https://doi.org/10.1016/j.geoforum.2019.12.019 .

Lioutas ED, Charatsari C, Rocca GL, Rosa MD. Key questions on the use of big data in farming: an activity theory approach. NJAS Wageningen J Life Sci. 2019. https://doi.org/10.1016/j.njas.2019.04.003 .

Liu B, Zhang Y, He D, Li Y. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry. 2017. https://doi.org/10.3390/sym10010011 .

Liu L, Dong Y, Huang W, Du X, Ma H. Monitoring wheat fusarium head blight using unmanned aerial vehicle hyperspectral imagery. Remote Sens. 2020. https://doi.org/10.3390/rs12223811 .

Ma H, Jing Y, Huang W, Shi Y, Dong Y, Zhang J, Liu L. Integrating early growth information to monitor winter wheat powdery mildew using multi-temporal Landsat-8 imagery. Sensors. 2018. https://doi.org/10.3390/s18103290 .

Mahlein A, Alisaac E, Masri AA, Behmann J, Dehne H, Oerke E. Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring fusarium head blight of wheat on spikelet scale. Sensors. 2019. https://doi.org/10.3390/s19102281 .

Maimaitijiang M, Sagan V, Sidike P, Hartling S, Esposito F, Fritschi F. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sens Environ. 2020. https://doi.org/10.1016/j.rse.2019.111599 .

Martinez-Casasnovas J, Escola A, Arno J. Use of farmer knowledge in the delineation of potential management zones in precision agriculture: a case study in maize (Zea mays L.). Agriculture. 2018. https://doi.org/10.3390/agriculture8060084 .

Mathur SBR, Shukla A, Suresh K, Prakash C. Spatial variability of soil properties and delineation of soil management zones of oil palm plantations grown in a hot and humid tropical region of southern India. Catena. 2018;165:251–9. https://doi.org/10.1016/j.catena.2018.02.008 .

Mauro AD, Greco M, Grimaldi M. A formal definition of big data based on its essential features. Libr Rev. 2016;65:122–35. https://doi.org/10.1108/LR-06-2015-0061 .

Metwally M, Shaddad S, Liu M, Yao R, Abdo A, Li P, Jiao J, Chen X. Soil properties spatial variability and delineation of site-specific management zones based on soil fertility using fuzzy clustering in a hilly field in Jianyang, Sichuan, China. Sustainability. 2019;2019. https://doi.org/10.3390/su11247084 .

Mohanty S, Hughes D, Salathe M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016;7:1–10. https://doi.org/10.3389/fpls.2016.01419 .

Mucherino A, Papajorgji P, Pardalos PM. A survey of data mining techniques applied to agriculture. J Operational Res. 2009;9:121–40. https://doi.org/10.1007/s12351-009-0054-6 .

Article   MATH   Google Scholar  

Nawar S, Corstanje R, Halcro G, Mulla D, Mouazen A. Delineation of soil management zones for variable-rate fertilization: a review. Adv Agron. 2017;143:175–245. https://doi.org/10.1016/bs.agron.2017.01.003 .

Nevavuori P, Narra N, Linna P, Lipping T. Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models. Remote Sens. 2020;12:4000. https://doi.org/10.3390/rs12234000 .

Newton J, Nettle R, Pryce J. Farming smarter with big data: Insights from the case of Australia’s national dairy herd milk recording scheme. Agric Syst. 2020. https://doi.org/10.1016/j.agsy.2020.102811 .

Ngo M, Kechadi T. Electronic farming records-a framework for normalising agronomic knowledge discovery. Comput Electron Agric. 2021. https://doi.org/10.1016/j.compag.2021.106074 .

Ngo QH, Le-Khac NA, Kechadi T. Predicting soil pH by using nearest fields. In: Bramer M, Petridis M, editors. Artificial Intelligence XXXVI. SGAI 2019. Lecture notes in computer science, vol. 11927. Cham: Springer; 2019. https://doi.org/10.1007/978-3-030-34885-4_40 .

Ngo VM, Kechadi MT Crop knowledge discovery based on agricultural big data integration. In: Proceedings of the 4th International conference on machine learning and soft computing, association for computing machinery. New York; ICMLSC. 2020. https://doi.org/10.1145/3380688.3380705

Ngo VM, Le-Khac N, Kechadi T. Data warehouse and decision support on integrated crop big data. Int J Bus Process Integr Manag. 2020. https://doi.org/10.1504/IJBPIM.2020.113115 .

Oliveira I, Cunha R, Silva B, Netto M. A scalable machine learning system for pre-season agriculture yield forecast. In: the 14th IEEE eScience Conference. 2018. https://doi.org/10.1109/eScience.2018.00131

Oliver D, Bartie P, Heathwaite A, Pschetz L, Quilliam R. Design of a decision support tool for visualising E. coli risk on agricultural land using a stakeholder-driven approach. Land Use Policy. 2017;66:227–34. https://doi.org/10.1016/j.landusepol.2017.05.005 .

Ortega R, Santibanez O. Determination of management zones in corn (Zea mays L.) based on soil fertility. Comput Electron Agric. 2007;58:49–59. https://doi.org/10.1016/j.compag.2006.12.011 .

Ouzemou J, Harti AE, Lhissou R. AEl-Moujahid, Bouch N, El-Ouazzani R, Bachaoui E, El-Ghmari A, Crop type mapping from pansharpened Landsat 8 NDVI data: a case of a highly fragmented and intensive agricultural system. Remote Sens Appl Soc Environ. 2018. https://doi.org/10.1016/j.rsase.2018.05.002 .

Pantazi X, Moshou D, Mouazen A, Alexandridis T, Kuang B. Data fusion of proximal soil sensing and remote crop sensing for the delineation of management zones in arable crop precision farming. In: CEUR Workshop Proceedings. CEUR-WS. 2015. p. 765–776.

Pantazi X, Moshou D, Alexandridis T, Whetton R, Mouazen A. Wheat yield prediction using machine learning and advanced sensing techniques. J Comput Electron Agric. 2016;121:57–65. https://doi.org/10.1016/j.compag.2015.11.018 .

Patricio D, Rieder R. Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput Electron Agric. 2018;153:69–81. https://doi.org/10.1016/j.compag.2018.08.001 .

Pivoto D, Waquil P, Talamini E, Finocchio C, Corte V, Mores G. Scientific development of smart farming technologies and their application in Brazil. Inform Process Agric. 2018;5:21–32. https://doi.org/10.1016/j.inpa.2017.12.002 .

Poppe K, Wolfert S, Verdouw C, Verwaart T. Information and communication technology as a driver for change in agri-food chains. Eurochoices. 2013;12:60–5.

Qin F, Liu D, Sun B, Ruan L, Ma Z, Wang H. Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE. 2016. https://doi.org/10.1371/journal.pone.0168274 .

Rafii F, TKechadi. Collection of historical weather data: Issues with missing values. In: Proceedings of the 4th International conference on smart city applications, association for computing machinery. New York; 2019. https://doi.org/10.1145/3368756.3368974

Ramos P, Prieto F, Montoya E, Oliveros C. Automatic fruit count on coffee branches using computer vision. Comput Electron Agric. 2017;137:9–22. https://doi.org/10.1016/j.compag.2017.03.010 .

Raza M, Harding C, Liebman M, Leandro L. Exploring the potential of high-resolution satellite imagery for the detection of soybean sudden death syndrome. Remote Sens. 2020. https://doi.org/10.3390/rs12071213 .

Reyes J, Wendroth O, Matocha C, Zhu J. Delineating site-specific management zones and evaluating soil water temporal dynamics in a farmer’s field in Kentucky. Vadose Zone J. 2019;18:1–19. https://doi.org/10.2136/vzj2018.07.0143 .

Rezapour S, Jooyandeh E, Ramezanzade M, Mostafaeipour S, Jahangiri M, Issakhov A, Chowdhury S, Techato K. Forecasting rainfed agricultural production in arid and semi-arid lands using learning machine methods: a case study. Sustainability. 2021;13:4607. https://doi.org/10.3390/su13094607 .

Reznik T, Lukas V, Krivanek Z, Kepka M, Herman L, Reznikova H. Disaster risk reduction in agriculture through geospatial (big) data processing. ISPRS Int J Geoinform. 2017. https://doi.org/10.3390/ijgi6080238 .

Rijswijk K, Klerk L, Turner J. Digitalisation in the New Zealand agricultural knowledge and innovation system: Initial understandings and emerging organisational responses to digital agriculture. NJAS Wageningen J Life Sci. 2019. https://doi.org/10.1016/j.njas.2019.100313 .

Ji R, Min J, Wang Y, Cheng H, Zhang H, Shi W. In-season yield prediction of cabbage with a hand-held active canopy sensor. Sensors. 2017. https://doi.org/10.3390/s17102287 .

Rosa LCL, Feitosa R, Happ P, Sanches ID, da Costa GOP. Combining deep learning and prior knowledge for crop mapping in tropical regions from multi-temporal SAR image sequences. Remote Sens. 2019. https://doi.org/10.3390/rs11172029 .

RuB G, Krus R. Exploratory hierarchical clustering for management zone delineation in precision agriculture. In: Industrial conference on data mining ICDM 2011: advances in data mining. Applications and theoretical aspects. Lecture notes in computer science book series (LNCS, volume 6870). 2011. p. 161–173. https://doi.org/10.1007/978-3-642-23184-1_13

Sa I, Ge Z, Upcroft FDB, Perez T, Mccool C. Deepfruits: a fruit detection system using deep neural networks. Sensors. 2016. https://doi.org/10.3390/s16081222 .

Sa I, Popovic M, Khanna R, Chen Z, Lottes P, Liebisch F, Nieto J, Stachniss C, Walter A, Siegwart R. Weedmap: a large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sens. 2018. https://doi.org/10.3390/rs10091423 .

Sabzi S, Abbaspour-Gilandeh Y. Using video processing to classify potato plant and three types of weed using hybrid of artificial neural network and particle swarm algorithm. Measurement. 2018;126:22–36. https://doi.org/10.1016/j.measurement.2018.05.037 .

Sakamoto T. Incorporating environmental variables into a modis-based crop yield estimation method for United states corn and soybeans through the use of a random forest regression algorithm. ISPRS J Photogramm Remote Sens. 2020;160:208–28. https://doi.org/10.1016/j.isprsjprs.2019.12.012 .

Schwalbert R, Amado T, Corassa G, Pott L, Prasad P, Ciampitti I. Satellite-based soybean yield forecast: integrating machine learning and weather data for improving crop yield prediction in southern brazil. Agric For Meteorol. 2020. https://doi.org/10.1016/j.agrformet.2019.107886 .

