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Machine Learning in Agriculture: A Comprehensive Updated Review

Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece
Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy
FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece
Author to whom correspondence should be addressed.
Sensors 2021, 21(11), 3758;
Received: 6 April 2021 / Revised: 21 May 2021 / Accepted: 24 May 2021 / Published: 28 May 2021
(This article belongs to the Collection Sensors and Robotics for Digital Agriculture)


The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

1. Introduction

1.1. General Context of Machine Learning in Agriculture

Modern agriculture has to cope with several challenges, including the increasing call for food, as a consequence of the global explosion of earth’s population, climate changes [1], natural resources depletion [2], alteration of dietary choices [3], as well as safety and health concerns [4]. As a means of addressing the above issues, placing pressure on the agricultural sector, there exists an urgent necessity for optimizing the effectiveness of agricultural practices by, simultaneously, lessening the environmental burden. In particular, these two essentials have driven the transformation of agriculture into precision agriculture. This modernization of farming has a great potential to assure sustainability, maximal productivity, and a safe environment [5]. In general, smart farming is based on four key pillars in order to deal with the increasing needs; (a) optimal natural resources’ management, (b) conservation of the ecosystem, (c) development of adequate services, and (d) utilization of modern technologies [6]. An essential prerequisite of modern agriculture is, definitely, the adoption of Information and Communication Technology (ICT), which is promoted by policy-makers around the world. ICT can indicatively include farm management information systems, humidity and soil sensors, accelerometers, wireless sensor networks, cameras, drones, low-cost satellites, online services, and automated guided vehicles [7].
The large volume of data, which is produced by digital technologies and usually referred to as “big data”, needs large storage capabilities in addition to editing, analyzing, and interpreting. The latter has a considerable potential to add value for society, environment, and decision-makers [8]. Nevertheless, big data encompass challenges on account of their so-called “5-V” requirements; (a) Volume, (b) Variety, (c) Velocity, (d) Veracity, and (e) Value [9]. The conventional data processing techniques are incapable of meeting the constantly growing demands in the new era of smart farming, which is an important obstacle for extracting valuable information from field data [10]. To that end, Machine Learning (ML) has emerged, which is a subset of artificial intelligence [11], by taking advantage of the exponential computational power capacity growth.
There is a plethora of applications of ML in agriculture. According to the recent literature survey by Liakos et al. [12], regarding the time period of 2004 to 2018, four generic categories were identified (Figure 1). These categories refer to crop, water, soil, and livestock management. In particular, as far as crop management is concerned, it represented the majority of the articles amongst all categories (61% of the total articles) and was further sub-divided into:
  • Yield prediction;
  • Disease detection;
  • Weed detection;
  • Crop recognition;
  • Crop quality.
The generic categories dealing with the management of water and soil were found to be less investigated, corresponding cumulatively to 20% of the total number of papers (10% for each category).
Finally, two main sub-categories were identified for the livestock-related applications corresponding to a total 19% of journal papers:
  • Livestock production;
  • Animal welfare.

1.2. Open Problems Associated with Machine Learning in Agriculture

Due to the broad range of applications of ML in agriculture, several reviews have been published in this research field. The majority of these review studies have been dedicated to crop disease detection [13,14,15,16], weed detection [17,18], yield prediction [19,20], crop recognition [21,22], water management [23,24], animal welfare [25,26], and livestock production [27,28]. Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [29]. Finally, focus has been paid on big data analysis using ML, aiming at finding out real-life problems that originated from smart farming [30], or dealing with methods to analyze hyperspectral and multispectral data [31].
Although ML in agriculture has made considerable progress, several open problems remain, which have some common points of reference, despite the fact that the topic covers a variety of sub-fields. According to [23,24,28,32], the main problems are associated with the implementation of sensors on farms for numerous reasons, including high costs of ICT, traditional practices, and lack of information. In addition, the majority of the available datasets do not reflect realistic cases, since they are normally generated by a few people getting images or specimens in a short time period and from a limited area [15,21,22,23]. Consequently, more practical datasets coming from fields are required [18,20]. Moreover, the need for more efficient ML algorithms and scalable computational architectures has been pointed out, which can lead to rapid information processing [18,22,23,31]. The challenging background, when it comes to obtaining images, video, or audio recordings, has also been mentioned owing to changes in lighting [16,29], blind spots of cameras, environmental noise, and simultaneous vocalizations [25]. Another important open problem is that the vast majority of farmers are non-experts in ML and, thus, they cannot fully comprehend the underlying patterns obtained by ML algorithms. For this reason, more user-friendly systems should be developed. In particular, simple systems, being easy to understand and operate, would be valuable, as for example a visualization tool with a user-friendly interface for the correct presentation and manipulation of data [25,30,31]. Taking into account that farmers are getting more and more familiar with smartphones, specific smartphone applications have been proposed as a possible solution to address the above challenge [15,16,21]. Last but not least, the development of efficient ML techniques by incorporating expert knowledge from different stakeholders should be fostered, particularly regarding computing science, agriculture, and the private sector, as a means of designing realistic solutions [19,22,24,33]. As stated in [12], currently, all of the efforts pertain to individual solutions, which are not always connected with the process of decision-making, as seen for example in other domains.

