Data analytics for crop management: a big data view

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

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

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:

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.

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

figure 3

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

figure 5

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 arecategorisation, 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:

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.

figure 6

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:

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.

figure 7

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

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.

Conclusion

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

Notes

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

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

Convolution Neural Network

Delineation of management zones

Deep Neural Network

Extreme learning machine

Enhanced Vegetation Index

Geographical information system

Global positioning system

Inertial navigation system

Long/Short Term Memory Network

Moderate-resolution imaging spectro-radiometer

Normalised difference vegetation index

Optimised soil adjusted vegetation index

Radial basis function

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

References

  1. 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. ArticleGoogle Scholar
  2. 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. ArticleGoogle Scholar
  3. 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. ArticleGoogle Scholar
  4. 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. ArticleGoogle Scholar
  5. 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. ArticleGoogle Scholar
  6. 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. ArticleGoogle Scholar
  7. 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.
  8. 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. ArticleGoogle Scholar
  9. 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. ArticleGoogle Scholar
  10. 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.
  11. 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. ArticleGoogle Scholar
  12. 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. ArticleGoogle Scholar
  13. 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.
  14. Berckmans D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev Sci. 2014;33:189–96. Google Scholar
  15. 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. ArticleGoogle Scholar
  16. 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.
  17. 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. ArticleGoogle Scholar
  18. 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. ArticleGoogle Scholar
  19. 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. ArticleGoogle Scholar
  20. 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. ArticleGoogle Scholar
  21. 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. ArticleGoogle Scholar
  22. 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.
  23. 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. ArticleGoogle Scholar
  24. 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
  25. 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. Google Scholar
  26. 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. ArticleGoogle Scholar
  27. 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. ArticleGoogle Scholar
  28. 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. ArticleGoogle Scholar
  29. 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. ArticleGoogle Scholar
  30. 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. ArticleGoogle Scholar
  31. 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. ArticleGoogle Scholar
  32. 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.
  33. 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. ArticleGoogle Scholar
  34. 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. ArticleGoogle Scholar
  35. 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. ArticleGoogle Scholar
  36. 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.
  37. 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
  38. 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. ArticleGoogle Scholar
  39. 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. ArticleGoogle Scholar
  40. 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
  41. 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
  42. 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.
  43. 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.
  44. 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. ArticleGoogle Scholar
  45. 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. ArticleGoogle Scholar
  46. 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. ArticleGoogle Scholar
  47. 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. Google Scholar
  48. 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. ArticleGoogle Scholar
  49. 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. ArticleGoogle Scholar
  50. 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. ArticleGoogle Scholar
  51. 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. ArticleGoogle Scholar
  52. 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. ArticleGoogle Scholar
  53. 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. ArticleGoogle Scholar
  54. 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. ArticleGoogle Scholar
  55. 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. ArticleGoogle Scholar
  56. 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. ArticleGoogle Scholar
  57. 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. ArticleGoogle Scholar
  58. 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. ArticleGoogle Scholar
  59. 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. ArticleGoogle Scholar
  60. 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. ArticleGoogle Scholar
  61. 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
  62. 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. ArticleGoogle Scholar
  63. 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. ArticleGoogle Scholar
  64. 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. ArticleGoogle Scholar
  65. 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. ArticleGoogle Scholar
  66. 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. ArticleGoogle Scholar
  67. 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. ArticleGoogle Scholar
  68. 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. ArticleGoogle Scholar
  69. 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. ArticleGoogle Scholar
  70. 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.
  71. 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. ArticleGoogle Scholar
  72. 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. ArticleGoogle Scholar
  73. 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. ArticleGoogle Scholar
  74. 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.
  75. 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. ArticleGoogle Scholar
  76. 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. ArticleGoogle Scholar
  77. 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. ArticleGoogle Scholar
  78. 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. ArticleGoogle Scholar
  79. 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. ArticleGoogle Scholar
  80. 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. ArticleGoogle Scholar
  81. 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. ArticleGoogle Scholar
  82. 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. ArticleGoogle Scholar
  83. 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. ArticleGoogle Scholar
  84. 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.
  85. 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. ArticleGoogle Scholar
  86. 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. ArticleGoogle Scholar
  87. 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.
  88. 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. ArticleGoogle Scholar
  89. 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. ArticleMATHGoogle Scholar
  90. 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. ArticleGoogle Scholar
  91. 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. ArticleGoogle Scholar
  92. 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. ArticleGoogle Scholar
  93. 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. ArticleGoogle Scholar
  94. 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.
  95. 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
  96. 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. ArticleGoogle Scholar
  97. 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
  98. 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.
  99. 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.
  100. 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. ArticleGoogle Scholar
  101. 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.
  102. 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. ArticleGoogle Scholar
  103. 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. ArticleGoogle Scholar
  104. 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. ArticleGoogle Scholar
  105. 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. ArticleGoogle Scholar
  106. 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. ArticleGoogle Scholar
  107. 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
  108. 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. ArticleGoogle Scholar
  109. 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. ArticleGoogle Scholar
  110. 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. ArticleGoogle Scholar
  111. 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. ArticleGoogle Scholar
  112. 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. ArticleGoogle Scholar
  113. 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. ArticleGoogle Scholar
  114. 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. ArticleGoogle Scholar
  115. 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. ArticleGoogle Scholar
  116. 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
  117. 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. ArticleGoogle Scholar
  118. 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. ArticleGoogle Scholar
  119. 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. ArticleGoogle Scholar
  120. 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. ArticleGoogle Scholar
  121. 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. ArticleGoogle Scholar
  122. 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. ArticleGoogle Scholar
  123. 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. ArticleGoogle Scholar
  124. 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. ArticleGoogle Scholar
  125. 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. ArticleGoogle Scholar
  126. 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. ArticleGoogle Scholar
  127. 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. ArticleGoogle Scholar
  128. 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. ArticleGoogle Scholar
  129. 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. ArticleGoogle Scholar
  130. 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.
  131. 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. ArticleGoogle Scholar
  132. 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. ArticleGoogle Scholar
  133. 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,
  134. 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.
  135. Stombaugh T, Shearer S. Equipment technologies for precision agriculture. J Soil Water Conserv. 2000;55:6–11. Google Scholar
  136. 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. ArticleGoogle Scholar
  137. 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. ArticleGoogle Scholar
  138. 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.
  139. 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. ArticleGoogle Scholar
  140. 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. ArticleGoogle Scholar
  141. 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. ArticleGoogle Scholar
  142. 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
  143. 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. ArticleGoogle Scholar
  144. 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. ArticleGoogle Scholar
  145. 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. ArticleGoogle Scholar
  146. 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. ArticleGoogle Scholar
  147. 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.
  148. 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. ArticleGoogle Scholar
  149. 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. ArticleGoogle Scholar
  150. 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. ArticleGoogle Scholar
  151. 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. Google Scholar
  152. 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.
  153. 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. ArticleGoogle Scholar
  154. 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. ArticleGoogle Scholar
  155. 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. ArticleGoogle Scholar
  156. 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. ArticleGoogle Scholar
  157. 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. ArticleGoogle Scholar

Acknowledgements

This work is supported by the SFI Strategic Partnerships Programme (16/SPP/3296) and is co-funded by Origin Enterprises Plc.