Sengupta S, Lee W. Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions. Biosyst Eng. 2014;117:51–61. https://doi.org/10.1016/j.biosystemseng.2013.07.007 .

Senthilnath J, Dokania A, Kandukuri M, Ramesh K, Anand G, Omkar S. Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV. Biosyst Eng. 2016;146:16–32. https://doi.org/10.1016/j.biosystemseng.2015.12.003 .

Shafi U, Mumtaz R, Garcia-Nieto J, Hassan S, Zaidi S, Iqbal N. Precision agriculture techniques and practices: from considerations to applications. Sensors. 2019. https://doi.org/10.3390/s19173796 .

Sibiya M, Sumbwanyambe M. A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering. 2019;1:119–31. https://doi.org/10.3390/agriengineering1010009 .

Singh A, Jones S, Ganapathysubramanian B, Sarkar S, Mueller D, Sandhu K, Nagasubramanian K. Challenges and opportunities in machine-augmented plant stress phenotyping. Trends Plant Sci. 2021;25:53–69. https://doi.org/10.1016/j.tplants.2020.07.010 .

Singh S, Ganapathysubramanian B, Sarkar S, Singh A. Deep learning for plant stress phenotyping: trends and future perspectives. Trends Plant Sci. 2018;23:883–98. https://doi.org/10.1016/j.tplants.2018.07.004 .

Sivakumar ANV, Li J, Scott S, Psota E, Jhala A, Luck J, Shi Y. Comparison of object detection and patch-based classification deep learning models on mid- to late-season weed detection in UAV imagery. Remote Sens. 2020. https://doi.org/10.3390/rs12132136 .

Sladojevic S, Arsenovic M, Culibrk AAD, Stefanovic D. Deep neural networks based recognition of plant diseases by leaf image classification. Computl Intell Neurosci. 2016. https://doi.org/10.1155/2016/3289801 .

Soma K, Bogaardt M, Poppe K, Wolfert S, Beers G, Urdu D, Kirova MP, Thurston C, Belles CM. Research for agri committee. impacts of the digital economy on the food chain and the cap. Policy department for structural and cohesion policies. European parliament. Brussels; 2019.

Song Q, Hu Q, Zhou Q, Hovis C, Xiang M, Tang H, Wu W. In-season crop mapping with GF-1/WFV data by combining object-based image analysis and random forest. Remote Sens. 2017. https://doi.org/10.3390/rs9111184 .

Song X, Wang J, Huang W, Liu L, Yan G, Pu R. The delineation of agricultural management zones with high resolution remotely sensed data. Precis Agric. 2009;10:471–87. https://doi.org/10.1007/s11119-009-9108-2 .

Speranza E, Ciferri R, Grego C, Vicente L. A cluster-based approach to support the delination of management zones in precision agriculture. In: IEEE 10 th International Conference on eScience. 2014. https://doi.org/10.1109/eScience.2014.42 ,

Speranza E, Ciferri R, Ciferri C. Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture. In: Proceedings of the XVII GEOINFO, November 2016. Campos do Jordao; 2016. p. 27-30.

Stombaugh T, Shearer S. Equipment technologies for precision agriculture. J Soil Water Conserv. 2000;55:6–11.

Su J, Liu C, Coombes M, Hu X, Wang C, Xu X, Li Q, Chen LGW. Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Comput Electron Agric. 2018;155:157–66. https://doi.org/10.1016/j.compag.2018.10.017 .

Tagarakis A, Liakos V, Fountas S, Koundouras S, Gemtos T. Management zones delineation using fuzzy clustering techniques in grapevines. Prec Agric. 2013;14:18–39.

Taylor S, Veal M, Grift T, Mcdonald T, Corley F. Precision forestry-operational tactics for today and tomorrow. In: In: 25th annual Meeting of the council of Forest Engineers. Auburn: Auburn University; 2002.

Too E, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric. 2019;161:272–9. https://doi.org/10.1016/j.compag.2018.03.032 .

Tripathi R, Shahid ANM, Lal B, Gautam P, Raja R, Mohanty S, Kumar A, Panda B, Sahoo R. Delineation of soil management zones for a rice cultivated area in Eastern India using fuzzy clustering. Catena. 2015;133:128–36. https://doi.org/10.1016/j.rse.2016.03.010 .

Vallentin C, Dobers E, Itzerott S, Kleinschmit B, Spengler D. Delineation of management zones with spatial data fusion and belief theory. Prec Agric. 2010;21:802–30. https://doi.org/10.1007/s11119-019-09696-0 .

Vendrusculo L, Kaleita A. Modeling zone management in precision agriculture through fuzzy c-means technique at spatial database. In: Proceedings of the 2011 ASABE Annual International Meeting Sponsored by ASABE. Gault House, Louisville, Kentucky. August 7-10. 2016. p. 350–359. https://doi.org/10.13031/2013.38168

Veys C, Chatziavgerinos F, AlSuwaidi A, Hibbert J, Hansen M, Bernotas G, Smith M, Yin H, Rolfe S, Grieve B. Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape. Plant Methods. 2019. https://doi.org/10.1186/s13007-019-0389-9 .

Villa P, Bresciani M, Pinardi RBM, Giardino C. A rule-based approach for mapping macrophyte communities using multi-temporal aquatic vegetation indices. Remote Sens Environ. 2015;171:218–33. https://doi.org/10.1016/j.rse.2015.10.020 .

Vrindts E, Mouazen A, Reyniers M, Maertens K, Maleki M, Ramon H, Baerdemaeker JD. Management zones based on correlation between soil compaction, yield and crop data. Biosyst Eng. 2005;92:419–28. https://doi.org/10.1016/j.biosystemseng.2005.08.010 .

Wiseman L, Sanderson J, Zhang A, Jakku E. Farmers and their data: an examination of farmers’ reluctance to share their data through the lens of the laws impacting smart farming. NJAS Wageningen J Life Sci. 2019. https://doi.org/10.1016/j.njas.2019.04.007 .

Wolfert S, Sorensen C, Goense D. Precision forestry-operational tactics for today and tomorrow. In: Global Conference (SRII). San Jose: Annual SRII. IEEE; 2014. p. 266–73.

Wolfert S, Verdouw C, Bogaardt M. Big data in smart farming: a review. Agric Syst. 2017;153:69–80. https://doi.org/10.1016/j.agsy.2017.01.023 .

Xue J, Su B. Significant remote sensing vegetation indices: a review of developments and applications. J Sensors. 2017. https://doi.org/10.1155/2017/1353691 .

Yamamoto K, Togami T, Yamaguch N. Super-resolution of plant disease images for the acceleration of image-based phenotyping and vigor diagnosis in agriculture. Sensors. 2017. https://doi.org/10.3390/s17112557 .

Yan L, Zhou S, Cifang W, Hongyi L, Feng L. Classification of management zones for precision farming in saline soil based on multi-data sources to characterize spatial variability of soil properties. Trans Chin Soc Agric Eng. 2007;23:84–9.

You J, Li X, Low M, Lobell D, Ermon S. Deep gaussian process for crop yield prediction based on remote sensing data. In: the Thirty-First AAAI Conference on Artificial Intelligence. AAAI Publications. 2017. p. 4559–4566.

Zan X, Zhang X, Xing Z, Liu W, Zhang X, Su W, Liu Z, Zhao Y, Li S. Automatic detection of maize tassels from UAV images by combining random forest classifier and VGG16. Remote Sens. 2020. https://doi.org/10.3390/rs12183049 .

Zhang X, Shi L, Jia X, Seielstad G, Helgason C. Zone mapping application for precision farming: a decision support tool for variable rate application. Prec Agric. 2010;11:103–14. https://doi.org/10.1007/s11119-009-9130-4 .

Zhang X, Han L, Dong Y, Shi Y, Huang W, Han L, Gonzalez-Moreno P, Ma H, Ye H, Sobeih T. A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sens. 2019. https://doi.org/10.3390/rs11131554 .

Zheng Q, Huang W, Cui X, Shi Y, Liu L. New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery. Sensors. 2018. https://doi.org/10.3390/s18030868 .

Zhou Y, Luo J, Feng L, Zhou X. DCN-based spatial features for improving parcel-based crop classification using high-resolution optical images and multi-temporal SAR data. Remote Sens. 2019. https://doi.org/10.3390/rs11131619 .

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Chergui, N., Kechadi, M. Data analytics for crop management: a big data view. J Big Data 9 , 123 (2022). https://doi.org/10.1186/s40537-022-00668-2

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Agriculture has conventionally been treated as an intuitive space with wisdom passed down from one generation to another. But today’s problems — like the changing climate and depletion of viable farmland — are more complex and urgent in nature. 

The United Nations estimates that the global population will reach 9.8 billion by 2050 , a 2.2 billion increase from now. This means that we need to step up our crop production significantly to cater to the growing number of people. Unfortunately, rapid urbanization and climate changes have claimed a major share of farmlands. In the United States alone, there has been a dip in the total area of farmlands from 913 million acres in 2014 to 899 million acres in 2018.

Today there is an urgent need to produce more food for the growing population - with less land to grow it on. In this article, let’s take a closer look into how big data and agtech (or agricultural technology) can help tackle this challenge.

How big data can help agriculture

To counter the pressures of increasing food demand and climate changes, policymakers and industry leaders are seeking assistance from technology forces such as IoT, big data, analytics , and cloud computing. 

IoT devices help in the first phase of this process — data collection. Sensors plugged in tractors and trucks as well as in fields, soil, and plants aid in the collection of real-time data directly from the ground. 

Second, analysts integrate the large amounts of data collected with other information available in the cloud, such as weather data and pricing models to determine patterns. 

Finally, these patterns and insights assist in controlling the problem. They help to pinpoint existing issues, like operational inefficiencies and problems with soil quality, and formulate predictive algorithms that can alert even before a problem occurs.

The adoption of analytics in agriculture has been increasing consistently; its market size is expected to grow from USD 585 million in 2018 to USD 1236 million by 2023, at a Compound Annual Growth Rate (CAGR) of 16.2%.

Top 4 use cases for big data on the farm 

The scope for big data applications is large, and we’ve only just begun to explore the tip of the iceberg. The ability to track physical items, collect real-time data, and forecast scenarios can be a real game changer in farming practices. Let’s take a look at a few use cases where big data can make a difference.

1.      Feeding a growing population

This is one of the key challenges that even governments are putting their heads together to solve. One way to achieve this is to increase the yield from existing farmlands.