1.3. Aim of the Present Study

As pointed out above, because of the multiple applications of ML in agriculture, several review studies have been published recently. However, these studies usually concentrate purely on one sub-field of agricultural production. Motivated by the current tremendous progress in ML, the increasing interest worldwide, and its impact in various do-mains of agriculture, a systematic bibliographic survey is presented on the range of the categories proposed in [12], which were summarized in Figure 1. In particular, we focus on reviewing the relevant literature of the last three years (2018–2020) for the intention of providing an updated view of ML applications in agricultural systems. In fact, this work is an updated continuation of the work presented at [12]; following, consequently, exactly the same framework and inclusion criteria. As a consequence, the scholarly literature was screened in order to cover a broad spectrum of important features for capturing the current progress and trends, including the identification of: (a) the research areas which are interested mostly in ML in agriculture along with the geographical distribution of the contributing organizations, (b) the most efficient ML models, (c) the most investigated crops and animals, and (d) the most implemented features and technologies.
As will be discussed next, overall, a 745% increase in the number of journal papers took place in the last three years as compared to [12], thus justifying the need for a new updated review on the specific topic. Moreover, crop management remained as the most investigated topic, with a number of ML algorithms having been exploited as a means of tackling the heterogeneous data that originated from agricultural fields. As compared to [12], more crop and animal species have been investigated by using an extensive range of input parameters coming mainly from remote sensing, such as satellites and drones. In addition, people from different research fields have dealt with ML in agriculture, hence, contributing to the remarkable advancement in this field.

1.4. Outline of the Paper

The remainder of this paper is structured as follows. The second section briefly describes the fundamentals of ML along with the subject of the four generic categories for the sake of better comprehension of the scope of the present study. The implemented methodology, along with the inclusive criteria and the search engines, is analyzed in the third section. The main performance metrics, which were used in the selected articles, are also presented in this section. The main results are shown in the fourth section in the form of bar and pie charts, while in the fifth section, the main conclusions are drawn by also discussing the results from a broader perspective. Finally, all the selected journal papers are summarized in Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8 and Table A9, in accordance with their field of application, and presented in the Appendix A, together with Table A10 and Table A11 that contain commonly used abbreviations, with the intention of not disrupting the flow of the main text.