Big data provides farmers granular data on rainfall patterns, water cycles, fertilizer requirements, and more. This enables them to make smart decisions, such as what crops to plant for better profitability and when to harvest. The right decisions ultimately improve farm yields.

2.      Using pesticides ethically

Administration of pesticides has been a contentious issue due to its side effects on the ecosystem. Big data allows farmers to manage this better by recommending what pesticides to apply, when, and by how much. 

By monitoring it closely, farmers can adhere to government regulations and avoid overuse of chemicals in food production. Moreover, this leads to increased profitability because crops don’t get destroyed by weeds and insects.

3.      Optimizing farm equipment

Companies like John Deere have integrated sensors in their farming equipment and deployed big data applications that will help better manage their fleet. For large farms, this level of monitoring can be a lifesaver as it lets users know of tractor availability, service due dates, and fuel refill alerts. In essence, this optimizes usage and ensure the long-term health of farm equipment.

4.      Managing supply chain issues

McKinsey reports that a third of food produced for human consumption is lost or wasted every year. A devastating fact since the industry struggles to bridge the gap between supply and demand. To address this, food delivery cycles from producer to the market need to be reduced. Big data can help achieve supply chain efficiencies by tracking and optimizing delivery truck routes.

Big data in agriculture case studies

Let’s do a deep dive into two case studies of how companies have leveraged big data effectively to solve issues plaguing the farming industry. This will help appreciate how big data solutions can make a real, hard-hitting impact on the ground. 

DTN uses big data to improve yields and profitability 

Digital Transmission network (DTN), a division of Schneider Electric, provides agricultural information solutions and market intelligence to its customers. Using DTN, farmers and commodity traders can access up-to-date weather and pricing data to better manage their business.

Faced with the challenge of managing a complex network of data sources — an enterprise resource planning (ERP) system, financial applications, GIS, agronomy packages, and sensing applications — to render information in real-time for customers, DTN current method of connecting these systems was proving too expensive to maintain. 

DTN invested in a modern data integration tool that consolidated data from multiple sources without having to write a ton of custom code. With a clean and consistent set of interfaces, DTN can now combine critical weather and agronomic data from fields to give accurate forecasts. Using DTN, farmers are able to improve yields and cut costs on the basis of these forecasts. 

DTN has rapidly become an industry standard for agribusiness information sharing and has evolved into an information hub for a networked farming and agribusiness community. 

SMAG InVivo uses big data to empower precision farming

InVivo is France’s leading agricultural cooperative group with 220 members and €6.4 billion in sales. SMAG , its subsidiary, is the French leader in agronomic information systems. Its software is used by 80% of cooperatives and 50% of merchants in France. 

While SMAG had developed many mobile applications to support farmers in their daily operations, SMAG wanted to pool all its data — 30 years of weather data history, satellite and drone images, and soil types — to make informed decisions faster. Their objective: use digitization to solve the food challenges of the 21st century. 

Using a tool to help process the vast amount of stored and accumulated data, SMAG developed a complex agronomic Data Crop algorithm, allowing for the use of different types of data to optimize decision-making. For example, Data Crop enables users to track the progress of crops over the year and predict yields — a data point that has led to incredible wheat production results. Currently, 80% of French agricultural land under wheat cultivation is managed through Data Crop. SMAG plans to expand this to other crops and countries as well. 

The cloud and the future of big data in agriculture 

Success in farming has been largely dependent on favorable natural forces, but not anymore. The coming together of cloud computing and big data has ensured that farmers have sufficient data points to make good decisions. 

Cloud computing has democratized the availability of huge computing power as data centers and storage are now available on a ‘pay-as-you-go’ model. This has made it possible to bring together knowledge repositories that contain data such as weather, irrigation practices, plant nutrient requirements, and several other farming techniques. 

Cloud-based apps can guide farmers on how to adjust their production based on market demand and how to improve their yield and profitability. Today, a farmer can micromanage farming and all its accompanying activities — even before planting crops, it’s feasible to estimate the results by tweaking the variables involved.

Getting started with big data in agriculture 

Big data can truly revolutionize the agricultural sector only by having a cloud-based ecosystem with the right tools and software to integrate various data sources. These tools should be able to consolidate data on climate, agronomy, water, farm equipment, supply chain, weeds, nutrients, and so much more to aid the farmer make decisions. 

Talend Data Fabric achieves that by offering a single suite of self-service apps for data integration and data integrity. It lets you stream data from multiple sources in real-time and helps derive crucial insights on the basis of trusted quality data.  Try Talend Data Fabric today .

Ready to get started with Talend?

case study of big data in agriculture

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Big data in agriculture: Between opportunity and solution

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Research output : Contribution to journal › Article › Academic › peer-review

CONTEXT: Big data applications in agriculture evolve fast, as more experience, applications, good practices and computational power become available. Actual solutions to real-life problems are scarce. What characterizes the adoption of big data problems to solutions and to what extent is there a match between them? OBJECTIVE: We aim to assess the conditions of the adoption of big data technologies in agricultural applications, based on the investigation of twelve real-life practical use cases in the precision agriculture and livestock domain. METHODS: We use a mixed method approach: a case study research around the twelve use cases of Horizon 2020 project CYBELE, varying from precision arable and livestock farming to fishing and food security, and a stakeholder survey (n = 56). Our analysis focuses on four perspectives: (1) the drivers of change that initiated the use cases; (2) the big data characteristics of the problem; (3) the technological maturity level of the solution both at start and end of the project; (4) the stakeholder perspective. RESULTS AND CONCLUSIONS: Results show that the use cases’ drivers of change are a combination of data-, technology, research- and commercial interests; most have at least a research drive. The big data characteristics (volume, velocity, variety, veracity) are well-represented, with most emphasis on velocity and variety. Technology readiness levels show that the majority of use cases started at experimental or lab environment stage and aims at a technical maturity of real-world small-scale deployment. Stakeholders’ main concern is cost, user friendliness and to embed the solution within their current work practice. The adoption of better-matching big data solutions is modest. Big data solutions do not work out-of-the-box when changing application domains. Additional technology development is needed for addressing the idiosyncrasies of agricultural applications. SIGNIFICANCE: We add a practical, empirical assessment of the current status of big data problems and solutions to the existing body of mainly theoretical knowledge. We considered the CYBELE research project as our laboratory for this. Our strength is that we interviewed the use case representatives in person, and that we included the stakeholders’ perspective in our results. Large-scale deployments need effective interdisciplinary approaches and long-term project horizons to address issues emerging from big data characteristics, and to avoid compartmentalization of agricultural sciences. We need both an engineering perspective – to make things work in practice – and a systems thinking perspective – to offer holistic, integrated solutions.

  • Big data solutions
  • Mixed-method approach
  • Precision Agriculture
  • Stakeholders
  • Technological maturity level

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

  • 10.1016/j.agsy.2021.103298 Licence: CC BY
  • https://edepot.wur.nl/556188 Licence: CC BY

Fingerprint

  • Use Case Computer Science 100%
  • Big Data Computer Science 100%
  • Food Security Food Science 100%
  • Adoption Psychology 30%
  • Real Life Engineering 20%
  • Velocity Engineering 20%
  • User Computer Science 16%
  • Research Project Computer Science 16%

Projects per year

CYBELE: FOSTERING PRECISION AGRICULTURE AND LIVESTOCK FARMING THROUGH SECURE ACCESS TO LARGE-SCALE HPC-ENABLED VIRTUAL INDUSTRIAL EXPERIMENTATION ENVIRONMENT EMPOWERING SCALABLE BIG DATA ANALYTICS

1/01/19 → 31/03/22

Project : EU research project

  • Big Data 100%
  • Crop Yield 74%
  • Data Set 44%
  • Big Data Analytics 37%

T1 - Big data in agriculture

T2 - Between opportunity and solution

AU - Osinga, Sjoukje A.

AU - Paudel, Dilli

AU - Mouzakitis, Spiros A.

AU - Athanasiadis, Ioannis N.

PY - 2022/1

Y1 - 2022/1

N2 - CONTEXT: Big data applications in agriculture evolve fast, as more experience, applications, good practices and computational power become available. Actual solutions to real-life problems are scarce. What characterizes the adoption of big data problems to solutions and to what extent is there a match between them? OBJECTIVE: We aim to assess the conditions of the adoption of big data technologies in agricultural applications, based on the investigation of twelve real-life practical use cases in the precision agriculture and livestock domain. METHODS: We use a mixed method approach: a case study research around the twelve use cases of Horizon 2020 project CYBELE, varying from precision arable and livestock farming to fishing and food security, and a stakeholder survey (n = 56). Our analysis focuses on four perspectives: (1) the drivers of change that initiated the use cases; (2) the big data characteristics of the problem; (3) the technological maturity level of the solution both at start and end of the project; (4) the stakeholder perspective. RESULTS AND CONCLUSIONS: Results show that the use cases’ drivers of change are a combination of data-, technology, research- and commercial interests; most have at least a research drive. The big data characteristics (volume, velocity, variety, veracity) are well-represented, with most emphasis on velocity and variety. Technology readiness levels show that the majority of use cases started at experimental or lab environment stage and aims at a technical maturity of real-world small-scale deployment. Stakeholders’ main concern is cost, user friendliness and to embed the solution within their current work practice. The adoption of better-matching big data solutions is modest. Big data solutions do not work out-of-the-box when changing application domains. Additional technology development is needed for addressing the idiosyncrasies of agricultural applications. SIGNIFICANCE: We add a practical, empirical assessment of the current status of big data problems and solutions to the existing body of mainly theoretical knowledge. We considered the CYBELE research project as our laboratory for this. Our strength is that we interviewed the use case representatives in person, and that we included the stakeholders’ perspective in our results. Large-scale deployments need effective interdisciplinary approaches and long-term project horizons to address issues emerging from big data characteristics, and to avoid compartmentalization of agricultural sciences. We need both an engineering perspective – to make things work in practice – and a systems thinking perspective – to offer holistic, integrated solutions.

AB - CONTEXT: Big data applications in agriculture evolve fast, as more experience, applications, good practices and computational power become available. Actual solutions to real-life problems are scarce. What characterizes the adoption of big data problems to solutions and to what extent is there a match between them? OBJECTIVE: We aim to assess the conditions of the adoption of big data technologies in agricultural applications, based on the investigation of twelve real-life practical use cases in the precision agriculture and livestock domain. METHODS: We use a mixed method approach: a case study research around the twelve use cases of Horizon 2020 project CYBELE, varying from precision arable and livestock farming to fishing and food security, and a stakeholder survey (n = 56). Our analysis focuses on four perspectives: (1) the drivers of change that initiated the use cases; (2) the big data characteristics of the problem; (3) the technological maturity level of the solution both at start and end of the project; (4) the stakeholder perspective. RESULTS AND CONCLUSIONS: Results show that the use cases’ drivers of change are a combination of data-, technology, research- and commercial interests; most have at least a research drive. The big data characteristics (volume, velocity, variety, veracity) are well-represented, with most emphasis on velocity and variety. Technology readiness levels show that the majority of use cases started at experimental or lab environment stage and aims at a technical maturity of real-world small-scale deployment. Stakeholders’ main concern is cost, user friendliness and to embed the solution within their current work practice. The adoption of better-matching big data solutions is modest. Big data solutions do not work out-of-the-box when changing application domains. Additional technology development is needed for addressing the idiosyncrasies of agricultural applications. SIGNIFICANCE: We add a practical, empirical assessment of the current status of big data problems and solutions to the existing body of mainly theoretical knowledge. We considered the CYBELE research project as our laboratory for this. Our strength is that we interviewed the use case representatives in person, and that we included the stakeholders’ perspective in our results. Large-scale deployments need effective interdisciplinary approaches and long-term project horizons to address issues emerging from big data characteristics, and to avoid compartmentalization of agricultural sciences. We need both an engineering perspective – to make things work in practice – and a systems thinking perspective – to offer holistic, integrated solutions.

KW - Big data solutions

KW - Case study

KW - Mixed-method approach

KW - Precision Agriculture

KW - Stakeholders

KW - Technological maturity level

U2 - 10.1016/j.agsy.2021.103298

DO - 10.1016/j.agsy.2021.103298

M3 - Article

AN - SCOPUS:85117363058

SN - 0308-521X

JO - Agricultural Systems

JF - Agricultural Systems

M1 - 103298

case study of big data in agriculture

CGIAR Platform for Big Data in Agriculture

case study of big data in agriculture

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  • CGIAR Research Programs
  • Big Data Partners

The CGIAR Platform for Big Data in Agriculture is where information becomes power: power to predict, prescribe, and produce more food, more sustainably. It democratizes decades of agricultural data empowering analysts, statisticians, programmers and more to mine information for trends and quirks, and develop rapid, accurate and compelling recommendations for farmers, researchers and policymakers.

This is an exciting new frontier in agricultural research-for-development. Better use of data will help drive better policy decisions, helping solve development problems more quickly, cheaply, and at a greater scale than before.

Data is much more than simply information: in expert hands, it is intelligence

The rapid growth in processing power and global connectivity means we can now quickly collect, share and analyze enormous amounts of data and turn it into recommendations that can be of use to farmers and policymakers.

Applying these ‘big data’ approaches to agriculture promises to find new ways to reduce hunger and poverty, and develop robust responses to challenges such as climate change, pest and disease outbreaks, and land degradation. It could help reduce some of the daily risks farmers in developing countries face, enabling them to thrive.

The CGIAR Platform for Big Data in Agriculture is a global leader in this effort. It is working to positively disrupt agricultural research, helping to generate impactful big data innovations that can revolutionize farming in developing countries.

Through its global leadership in organizing open data, convening partners to develop innovative ideas, and demonstrating the power of big data analytics through inspiring projects, it will help to ensure that the data revolution is deep, diffuse and democratic, reaching the most vulnerable farmers.

As the largest network of agricultural research organizations in the world, CGIAR is uniquely positioned to be a thought leader and global convener on the use of big data and information technology in agriculture.

Where We Work

  • The CGIAR Platform for Big Data in Agriculture is global. It connects experts all over the world to tackle stubborn agricultural challenges across the developing world, from Africa and Asia to Latin America and the Caribbean.

Impacts by 2021

The Platform aims to increase the impact of agricultural development by embracing big data approaches to solve development problems faster, better and at a greater scale than ever before.

The Platform is focusing on opening up and sharing agricultural data, demonstrating that CGIAR is able to hold in trust and deliver data-related global public goods. In this way CGIAR will become a broker of big data information, actively promoting data-driven agricultural development.

The Platform will also work to develop novel methodologies and innovative pilot projects to increase the impact of its community or researchers and analysts. It will establish non-traditional partnerships to bring together institutions with complementary big data expertise, connecting CGIAR scientists to a global network of big data practitioners and developers, expanding the delivery capability and horizons of CGIAR research.

The expected outcomes of the Platform include:

  • Greater data and knowledge sharing across CGIAR – by reducing barriers to information access and reuse, the Platform will democratize information availability and use, to help farmers and policymakers take reliable, informed decisions.
  • Foster a culture of open access publishing and data sharing across CGIAR Centers and partner organizations
  • Recognition of CGIAR as a global thought leader on big data in agriculture and 
development.
  • Brian King, Platform Coordinator (Cali, Colombia) [email protected]
  • Andrew Jarvis, Co-Founder (Cali, Colombia) [email protected]
  • Jawoo Koo, Co-Founder (Washington DC, USA) [email protected]
  • Mathilde Overduin, Project Manager (Cali, Colombia) [email protected]
  • Marianne McDade, Communications Coordinator (Cali, Colombia)  [email protected]

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The bigdata management and use case study for agriculture based on data.

The bigdata is produced by various industry fields. Especially, the agriculture bigdata is collected from IoT (Internet of Things) technology. In this study, we researched the Smart Farm Big Data management and utilization. The smartfarm bigdata in Korea refers to real-time environment data (temperature, relative humidity and solar radiation, etc.) from control system which applies ICT technology to facilities such as a greenhouse or livestock farm, and growth data, yield, and cultivation information of the farm. In smart farm research, the simple smart farm technology was developed from 2014. It seeks farmer's agricultural working convenience in managing greenhouses through remote control and monitoring. Currently, to enhance productivity of farm households , the research and development of utilization big data technology is actively carried out. In Rural Development Administration, we collect and manage the horticultural smart farm bigdata, and analyze and develop the productivity improvement model since 2017. The major crop in Korea horticulture are tomato, strawberry, paprika and oriental melon, etc. The number of collecting farm bigdata is 280 including the 9 crop items. The bigdata collected from smart farm is environmental, irrigation, and yield. First, the collecting and managing system (Agriculture Bigdata Management System: ABMS) is constructed for utilization the data. Second, we studied the productivity improvement model which had the characteristics of the crop (tomato, strawberry) and farmer's cultivation and management difficulties. The model means the development of environment setting for proper growth and production in cultivation period. For example, it is necessary to harvest stable production tomato in a harvest period for tomato productivity improvement. In order to develop the model, environmental variables are selected by high correlation production and growth from association analysis in growth stage. And the conditions of the environment variables (temperature, solar radiation) were compared with the production . Not only smartfarm data management and modelling, but also it can be applied other precise agriculture fields, such as livestock, fruit, and crop.

Keywords : Bigdata, management, platform, horticulture

INTRODUCTION

The term “bigdata” designates the definition caused by i nternet of things (IoT) and information communication Technology (ICT). The bigdata is very interesting topic for researcher to analysis their phenomenon. The google took over the bigdata analysis industry recently. The bigdata analysis industry has technology that planning the company management, performance management, and predicting the market using bigdata. That means bigdata has big meaning for determining based on data and can be big background for new market, data cloud computing and platform service. For example, in agriculture and consumption of agrifood, in Rural Development Administration (RDA), it has collected the bigdata for consumption of agrifood from Korean consumer panel. They have collected the data for consuming the agrifood receipt from market from 2010. So, the data become bigdata for Korean agrifood consumption. The researcher for agrifood field can analyze the consumer characteristic for agrifood, and can make the decision the harvesting for farmer and establishing the strategy. The bigdata is the huge data set for out-of-hand data management and analysis system. First, Cox and Ellsworth talked the bigdata concept, and Gartner definited the bigdata characteristic from 2001. The bigdata has 3 characteristic, velocity, volume, and variety. That means the information technology, IT has high technology, so it can collect the data, sending the data in short time. 

To understanding the concept of Korean “smartfarm”, we need to understand the gardening on greenhouse. In Korea, we build the various greenhouse and grow the kinds of crops in it. The Korean smartfarm is one of the areas of precision agriculture, not a new one. This agriculture system is using automated facilities, and information communication technology, monitoring and managing the environment in real time. And the smartfarm has complex environment control system. The reason why we call the samrtfarm is a smartphone or a tablet to monitor and control the facilities. The smartfarm in agriculuture is one of the national innovation projects in Korea from 2017. There is big smartfarm innovation complex by 2022 in main horticulture vegetable site, and education program is proceeded that young people has knowledge about the agriculturesmartfarm. This project purpose means having chance of young man’s job and development of the farm, rural areas from smartfarm. The Korean smart farm is widely divided in controlled horticulture, open field, fruit trees, and livestock, etc. The horticulture facility area is 4,000 ha in 2017, but the planning of extension to 7,000 ha in horticulture, and livestock 5750 farms.

In this smartfarm technology, Netherlands has the top research and technology of the precision farming. But it is very difficult to apply Netherlands technology to Korea because of climate condition, a king of crop, horticulture facility type, and area. Also, many farmers of horticulture in Korea, talked to us that the smartfarm is very convenient for farming (satisfaction score: 7.3 / 10), but it is much expensive to build smartfarm only for convenience. That means it is low satisfaction score for increasing productivity (6.0/10). and Farmers know the generated bigdata from smartfarm and control the environment using environment and growth data. Above of all, farmers insist that it is very important to increase crop productivity from information such as data, not convenience, and they have their income increasing. In Rural Development Administration, from the farmer’s thinking and Netherlands technology, we have researched the development of productivity improvement model from 2016.

Data collecting and management

First to develop the model, we need to collect the data. The data is generated by horticulture farms. The first data is environment data in a facility. The environmental data is collected by environment control system. The environment factors depend on equipment. The second is growth data. Because the crop growth condition is managed by environment condition. And third is yield, harvesting the fruit. These are very high relation for yield. We planned to collecting environment, growth, yield from smartfarm. And then we plan the collecting data of various crops in Korea. There are many crops from smartfarm but it is different to cultivate a kind of crop by site. We collected the tomato, strawberry, paprika, cucumber, flower, etc. in smartfarm. The number of farms collecting data is 280 farms from RDA. The number of smartfarm crop is table 1.

case study of big data in agriculture

Agriculture Bigdata Management System

The collected bigdata is environmental, irrigation, and yield. First, the collecting and managing system (Agriculture Bigdata Management System: ABMS) is constructed for utilization the data in RDA. So, researchers can be connected the system (ABMS) and upload and download data easily. Also, the data can be transformed by analyzing the data. The ABMS structure is Figure 1. This system flow is summarized as follows. First the data from smartfarm is collected to ABMS. Second, the collected data are stacked the converged for analysis. Also, it can be analyzed the smartfarm data using statistical method and machine learning, etc. Finally, the information from analyzing the data is serviced to farmer that provide the data. The system can be used for researcher in RDA and province agriculture researcher.

case study of big data in agriculture

The collecting the smartfarm bigdata

There are 3 data type of smarttarm, environment, growth, yield data. First is environment data, depending on the installed sensor, the environment data, for example inside temperature, relative humidity, solar radiation, existed CO 2 , watering (supply EC, pH, the number of watering, amount of water per a plant). The data is measured, and also displayed in the database for real-time. The second and third are growth data and yield. This data is not collected automatically, we planned the researching program that training researcher about smartfarm. They can investigate the growth condition, for example growth length, number of leaves, flower condition, fruit condition, and yield. Also, they can consult the farms that they visited later.