2. Background

2.1. Fundamentals of Machine Learning: A Brief Overview

In general, the objective of ML algorithms is to optimize the performance of a task, via exploiting examples or past experience. In particular, ML can generate efficient relationships regarding data inputs and reconstruct a knowledge scheme. In this data-driven methodology, the more data are used, the better ML works. This is similar to how well a human being performs a particular task by gaining more experience [34]. The central outcome of ML is a measure of generalizability; the degree to which the ML algorithm has the ability to provide correct predictions, when new data are presented, on the basis of learned rules originated from preceding exposure to similar data [35]. More specifically, data involve a set of examples, which are described by a group of characteristics, usually called features. Broadly speaking, ML systems operate at two processes, namely the learning (used for training) and testing. In order to facilitate the former process, these features commonly form a feature vector that can be binary, numeric, ordinal, or nominal [36]. This vector is utilized as an input within the learning phase. In brief, by relying on training data, within the learning phase, the machine learns to perform the task from experience. Once the learning performance reaches a satisfactory point (expressed through mathematical and statistical relationships), it ends. Subsequently, the model that was developed through the training process can be used to classify, cluster, or predict.
An overview of a typical ML system is illustrated in Figure 2. With the intention of forming the derived complex raw data into a suitable state, a pre-processing effort is required. This usually includes: (a) data cleaning for removing inconsistent or missing items and noise, (b) data integration, when many data sources exist and (c) data transformation, such as normalization and discretization [37]. The extraction/selection feature aims at creating or/and identifying the most informative subset of features in which, subsequently, the learning model is going to be implemented throughout the training phase [38]. Regarding the feedback loop, which is depicted in Figure 2, it serves for adjustments pertaining to the feature extraction/selection unit as well as the pre-processing one that further improves the overall learning model’s performance. During the phase of testing, previously unseen samples are imported to the trained model, which are usually represented as feature vectors. Finally, an appropriate decision is made by the model (for example, classification or regression) in reliance of the features existing in each sample. Deep learning, a subfield of ML, utilizes an alternative architecture via shifting the process of converting raw data to features (feature engineering) to the corresponding learning system. Consequently, the feature extraction/selection unit is absent, resulting in a fully trainable system; it starts from a raw input and ends with the desired output [39,40].
Based on the learning type, ML can be classified according to the relative literature [41,42] as:
  • Supervised learning: The input and output are known and the machine tries to find the optimal way to reach an output given an input;
  • Unsupervised learning: No labels are provided, leaving the learning algorithm itself to generate structure within its input;
  • Semi-supervised learning: Input data constitute a mixture of labeled and non-labeled data;
  • Reinforcement learning: Decisions are made towards finding out actions that can lead to the more positive outcome, while it is solely determined by trial and error method and delayed outcome.
Nowadays, ML is used in facilitating several management aspects in agriculture [12] and in a plethora of other applications, such as image recognition [43], speech recognition [44], autonomous driving [45], credit card fraud detection [46], stock market forecasting [47], fluid mechanics [48], email, spam and malware filtering [49], medical diagnosis [40], contamination detection in urban water networks [50], and activity recognition [51], to mention but a few.

2.2. Brief Description of the Four Generic Categories

2.2.1. Crop Management

The crop management category involves versatile aspects that originated from the combination of farming techniques in the direction of managing the biological, chemical and physical crop environment with the aim of reaching both quantitative and qualitative targets [52]. Using advanced approaches to manage crops, such as yield prediction, disease detection, weed detection, crop recognition, and crop quality, contributes to the increase of productivity and, consequently, the financial income. The above aspects constitute key goals of precision agriculture.

Yield Prediction

In general, yield prediction is one of the most important and challenging topics in modern agriculture. An accurate model can help, for instance, the farm owners to take informed management decisions on what to grow towards matching the crop to the existing market’s demands [20]. However, this is not a trivial task; it consists of various steps. Yield prediction can be determined by several factors such as environment, management practices, crop genotypic and phenotypic characteristics, and their interactions. Hence, it necessitates a fundamental comprehension of the relationship between these interactive factors and yield. In turn, identifying such kinds of relationships mandates comprehensive datasets along with powerful algorithms such as ML techniques [53].

Disease Detection

Crop diseases constitute a major threat in agricultural production systems that deteriorate yield quality and quantity at production, storage, and transportation level. At farm level, reports on yield losses, due to plant diseases, are very common [54]. Furthermore, crop diseases pose significant risks to food security at a global scale. Timely identification of plant diseases is a key aspect for efficient management. Plant diseases may be provoked by various kinds of bacteria, fungi, pests, viruses, and other agents. Disease symptoms, namely the physical evidence of the presence of pathogens and the changes in the plants’ phenotype, may consist of leaf and fruit spots, wilting and color change [55], curling of leaves, etc. Historically, disease detection was conducted by expert agronomists, by performing field scouting. However, this process is time-consuming and solely based on visual inspection. Recent technological advances have made commercially available sensing systems able to identify diseased plants before the symptoms become visible. Furthermore, in the past few years, computer vision, especially by employing deep learning, has made remarkable progress. As highlighted by Zhang et al. [56], who focused on identifying cucumber leaf diseases by utilizing deep learning, due to the complex environmental background, it is beneficial to eliminate background before model training. Moreover, accurate image classifiers for disease diagnosis need a large dataset of both healthy and diseased plant images. In reference to large-scale cultivations, such kinds of automated processes can be combined with autonomous vehicles, to timely identify phytopathological problems by implementing regular inspections. Furthermore, maps of the spatial distribution of the plant disease can be created, depicting the zones in the farm where the infection has been spread [57].