The development of productivity improvement model

From the collected data, we designed the development of productivity improvement model, first of all, the first crop is tomato. The reason why is that tomato is most famous vegetable in a whole world. Also, it can be cultivated all sites in Korea. So, we selected the 41 farms data from Jeonbuk, Jeonnam, Kyoungnam site in multi-span greenhouse. Their cultivation has the planting in Summer (middle of August) to ending in next summer for 1 year. To use the data, we need to understand what data is collected and growth characteristics of crop, physiology of tomato. The tomato has 7~8 weeks’ growth period from flowering to harvest fruit. It means the tomato yield is affected by 7~8 weeks’ environment condition, not right now (Figure 2). Also, for productivity improvement for tomato fruit, it is different to control inside temperature from collected data such as Figure 3. This result means that it is very important to control to long period (7-8 weeks) and short period (1 day). So, we used the characteristic the tomato physiology and environment condition.

case study of big data in agriculture

Monitoring method for smartfarm farmer

First, we developed the monitoring method the smartfarm data for farmer. The method is the farmers want that overall their farm consulting from their data. The guideline for smartfarm is based on crop growth characteristics. These methods are the decision tool for environment factor in growth stage and able to smartfarm management using various statistic tables and graph. Then we consulted the tomato farms using collected bigdata. The Figure 4 is 24 hours, daytime and night inside temperatures in smartfarm cultivation period. This farm was well managed by environment factor. The tomato growth temperature is 15~ 25 Celsius. So, for productivity improvement of tomato, this farm is considered another factor, watering, and growth. So, the method is published by book. And the farmer can be analyzed the own data easily.

case study of big data in agriculture

For these results, it is necessary to understand the principle of bigdata analysis. There is one point for analyzing the data is to reduce the time dimension of data. The environment data is generated by minute time unit form facility sensor. So, there are many data generated. But the growth data is collected by 1 week with replication from researcher. For analyzing the data, it is necessary to summarize the data by time.

Development the productivity improvement model

For developing the model, we analyzed the environment factor and growth condition and yield relation. We used the analysis method, multiple regression in statistics to find environment variable that affect the tomato yield. From this analysis the environment variables were transformed by 7 weeks of tomato crop growth. Therefore, inside temperature, solar radiation, amount of watering per plant, the number of watering per day, supply EC, supply pH and relative humidity were selected in model. And from the selected environment variable farm’s yield level. The daytime temperature has 18-29 Celsius, nighttime temperature 15~23 Celsius. It is similar to controlling temperature all smart farms (Figure 5). But the supply water is depending on season. Therefore, it is different go watering control to farm’s productivity level such as Figure 6.

case study of big data in agriculture

Furthermore, from growth and yield data, it is different to growth level for growth condition and season. It means the environment factor control in smartfarm is very important to improve tomato’s growth and yield. So, we developed the model that provides a way to control the environment setting value for farmer from collected environment, growth, and yield data. we developed the productivity improvement model. The model has the short environment condition setting for maintaining optimal growth stage and season. It is enable to increasing tomato yield, control growth condition by data. If anyone control the growth level using environment condition, it can be harvest y 150 kg/3.3 m 2 for 1 year (Table 2). For example, in middle growth stage (winter in Korea: solar radiation 993~1146J/cm 2 ), for growth length in tomato up, height of flower be downer, the inside temperature become control up, nighttime temperature become up such as table 3. So, the farmer can do control the environment condition comparison with crop growth.

case study of big data in agriculture

Future plan for smartfarm productivity improvement model

It is the future plan in smart farm bigdata research figure (Figure 7). For productivity improvement model and consulting from with data, we expand collection of farm data. And data will be also collected continuously by cropping season and data quality management will be maintained. Also, we develop the basic model for crop productivity improvement. And then we test the model for high accuracy model fitting from tomato smartfarm demonstration research. So, we will modify the productivity improvement model. Also, we will service the model for farmers from cloud service platform in RDA. Therefore, the model and data are used by smart farm industry, a farmer, and consumers.

case study of big data in agriculture

In this study, we introduced the bigdata definition and bigdata in agriculture. Especially there are various data, environment, growth and yield data from smartfarm data in Korea. We collected the environment data from sensor automatically, and growth and yield data by researcher. These data are stacked with the Agriculture Bigdata Management System in RDA, and is managed to analyze. We have developed the tomato productivity improvement model and consulting farmers from with data. For precision of model, we expand collection of farm data in various field, like horticulture, main vegetable. There are a small number of data because of smartfarm type, size of facility and species of crop. Data will be also collected continuously by cropping season and data quality management will be maintained. Also, we will develop and the basic model for crop productivity improvement. And then we will service the model for farmers from cloud service platform in RDA.

Cho, I. H., J. K. Kwon, D. M. Oh, H. D. Lee and T. W. Jung, 2013. The guideline for agriculture technology: horticulture, Rural Development Administration , Jeonju, Korea.

Han J. and M. K., 2015. Data Mining: concepts and Techniques, Elsevier, Inc, NY, USA.

Lee, H. R. and J.E. Song. 2018. The study of sharing and utilization horticulture bigdata, E-business research winter symposium.

Lee, G. H., Y. G. Ham, Y. D. Kim, J. H. Lee, and J. H. Won, 2016. The understanding of bigdata, knou press , Seoul, Korea.

Lee, H. R., S. H. Park, S. J. Park and D. H. Kim, 2018. The research of farmer feedback for improvement of productivity using horticulture smartfarm bigdata, horticultural science and technology 36(2): 211.

Lee, H. R., M. O. Park and S. J. Park 2018. The study of environment and growth variation in the regional tomato greenhouse facility by abnormal weather, 19 th c onference on agricultural and forest methodology : 132.

Lee, H. R., M. O. Kim, Y. B. Cho, S. J. Park and J. H. Hwang, 2018. The economic model for enhancement upgrade of tomato growth data in horticulture smart farm, horticultural science and technology 36(1): 80.

Lee, H. R., J. H. Hwang, M. O. Kim, and Y. B. Cho, 2017. Development of economic model for enhancement of tomato farmer’s productivity using Smartfarm bigdata, horticultural science and technology 36(2): 100.

Lee, H. R., Y. B. Cho, J. H. Hwang, D. H. Kim, Y. S. Yu, D. W. Choi, S. R. Kim, Y. J. Ahn, I. K. Ham, M. H. Jeon, G. W. Park, T. W. Kang, M. H. Yoon and S. Y. Lee, 2017. The research of collecting the controlled horticulture smart farm bigdata, horticultural science and technology 35(2): 1021.

Lee, H. R., S. J. Park, S. H. Park, and D. H. Kim, 2018. The bigdata analysis method for smartfarm environment factors management (tomato, 2 nd edition), Rural Development Administration , Jeonju, Korea.

Lee, M. S., J. K. Jang and D. H. Kim, 2017. The guideline for agriculture technology: tomato, Rural Development Administration , Jeonju, Korea.

Park, S. H., H. R. Lee, S. J. Park and Y. B. Cho, 2018. Facility horticulture smart farm environment integrated solar radiation quality management of bigdata, horticultural science and technology 36(2): 213.

Park, Y. H., W. H. Cho, M. H. Na, D. H. Kim, Y. B. Cho, and H. Y. Lee, 2018. Extraction of Environmental Factors Influencing Strawberry Yield in the facility farms using pattern recognition techniques, horticultural science and technology 36(1): 48-49.

Yeom, J. K., D. K. Kim and I. H. Jang, 2017. The linear regression analysis using SAS and R, Jayu Academy , Seoul, Korea.

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Is Women Entrepreneurship a Key Driver for Business Performance of the SMEs in Asian Developing Nations? A Case Study of SMEs in the Agricultural Sector in Sri Lanka

  • Published: 15 February 2024

Cite this article

  • Jayasooriya Mudiyanselage Harshana Miyuranga Upulwehera   ORCID: orcid.org/0000-0001-7995-0612 1 ,
  • Senanayake Mudiyanselage Sadeesha Nuwandi Senanayake 1 ,
  • Sisira Kumara Naradda Gamage   ORCID: orcid.org/0000-0002-7186-3474 1 ,
  • Jayasundara Mudiyanselage Samarakoon Banda Jayasundara 2 ,
  • Edirisinghe Mudiyanselage Samantha Ekanayake 3 ,
  • Jayasundara Mudiyanselage Ganga Lalani   ORCID: orcid.org/0000-0002-9000-6072 1 ,
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  • Prasanna Sisira Kumara Rajapakshe 2 &
  • Ran Pathige Indika Ruwan Prasanna   ORCID: orcid.org/0000-0002-5132-1272 1  

The contribution of women’s entrepreneurial activities to global economic development is vastly increasing, and the relationship between their entrepreneurial attributes and business performance is broadly questioned empirically. Therefore, a profound investigation of this phenomenon is essential as those activities’ social and economic outcomes are more critical in achieving economic development, specifically in developing countries. We examine in this study the association of entrepreneurial attributes of women entrepreneurs with the performance of SMEs interpretively by considering the small and medium enterprises in the agricultural sector in Sri Lanka as a case. To realize this purpose, we use a dataset from a survey conducted with 725 agro-based entrepreneurs in Sri Lanka. We applied the chi-square test and cross-tabulation to study the research phenomena and explored the association between women’s entrepreneurship and SME performance under four entrepreneurial attributes. The study confirms the high performance of women entrepreneurs with business-related training and experience by considering human capital attributes. Financial management attributes confirmed the association of women entrepreneurs’ credit market accessibility and better business record-keeping behavior with higher performance. Social capital attributes revealed the high performance of women entrepreneurs with household responsibilities, internal social capital, and external social capital. The innovation capacity attribute confirmed the association of women entrepreneurs’ product innovation and market innovation in business processes with higher business performance. The results emphasize the importance of government and policymakers’ intervention in forming a secured, favorable, and sustained business environment for women entrepreneurs.

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case study of big data in agriculture

Data Availability

The data supporting the findings of this study were collected in a survey conducted by a research project supported by the Accelerating Higher Education Expansion and Development (AHEAD) Operation of the Ministry of Higher Education of Sri Lanka, funded by the World Bank. However, some restrictions apply to the availability of this data, so it is not publicly available. Nevertheless, the data are available from the authors upon reasonable request and with the permission of the Research Project Leader of the aforementioned project.

Agyapong, D. (2010). Micro, small, and medium enterprises’ activities, income level, and poverty reduction in Ghana-A synthesis of related literature. International Journal of Business and Management, 5 (12), 196. https://doi.org/10.5539/ijbm.v5n12p196

Article   Google Scholar  

Agyapong, D., & Attram, A. B. (2019). Effect of owner-manager’s financial literacy on the performance of SMEs in the Cape Coast Metropolis in Ghana. Journal of Global Entrepreneurship Research, 9 (1), 1–13. https://doi.org/10.1186/s40497-019-0191-1

Ahmad, N., & Seymour, R. (2008). Defining entrepreneurial activity: Definitions supporting frameworks for data collection. Paris: (OECD Statistics Working Paper No. 2008/01) OECD Publishing. https://doi.org/10.1787/243164686763

Chapter   Google Scholar  

Ahmedova, S. (2015). Factors for increasing the competitiveness of small and medium-sized enterprises (SMEs) in Bulgaria. Procedia-Social and Behavioral Sciences, 195 , 1104–1112. https://doi.org/10.1016/j.sbspro.2015.06.155

Aidis, R., Welter, F., Smallbone, D., & Isakova, N. (2007). Female entrepreneurship in transition economies: The case of Lithuania and Ukraine. Feminist Economics, 13 (2), 157–183. https://doi.org/10.1080/13545700601184831

Ajuna, A., Ntale, J., & Ngui, T. (2018). Impact of training on the performance of women entrepreneurs in Kenya: Case of Meru Town. International Academic Journal of Innovation, Leadership and Entrepreneurship, 2 (2), 93–112.

Google Scholar  

Arch, E. C. (1993). Risk-taking: A motivational basis for sex differences. Psychological Reports, 73 (1), 3–11. https://doi.org/10.2466/pr0.1993.73.1.3

Asare, R., Akuffobe, M., Quaye, W., & Atta-Antwi, K. (2015). Characteristics of micro, small and medium enterprises in Ghana: Gender and implications for economic growth. African Journal of Science, Technology, Innovation and Development, 7 (1), 26–35. https://doi.org/10.1080/20421338.2014.979651

Audretsch, D. B., Belitski, M., & Brush, C. (2022). Innovation in women-led firms: An empirical analysis. Economics of Innovation and New Technology, 31 (1–2), 90–110. https://doi.org/10.1080/10438599.2020.1843992

Auwal, A. M., Mohamed, Z., Shamsudin, M. N., Sharifuddin, J., & Ali, F. (2020). External pressure influence on entrepreneurship performance of SMEs: A case study of Malaysian herbal industry. Journal of Small Business & Entrepreneurship, 32 (2), 149–171. https://doi.org/10.1080/08276331.2018.1509504

Bednar, S., Gicheva, D., & Link, A. N. (2021). Innovative activity and gender dynamics. Small Business Economics, 56 , 1591–1599. https://doi.org/10.1007/s11187-019-00282-2

Belwal, S., Belwal, R., & Saidi, F. A. (2014). Characteristics, motivations, and challenges of women entrepreneurs in Oman’s Al-Dhahira region. Journal of Middle East Women’s Studies, 10 (2), 135–151. https://doi.org/10.2979/jmiddeastwomstud.10.2.135

Ben Slimane, S., & M’henni, H. (2020). Entrepreneurship and development: Realities and future prospects. ISTE and John Wiley and Sons . https://doi.org/10.1002/9781119788119

Berge, L. I. O., Bjorvatn, K., & Tungodden, B. (2015). Human and financial capital for microenterprise development: Evidence from a field and lab experiment. Management Science, 61 (4), 707–722. https://doi.org/10.1287/mnsc.2014.1933

Bettany, S., Dobscha, S., O’Malley, L., & Prothero, A. (2010). Moving beyond binary opposition: Exploring the tapestry of gender in consumer research and marketing. Marketing Theory, 10 (1), 3–28. https://doi.org/10.1177/1470593109355244

Bontis, N., Keow, W. C. C., & Richardson, S. (2000). Intellectual capital and business performance in Malaysian industries. Journal of Intellectual Capital, 1 (1), 85–100. https://doi.org/10.1108/14691930010324188

Bosma, N., Van Praag, M., Thurik, R., & De Wit, G. (2004). The value of human and social capital investments for the business performance of startups. Small Business Economics, 23 , 227–236. https://doi.org/10.1023/B:SBEJ.0000032032.21192.72

Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of the education (pp. 241–258). Greenwood.

Brindley, C. (2005). Barriers to women achieving their entrepreneurial potential: Women and risk. International Journal of Entrepreneurial Behavior & Research., 11 (2), 144–161. https://doi.org/10.1108/13552550510590554

Brush, C. G., & Cooper, S. Y. (2012). Female entrepreneurship and economic development: An international perspective. Entrepreneurship & Regional Development, 24 (1–2), 1–6. https://doi.org/10.1080/08985626.2012.637340

Brush, C. G., & Vanderwerf, P. A. (1992). A comparison of methods and sources for obtaining estimates of new venture performance. Journal of Business Venturing, 7 (2), 157–170. https://doi.org/10.1016/0883-9026(92)90010-O

Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125 (3), 367–383. https://doi.org/10.1037/0033-2909.125.3.367

Carney, M. (2005). Corporate governance and competitive advantage in family–controlled firms. Entrepreneurship Theory and Practice, 29 (3), 249–265. https://doi.org/10.1111/j.1540-6520.2005.00081.x

Carranza, E., Dhakal, C., & Love, I. (2018). Female entrepreneurs : How and why are they different? (Working Paper No. 20) World Bank, Washington DC. https://doi.org/10.1596/31004

Carter, S. L., Anderson, S., & Shaw, E. (2001).  Women’s business ownership: A review of the academic, popular and internet literature . (Annual Review of Progress in Entrepreneurship: European Foundation for Management Development). Retrieved June 11, 2022, from.  https://pureportal.strath.ac.uk/en/publications/womens-business-ownership-a-review-of-the-academic-popular-and-in-3

Carter, N. M., & Allen, K. R. (1997). Size determinants of women-owned businesses: Choice or barriers to resources? Entrepreneurship & Regional Development, 9 (3), 211–220. https://doi.org/10.1080/08985629700000012

CBSL (Central Bank of Sri Lanka). (2020). Annual report of Central Bank of Sri Lanka 2020 . Retrieved June 1, 2022, from https://www.cbsl.gov.lk/en/publications/economic-and-financial-reports/annual-reports/annual-report-2020

Cicea, C., Popa, I., Marinescu, C., & Cătălina Ștefan, S. (2019). Determinants of SMEs’ performance: Evidence from European countries. Economic Research-Ekonomska Istraživanja, 32 (1), 1602–1620. https://doi.org/10.1080/1331677X.2019.1636699

Cirera, X., & Qasim, Q. (2014). Supporting growth-oriented women entrepreneurs: A review of the evidence and key challenges (Innovation, Technology and Entrepreneurship Policy Note 5). World Bank Group. Retrieved July 21, 2022, from https://documents1.worldbank.org/curated/en/301891468327585460/pdf/92210-REPLACEMENT-Supporting-Growth-Oriented-Women-Entrepreneurs-A-Review-of-the-Evidence-and-Key-Challenge.pdf

Cropley, D., & Cropley, A. (2017). Innovation capacity, organisational culture and gender. European Journal of Innovation Management, 20 (3), 493–510. https://doi.org/10.1108/EJIM-12-2016-0120

Cuba, R., Decenzo, D., & Anish, A. (1983). Management practices of successful female business owners. American Journal of Small Business, 8 (2), 40–46. https://doi.org/10.1177/104225878300800208

De Bruin, A., Brush, C. G., & Welter, F. (2007). Advancing a framework for coherent research on women’s entrepreneurship. Entrepreneurship Theory and Practice, 31 (3), 323–339. https://doi.org/10.1111/j.1540-6520.2007.00176.x

De Mel, S., McKenzie, D., & Woodruff, C. (2008). Returns to capital in microenterprises: Evidence from a field experiment. The Quarterly Journal of Economics, 123 (4), 1329–1372. https://doi.org/10.1162/qjec.2008.123.4.1329

De Mel, S., McKenzie, D., & Woodruff, C. (2009). Are women more credit constrained? Experimental evidence on gender and microenterprise returns. American Economic Journal: Applied Economics, 1 (3), 1–32. https://doi.org/10.1257/app.1.3.1

DeTienne, D. R., & Chandler, G. N. (2007). The role of gender in opportunity identification. Entrepreneurship Theory and Practice, 31 (3), 365–386. https://doi.org/10.1111/j.1540-6520.2007.00178.x

Don, E. (2006). Theory of performance. Retrieved June 15, 2023, from www.webpages.uidaho.edu/ele/scholars/Results/Workshops/Facilitators_Institute/Theory%20of%20Performance.pdf

Dougherty, D. (1992). Interpretive barriers to successful product innovation in large firms. Organization Science, 3 (2), 179–202. https://doi.org/10.1287/orsc.3.2.179

Eikhof, D. R., Summers, J., & Carter, S. (2013). Women doing their own thing: Media representations of female entrepreneurship. International Journal of Entrepreneurial Behaviour & Research, 19 (5), 547–564. https://doi.org/10.1108/IJEBR-09-2011-0107

Elizabeth, C., & Baines, S. (1998). Does gender affect business ‘performance’? A study of microbusinesses in business services in the UK. Entrepreneurship & Regional Development, 10 (2), 117–135. https://doi.org/10.1080/08985629800000007

Fafchamps, M., McKenzie, D., Quinn, S., & Woodruff, C. (2014). Microenterprise growth and the flypaper effect: Evidence from a randomized experiment in Ghana. Journal of Development Economics, 106 , 211–226. https://doi.org/10.1016/j.jdeveco.2013.09.010

Fairlie, R. W., & Robb, A. M. (2009). Gender differences in business performance: Evidence from the characteristics of business owners survey. Small Business Economics, 33 , 375–395. https://doi.org/10.1007/s11187-009-9207-5

Fiseha, G. G., & Oyelana, A. A. (2015). An assessment of the roles of small and medium enterprises (SMEs) in the local economic development (LED) in South Africa. Journal of Economics, 6 (3), 280–290. https://doi.org/10.1080/09765239.2015.11917617

Fornoni, M., Arribas, I. & Vila, J.E. (2012). An entrepreneur’s social capital and performance: The role of access to information in the Argentinean case (Discussion Papers on Economic Behaviours, Vol. 7) Retrieved 20 June, 2023, from https://ideas.repec.org/p/dbe/wpaper/0712.html

Gizaw, Y., Tsega, S., & Hailegiorgis, K. (2019). Assessment of the challenges and opportunities of women entrepreneurs in assessment of the challenges and opportunities of women entrepreneurs in Sodo Town, Wolaita Zone, SNNPR. Asian Journal of Economics, Business and Accounting, 10 (1), 1–8. https://doi.org/10.9734/AJEBA/2019/v10i130097

Glaeser, E. L., Laibson, D., & Sacerdote, B. (2002). An economic approach to social capital. The Economic Journal, 112 (483), 437–458. https://doi.org/10.1111/1468-0297.00078

Golrod, P. (2005). Effective factors in development of Iranian female entrepreneurs. Pajouhesh-e- Zanan, 3 (1), 101–123.