Weed Detection

As a result of their prolific seed production and longevity, weeds usually grow and spread invasively over large parts of the field very fast, competing with crops for the resources, including space, sunlight, nutrients, and water availability. Besides, weeds frequently arise sooner than crops without having to face natural enemies, a fact that adversely affects crop growth [18]. In order to prevent crop yield reduction, weed control is an important management task by either mechanical treatment or application of herbicides. Mechanical treatment is, in most cases, difficult to be performed and ineffective if not properly performed, making herbicide application the most widely used operation. Using large quantities of herbicides, however, turns out to be both costly and detrimental for the environment, especially in the case of uniform application without taking into account the spatial distribution of the weeds. Remarkably, long-term herbicide use is very likely to make weeds more resistant, thus, resulting in more demanding and expensive weed control. In recent years, considerable achievements have been made pertaining to the differentiation of weeds from crops on the basis of smart agriculture. This discrimination can be accomplished by using remote or proximal sensing with sensors attached on satellites, aerial, and ground vehicles, as well as unmanned vehicles (both ground (UGV) and aerial (UAV)). The transformation of data gathered by UAVs into meaningful information is, however, still a challenging task, since both data collection and classification need painstaking effort [58]. ML algorithms coupled with imaging technologies or non-imaging spectroscopy can allow for real-time differentiation and localization of target weeds, enabling precise application of herbicides to specific zones, instead of spraying the entire fields [59] and planning of the shortest weeding path [60].

Crop Recognition

Automatic recognition of crops has gained considerable attention in several scientific fields, such as plant taxonomy, botanical gardens, and new species discovery. Plant species can be recognized and classified via analysis of various organs, including leaves, stems, fruits, flowers, roots, and seeds [61,62]. Using leaf-based plant recognition seems to be the most common approach by examining specific leaf’s characteristics like color, shape, and texture [63]. With the broader use of satellites and aerial vehicles as means of sensing crop properties, crop classification through remote sensing has become particularly popular. As in the above sub-categories, the advancement on computer software and image processing devices combined with ML has led to the automatic recognition and classification of crops.

Crop Quality

Crop quality is very consequential for the market and, in general, is related to soil and climate conditions, cultivation practices and crop characteristics, to name a few. High quality agricultural products are typically sold at better prices, hence, offering larger earnings to farmers. For instance, as regards fruit quality, flesh firmness, soluble solids content, and skin color are among the most ordinary maturity indices utilized for harvesting [64]. The timing of harvesting greatly affects the quality characteristics of the harvested products in both high value crops (tree crops, grapes, vegetables, herbs, etc.) and arable crops. Therefore, developing decision support systems can aid farmers in taking appropriate management decisions for increased quality of production. For example, selective harvesting is a management practice that may considerably increase quality. Furthermore, crop quality is closely linked with food waste, an additional challenge that modern agriculture has to cope with, since if the crop deviates from the desired shape, color, or size, it may be thrown away. Similarly to the above sub-section, ML algorithms combined with imaging technologies can provide encouraging results.

2.2.2. Water Management

The agricultural sector constitutes the main consumer of available fresh water on a global scale, as plant growth largely relies on water availability. Taking into account the rapid depletion rate of a lot of aquifers with negligible recharge, more effective water management is needed for the purpose of better conserving water in terms of accomplishing a sustainable crop production [65]. Effective water management can also lead to the improvement of water quality as well as reduction of pollution and health risks [66]. Recent research on precision agriculture offers the potential of variable rate irrigation so as to attain water savings. This can be realized by implementing irrigation at rates, which vary according to field variability on the basis of specific water requirements of separate management zones, instead of using a uniform rate in the entire field. The effectiveness and feasibility of the variable rate irrigation approach depend on agronomic factors, including topography, soil properties, and their effect on soil water in order to accomplish both water savings and yield optimization [67]. Carefully monitoring the status of soil water, crop growth conditions, and temporal and spatial patterns in combination with weather conditions monitoring and forecasting, can help in irrigation programming and efficient management of water. Among the utilized ICTs, remote sensing can provide images with spatial and temporal variability associated with the soil moisture status and crop growth parameters for precision water management. Interestingly, water management is challenging enough in arid areas, where groundwater sources are used for irrigation, with the precipitation providing only part of the total crop evapotranspiration (ET) demands [68].