Gottschalk, S., & Niefert, M. (2013). Gender differences in business success of German start-up firms. International Journal of Entrepreneurship and Small Business, 18 (1), 15–46. https://doi.org/10.1504/IJESB.2013.050750

Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91 (3), 481–510. http://www.jstor.org/stable/2780199

Green, E., & Cohen, L. (1995). ‘Women’s business’: Are women entrepreneurs breaking new ground or simply balancing the demands of ‘women’s work’ in a new way? Journal of Gender Studies, 4 (3), 297–314. https://doi.org/10.1080/09589236.1995.9960615

Guzman, J., & Kacperczyk, A. O. (2019). Gender gap in entrepreneurship. Research Policy, 48 (7), 1666–1680. https://doi.org/10.1016/j.respol.2019.03.012

Hamid, Z. (2017). Impact of high-performance work systems on export-oriented SMEs performance: The mediating role of human capital development. The South East Asian Journal of Management, 11 (2), 142–163. https://doi.org/10.21002/seam.v11i2.8524

Hamilton, L., & De Klerk, N. (2016). Generation Y female students’ motivation towards entrepreneurship. International Journal of Business and Management Studies, 8 (2), 50–65. https://dergipark.org.tr/en/pub/ijbms/issue/26059/274510

Hammer, M. H. (2019). The non-existence of failure: Talents, education and exits of entrepreneurs . Saxion University of Applied Sciences.

Book   Google Scholar  

Hardy, M., & Kagy, G. (2018). Mind the (profit) gap: Why are female enterprise owners earning less than men? AEA Papers and Proceedings, 108 , 252–255. https://doi.org/10.1257/pandp.20181025

Harrison, R. T., & Mason, C. M. (2007). Does gender matter? Women business angels and the supply of entrepreneurial finance. Entrepreneurship Theory and Practice, 31 (3), 445–472. https://doi.org/10.1111/j.1540-6520.2007.00182.x

Hasan, F. S., & Almubarak, M. M. S. (2016). Factors influencing women entrepreneurs’ performance in SMEs. World Journal of Entrepreneurship, Management and Sustainable Development, 12 (2). https://doi.org/10.1108/WJEMSD-09-2015-0037

Hisrich, R. D., Peter, M. P., & Shepherd, D. A. (2008). Entrepreneurship . McGraw-Hill Co. Inc.

Hisrich, R. D., & Brush, C. G. (1987). Women entrepreneurs: A longitudinal study. Frontiers of Entrepreneurship Research, 187 (1), 566–578.

Hundera, M. B. (2014). Factors affecting academic staff turnover intentions and the moderating effect of gender. International Journal of Research in Business Management, 2 (9), 57–70.

Ibáñez, M. J., Guerrero, M., & Mahto, R. V. (2020). Women-led SMEs: Innovation and collaboration→ performance? Journal of the International Council for Small Business, 1 (3–4), 111–117. https://doi.org/10.1080/26437015.2020.1850155

Ibrahim, A. B., & Goodwin, J. R. (1986). Perceived causes of success in small business. American Journal of Small Business, 11 (2), 41–50. https://doi.org/10.1177/104225878601100204

IFC (International Finance Corporation) (2011). Financials and projects. Washington, DC, USA. Retrieved August 21, 2022, from https://www.ifc.org/content/dam/ifc/doc/mgrt/ar2011-volume2.pdf

IFC (International Finance Corporation) (2016). Annual report of International Finance Corporation 2016 . Washington, DC, USA. Retrieved August 23, 2022 from: https://www.ifc.org/wps/wcm/connect/publications_ext_content/ifc_external_publication_site/publications_listing_page/ar2016

Inmyxai, S., & Takahashi, Y. (2009). Entrepreneurs as decisive human resources and business performance for the Lao SMEs. Chinese Business Review, 8 (7), 29–47. https://doi.org/10.17265/1537-1506/2009.07.003

Javadian, G., & Singh, R. P. (2012). Examining successful Iranian women entrepreneurs: An exploratory study. Gender in Management: An International Journal, 27 (3), 148–164. https://doi.org/10.1108/17542411211221259

Jha, P., Makkad, M., & Mittal, S. (2018). Performance-oriented factors for women entrepreneurs–A scale development perspective. Journal of Entrepreneurship in Emerging Economies, 10 (2), 329–360. https://doi.org/10.1108/JEEE-08-2017-0053

Jianakoplos, N. A., & Bernasek, A. (1998). Are women more risk averse? Economic Inquiry, 36 (4), 620–630. https://doi.org/10.1111/j.1465-7295.1998.tb01740.x

Johnson, J. E., & Powell, P. L. (1994). Decision making, risk and gender: Are managers different? British Journal of Management, 5 (2), 123–138. https://doi.org/10.1111/j.1467-8551.1994.tb00073.x

Article   MathSciNet   Google Scholar  

Karadag, H. (2015). Financial management challenges in small and medium-sized enterprises: A strategic management approach. EMAJ Emerging Markets Journal, 5 (1), 26–40. https://doi.org/10.5195/emaj.2015.67

Kibas, P. B. (2001). Impact of credit on women-operated microenterprises in Uasin Gishu district. In P. O. Alila & P. O. Pedersen (Eds.), Negotiating social space: East African micro-enterprises (pp. 197–222). Africa World Press Inc.

Krieger-Boden, C., & Sorgner, A. (2018). Labor market opportunities for women in the digital age. Economics: The Open-Access Open-Assessment E-Journal, 12 (1), 1–8. https://doi.org/10.5018/economics-ejournal.ja.2018-28

Kuratko, D. F., & Hodgetts, R. M. (1997). Entrepreneurship . Harcourt College.

Kusa, R., Duda, J., & Suder, M. (2021). Explaining SME performance with fsQCA: The role of entrepreneurial orientation, entrepreneur motivation, and opportunity perception. Journal of Innovation & Knowledge, 6 (4), 234–245. https://doi.org/10.1016/j.jik.2021.06.001

Kuzilwa, J. (2005). The role of credit for small business success: A study of the National Entrepreneurship Development Fund in Tanzania. The Journal of Entrepreneurship, 14 (2), 131–161. https://doi.org/10.1177/097135570501400204

Lee-Gosselin, H., & Grise, J. (1990). Are women owner-managers challenging our definitions of entrepreneurship? An in-depth survey. Journal of Business Ethics, 9 (4), 423–433. https://doi.org/10.1007/BF00380341

Lerner, M., Brush, C., & Hisrich, R. (1997). Israeli women entrepreneurs: An examination of factors affecting performance. Journal of Business Venturing, 12 (4), 315–339. https://doi.org/10.1016/S0883-9026(96)00061-4

Li, Y. H., Huang, J. W., & Tsai, M. T. (2009). Entrepreneurial orientation and firm performance: The role of knowledge creation process. Industrial Marketing Management, 38 (4), 440–449. https://doi.org/10.1016/j.indmarman.2008.02.004

Liao, J., Welsch, H., & Stoica, M. (2003). Organizational absorptive capacity and responsiveness: An empirical investigation of growth–oriented SMEs. Entrepreneurship Theory and Practice, 28 (1), 63–86. https://doi.org/10.1111/1540-8520.00032

Lim, S., & Envick, B. R. (2013). Gender and entrepreneurial orientation: A multi-country study. International Entrepreneurship and Management Journal, 9 (3), 465–482. https://doi.org/10.1007/s11365-011-0183-2

Longstreth, M., Stafford, K., & Mauldin, T. (1987). Self-employed women and their families: Time use and socioeconomic characteristics. Journal of Small Business Management, 25 (3), 30–37.

Mandawa, B. (2016). Enhancing the performance of women-owned small and medium-sized enterprises in developing countries-A study of Zambia . The University of Manchester.

Mayoux, L. (1995).  From vicious to virtuous circles? Gender and micro-enterprise development  (UNRISD Occasional Paper No. 3). United Nations Research Institute for Social Development (UNRISD), Geneva. Retrieved May 22, 2022, from https://www.econstor.eu/bitstream/10419/148820/1/863102050.pdf

Mazzarol, T. (2014). Research review: A review of the latest research in the field of small business and entrepreneurship: Financial management in SMEs. Small Enterprise Research, 21 (1), 2–13. https://doi.org/10.1080/13215906.2014.11082073

McMenamin, J. (2002). Financial management: An introduction . Routledge.

Mehta, A. M., Ali, A., Saleem, H., Qamruzzaman, M., & Khalid, R. (2021). The effect of technology and open innovation on women-owned small and medium enterprises in Pakistan. The Journal of Asian Finance, Economics and Business, 8 (3), 411–422. https://doi.org/10.13106/jafeb.2021.vol8.no3.0411

Michna, A. (2009). The relationship between organizational learning and SME performance in Poland. Journal of European Industrial Training, 33 (4), 356–370. https://doi.org/10.1108/03090590910959308

Muravyev, A., Talavera, O., & Schäfer, D. (2009). Entrepreneurs’ gender and financial constraints: Evidence from international data. Journal of Comparative Economics, 37 (2), 270–286. https://doi.org/10.1016/j.jce.2008.12.001

Murphy, G. B., Trailer, J. W., & Hill, R. C. (1996). Measuring performance in entrepreneurship research. Journal of Business Research, 36 (1), 15–23. https://doi.org/10.1016/0148-2963(95)00159-X

Nählinder, J., Tillmar, M., & Wigren, C. (2015). Towards a gender-aware understanding of innovation: A three-dimensional route. International Journal of Gender and Entrepreneurship, 7 (1), 66–86. https://doi.org/10.1108/IJGE-09-2012-0051

Nair, S. R. (2020). The link between women entrepreneurship, innovation and stakeholder engagement: A review. Journal of Business Research, 119 , 283–290. https://doi.org/10.1016/j.jbusres.2019.06.038

North, D. C. (1990). Institutions, institutional change and economic performance . Cambridge University Press.