2.2.3. Soil Management

Soil, a heterogeneous natural resource, involves mechanisms and processes that are very complex. Precise information regarding soil on a regional scale is vital, as it contributes towards better soil management consistent with land potential and, in general, sustainable agriculture [5]. Better management of soil is also of great interest owing to issues like land degradation (loss of the biological productivity), soil-nutrient imbalance (due to fertilizers overuse), and soil erosion (as a result of vegetation overcutting, improper crop rotations rather than balanced ones, livestock overgrazing, and unsustainable fallow periods) [69]. Useful soil properties can entail texture, organic matter, and nutrients content, to mention but a few. Traditional soil assessment methods include soil sampling and laboratory analysis, which are normally expensive and take considerable time and effort. However, remote sensing and soil mapping sensors can provide low-cost and effortless solution for the study of soil spatial variability. Data fusion and handling of such heterogeneous “big data” may be important drawbacks, when traditional data analysis methods are used. ML techniques can serve as a trustworthy, low-cost solution for such a task.

2.2.4. Livestock Management

It is widely accepted that livestock production systems have been intensified in the context of productivity per animal. This intensification involves social concerns that can influence consumer perception of food safety, security, and sustainability, based on animal welfare and human health. In particular, monitoring both the welfare of animals and overall production is a key aspect so as to improve production systems [70]. The above fields take place in the framework of precision livestock farming, aiming at applying engineering techniques to monitor animal health in real time and recognizing warning messages, as well as improving the production at the initial stages. The role of precision livestock farming is getting more and more significant by supporting the decision-making processes of livestock owners and changing their role. It can also facilitate the products’ traceability, in addition to monitoring their quality and the living conditions of animals, as required by policy-makers [71]. Precision livestock farming relies on non-invasive sensors, such as cameras, accelerometers, gyroscopes, radio-frequency identification systems, pedometers, and optical and temperature sensors [25]. IoT sensors leverage variable physical quantities (VPQs) as a means of sensing temperature, sound, humidity, etc. For instance, IoT sensors can warn if a VPQ falls out of regular limits in real-time, giving valuable information regarding individual animals. As a result, the cost of repetitively and arduously checking each animal can be reduced [72]. In order to take advantage of the large amounts of data, ML methodologies have become an integral part of modern livestock farming. Models can be developed that have the capability of defining the manner a biological system operates, relying on causal relationships and exploiting this biological awareness towards generating predictions and suggestions.

Animal Welfare

There is an ongoing concern for animal welfare, since the health of animals is strongly associated with product quality and, as a consequence, predominantly with the health of consumers and, secondarily, with the improvement of economic efficiency [73]. There exist several indexes for animal welfare evaluation, including physiological stress and behavioral indicators. The most commonly used indicator is animal behavior, which can be affected by diseases, emotions, and living conditions, which have the potential to demonstrate physiological conditions [25]. Sensors, commonly used to detect behavioral changes (for example, changes in water or food consumption, reduced animal activity), include microphone systems, cameras, accelerometers, etc.

Livestock Production

The use of sensor technology, along with advanced ML techniques, can increase livestock production efficiency. Given the impact of practices of animal management on productive elements, livestock owners are getting cautious of their asset. However, as the livestock holdings get larger, the proper consideration of every single animal is very difficult. From this perspective, the support to farmers via precision livestock farming, mentioned above, is an auspicious step for aspects associated with economic efficiency and establishment of sustainable workplaces with reduced environmental footprint [74]. Generally, several models have been used in animal production, with their intentions normally revolving around growing and feeding animals in the best way. However, the large volumes of data being involved, again, call for ML approaches.

3. Methods

3.1. Screening of the Relative Literature

In order to identify the relevant studies concerning ML in respect to different aspects of management in agriculture, the search engines of Scopus, Google Scholar, ScienceDirect, PubMed, Web of Science, and MDPI were utilized. In addition, keywords’ combinations of “machine learning” in conjunction with each of the following: “crop management”, “water management”, “soil management”, and “livestock management” were used. Our intention was to filter the literature on the same framework as [12]; however, focusing solely within the period 2018–2020. Once a relevant study was being identified, the references of the paper at hand were being scanned to find studies that had not been found throughout the initial searching procedure. This process was being iterated until no relevant studies occurred. In this stage, only journal papers were considered eligible. Thus, non-English studies, conferences papers, chapters, reviews, as well as Master and Doctoral Theses were excluded. The latest search was conducted on 15 December 2020. Subsequently, the abstract of each paper was being reviewed, while, at a next stage, the full text was being read to decide its appropriateness. After a discussion between all co-authors with reference to the appropriateness of the selected papers, some of them were excluded, in the case they did not meet the two main inclusion criteria, namely: (a) the paper was published within 2018–2020 and (b) the paper referred to one of the categories and sub-categories, which were summarized in Figure 1. Finally, the papers were classified in these sub-categories. Overall, 338 journal papers were identified. The flowchart of the present review methodology is depicted in Figure 3, based on the PRISMA guidelines [75], along with information about at which stage each exclusive criterion was imposed similarly to recent systematic review studies such as [72,76,77,78].