Omwenga, J., Mukulu, E., & Kanali, C. (2013). Towards improving the performance of women entrepreneurs in small and medium enterprises in Nairobi County-Kenya. International Journal of Business and Social Science, 4 (9), 312–316.

Poggesi, S., Mari, M., & De Vita, L. (2016). What’s new in female entrepreneurship research? Answers from the literature. International Entrepreneurship and Management Journal, 12 (3), 735–764. https://doi.org/10.1007/s11365-015-0364-5

Prasanna, R. P. I. R., Jayasundara, J. M. S. B., Naradda Gamage, S. K., Ekanayake, E. M. S., Rajapakshe, P. S. K., & Abeyrathne, G. A. K. N. J. (2019). Sustainability of SMEs in the competition: A systemic review on technological challenges and SME performance. Journal of Open Innovation: Technology, Market, and Complexity, 5 (4), 100. https://doi.org/10.3390/joitmc5040100

Prasanna, R. P. I. R., Upulwehera, J. M. H. M., Senarath, B. D. T. N., Abeyrathne, G. A. K. N. J., Rajapakshe, P. S. K., Jayasundara, J. M. S. B., Ekanayake, E. M. S., & Gamage, S. K. N. (2021). Factors determining the competitive strategic positions of the SMEs in Asian developing nations: Case study of SMEs in the agricultural sector in Sri Lanka. Economies, 9 (4), 193. https://doi.org/10.3390/economies9040193

Rajnoha, R., & Lorincová, S. (2015). Strategic management of business performance based on innovations and information support in specific conditions of Slovakia. Journal of Competitiveness., 7 (1), 3–21. https://doi.org/10.7441/joc.2015.01.01

Rosa, P., Carter, S., & Hamilton, D. (1996). Gender as a determinant of small business performance: Insights from a British study. Small Business Economics, 8 , 463–478. https://doi.org/10.1007/BF00390031

Ruiz-Jiménez, J. M., & Fuentes-Fuentes, M. D. M. (2016). Management capabilities, innovation, and gender diversity in the top management team: An empirical analysis in technology-based SMEs. BRQ Business Research Quarterly, 19 (2), 107–121. https://doi.org/10.1016/j.brq.2015.08.003

Sarfraz, M., Ivascu, L., Artene, A. E., Bobitan, N., Dumitrescu, D., Bogdan, O., & Burca, V. (2023). The relationship between firms’ financial performance and performance measures of circular economy sustainability: An investigation of the G7 countries. Economic Research-Ekonomska Istraživanja, 36 (1), 2101019. https://doi.org/10.1080/1331677x.2022.2101019

Scott, E. L., & Shu, P. (2017). Gender gap in high-growth ventures: Evidence from a university venture mentoring program. American Economic Review, 107 (5), 308–311. https://doi.org/10.1257/aer.p20171009

Shakeel, J., Mardani, A., Chofreh, A. G., Goni, F. A., & Klemeš, J. J. (2020). Anatomy of sustainable business model innovation. Journal of Cleaner Production, 261 , 121201. https://doi.org/10.1016/j.jclepro.2020.121201

Shava, H., & Rungani, E. C. (2016). Influence of gender on SME performance in emerging economies. Acta Commercii, 16 (1), 1–9. from:  https://hdl.handle.net/10520/EJC198638 . Accessed 20 Jul 2022.

Smith, T. M., & Reece, J. S. (1999). The relationship of strategy, fit productivity, and business performance in a services setting. Journal of Operations Management, 17 (2), 145–161. https://doi.org/10.1016/S0272-6963(98)00037-0

Stanford Encyclopedia of Philosophy (2015). Environmental ethics . Retrieved July 25, 2022, from: https://plato.stanford.edu/entries/ethics-environmental/

Storey, D. J. (1994). Understanding the small business sector . Routledge.

Subramaniam, M., & Youndt, M. A. (2005). The influence of intellectual capital on the types of innovative capabilities. Academy of Management Journal, 48 (3), 450–463. https://doi.org/10.5465/amj.2005.17407911

Sucuahi, W. T. (2013). Determinants of financial literacy of micro entrepreneurs in Davao City. International Journal of Accounting Research, 1 (1), 44–51. https://doi.org/10.12816/0001127

Tlaiss, H., & Kauser, S. (2011). The impact of gender, family, and work on the career advancement of Lebanese women managers. Gender in Management: An International Journal., 26 (1), 8–36. https://doi.org/10.1108/17542411111109291

Torres-Ortega, R., Rialp-Criado, A., Rialp-Criado, J., & Stoian, M. C. (2015). How to measure born-global firms’ orientation towards international markets? Revista Española De Investigación De Marketing ESIC, 19 (2), 107–123. https://doi.org/10.1016/j.reimke.2015.04.001

Upulwehera, J. M. H. M., Liyanage, D. Y. H., Bandara, K. B. T. U. K., Lalani, J. M. G., Gamage, S. K. N., Rajapakshe, P. S. K., Ekanayake, E. M. S., Jayasundara, J. M. S. B., & Prasanna, R. P. I. R. (2021). Preparedness and relative opinions of the owners of agro-based businesses to attain institutional support to face global challenges: A case of Sri Lankan SMEs. Sri Lanka Journal of Economic Research, 9 (2), 43–67. https://doi.org/10.4038/sljer.v9i2.162

Watson, J. (2002). Comparing the performance of male-and female-controlled businesses: Relating outputs to inputs. Entrepreneurship Theory and Practice, 26 (3), 91–100. https://doi.org/10.1177/104225870202600306

Watson, L. B., Robinson, D., Dispenza, F., & Nazari, N. (2012). African American women’s sexual objectification experiences: A qualitative study. Psychology of Women Quarterly, 36 (4), 458–475. https://doi.org/10.1177/0361684312454724

Wiklund, J., & Shepherd, D. (2003). Knowledge-based resources, entrepreneurial orientation, and the performance of small and medium-sized businesses. Strategic Management Journal, 24 (13), 1307–1314. https://doi.org/10.1002/smj.360

Wong, A., Tjosvold, D., & Liu, C. (2009). Innovation by teams in Shanghai, China: Cooperative goals for group confidence and persistence. British Journal of Management, 20 (2), 238–251. https://doi.org/10.1111/j.1467-8551.2008.00563.x

Wong, S. S. (2008). Task knowledge overlap and knowledge variety: The role of advice network structures and impact on group effectiveness. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 29 (5), 591–614. https://doi.org/10.1002/job.490

World Bank (2004).  World development report 2005: A better investment climate for everyone . The World Bank Group: Washington DC, USA. Retrieved June 20, 2022, from https://elibrary.worldbank.org/doi/epdf/10.1596/0-8213-5682-8

World Bank (2017). Enterprise surveys . World Bank Group: Washington DC, USA. Retrieved from July 12, 2022 from https://www.enterprisesurveys.org/en/enterprisesurveys

World Bank. (2020). Small and medium enterprises (SMEs) finance. World Bank Group: Washington DC, USA. Retrieved August 21, 2022, from https://www.worldbank.org/en/topic/smefinance

Xie, L., Zhou, J., Zong, Q., & Lu, Q. (2020). Gender diversity in R&D teams and innovation efficiency: Role of the innovation context. Research Policy, 49 (1), 103885. https://doi.org/10.1016/j.respol.2019.103885

Zulkiffli, S. N. A., & Perera, N. (2011). A literature analysis on business performance for SMEs: Subjective or objective measures? Proceedings of Society of Interdisciplinary Business Research (SIBR) 2011 Conference on Interdisciplinary Business Research . (pp. 1–9). https://doi.org/10.2139/ssrn.1867874

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This research was supported by the Accelerating Higher Education Expansion and Development (AHEAD) Operation of the Ministry of Higher Education of Sri Lanka and the Rajarata University of Sri Lanka and funded by the World Bank.

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Jayasooriya Mudiyanselage Harshana Miyuranga Upulwehera, Senanayake Mudiyanselage Sadeesha Nuwandi Senanayake, Sisira Kumara Naradda Gamage, Jayasundara Mudiyanselage Ganga Lalani, Ganihi Achchi Kankanamlage Niroshan Jayalath Abeyrathne & Ran Pathige Indika Ruwan Prasanna

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Jayasundara Mudiyanselage Samarakoon Banda Jayasundara & Prasanna Sisira Kumara Rajapakshe

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Conceptualization: Ran Pathige Indika Ruwan Prasanna and Jayasooriya Mudiyanselage Harshana Miyuranga Upulwehera; methodology: Sisira Kumara Naradda Gamage; formal analysis and investigation: Edirisinghe Mudiyanselage Samantha Ekanayake, Jayasundara Mudiyanselage Ganga Lalani, and Prasanna Sisira Kumara Rajapakshe; writing—original draft preparation: Ran Pathige Indika Ruwan Prasanna, Jayasooriya Mudiyanselage Harshana Miyuranga Upulwehera, Senanayake Mudiyanselage Sadeesha Nuwandi Senanayake, and Sisira Kumara Naradda Gamage; writing—review and editing: Jayasooriya Mudiyanselage Harshana Miyuranga Upulwehera, Senanayake Mudiyanselage Sadeesha Nuwandi Senanayake, and Jayasundara Mudiyanselage Samarakoon Banda Jayasundara; funding acquisition: Ganihi Achchi Kankanamlage Niroshan Jayalath Abeyrathne; resources: Ran Pathige Indika Ruwan Prasanna and Jayasooriya Mudiyanselage Harshana Miyuranga Upulwehera; supervision: Ran Pathige Indika Ruwan Prasanna.

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Upulwehera, J.M.H.M., Senanayake, S.M.S.N., Gamage, S.K.N. et al. Is Women Entrepreneurship a Key Driver for Business Performance of the SMEs in Asian Developing Nations? A Case Study of SMEs in the Agricultural Sector in Sri Lanka. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01833-z

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