3.2. Definition of the Performance Metrics Commonly Used in the Reviewed Studies

In this subsection, the most commonly used performance metrics of the reviewed papers are briefly described. In general, these metrics are utilized in an effort to provide a common measure to evaluate the ML algorithms. The selection of the appropriate metrics is very important, since: (a) how the algorithm’s performance is measured relies on these metrics and (b) the metric itself can influence the way the significance of several characteristics is weighted.
Confusion matrix constitutes one of the most intuitive metrics towards finding the correctness of a model. It is used for classification problems, where the result can be of at least two types of classes. Let us consider a simple example, by giving a label to a target variable: for example, “1” when a plant has been infected with a disease and “0” otherwise. In this simplified case, the confusion matrix (Figure 4) is a 2 × 2 table having two dimensions, namely “Actual” and “Predicted”, while its dimensions have the outcome of the comparison between the predictions with the actual class label. Concerning the above simplified example, this outcome can acquire the following values:
  • True Positive (TP): The plant has a disease (1) and the model classifies this case as diseased (1);
  • True Negative (TN): The plant does not have a disease (0) and the model classifies this case as a healthy plant (0);
  • False Positive (FP): The plant does not have a disease (0), but the model classifies this case as diseased (1);
  • False Negative (FN): The plant has a disease (1), but the model classifies this case as a healthy plant (0).
As can be shown in Table 1, the aforementioned values can be implemented in order to estimate the performance metrics, typically observed in classification problems [79].
Other common evaluation metrics were the coefficient of correlation ( R ), coefficient of determination ( R 2 ; basically, the square of the correlation coefficient), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE), which can be given via the following relationships [80,81]:
R = T · t = 1 T Z t · X t t = 1 T Z t · t = 1 T X t T · t = 1 T Z t 2 t = 1 T Z t 2 · T · t = 1 T X t 2 t = 1 T X t 2 ,
M A E = 1 T · t = 1 T Z t X t ,
M A P E = 1 T · t = 1 T Z t X t Z t ,
M S E = 1 T · t = 1 T Z t X t 2 ,
where X t and Z t correspond to the predicted and real value, respectively, t stands for the iteration at each point, while T for the testing records number. Accordingly, low values of MAE, MAPE, and MSE values denote a small error and, hence, better performance. In contrast, R 2 near 1 is desired, which demonstrates better model performance and also that the regression curve efficiently fits the data.

4. Results

4.1. Preliminary Data Visualization Analysis

Graphical representation of data related to the reviewed studies, by using maps, bar or pie charts, for example, can provide an efficient approach to demonstrate and interpret the patterns of data. The data visualization analysis, as it usually refers to, can be vital in the context of analyzing large amounts of data and has gained remarkable attention in the past few years, including review studies. Indicatively, significant results can be deduced in an effort to identify: (a) the most contributing authors and organizations, (b) the most contributing international journals (or equivalently which research fields are interested in this topic), and (c) the current trends in this field [82].

4.1.1. Classification of the Studies in Terms of Application Domain

As can be seen in the flowchart of the present methodology (Figure 3), the literature survey on ML in agriculture resulted in 338 journal papers. Subsequently, these studies were classified into the four generic categories as well as into their sub-categories, as already mentioned above. Figure 5 depicts the aforementioned papers’ distribution. In particular, the majority of the studies were intended for crop management (68%), while soil management (10%), water management (10%), and livestock management (12% in total; animal welfare: 7% and livestock production: 5%) had almost equal contribution in the present bibliographic survey. Focusing on crop management, the most contributing sub-categories were yield prediction (20%) and disease detection (19%). The former research field arises as a consequence of the increasing interest of farmers in taking decisions based on efficient management that can lead to the desired yield. Disease detection, on the other hand, is also very important, as diseases constitute a primary menace for food security and quality assurance. Equal percentages (13%) were observed for weed detection and crop recognition, both of which are essential in crop management at farm and agricultural policy making level. Finally, examination of crop quality was relatively scarce corresponding to 3% of all studies. This can be attributed to the complexity of monitoring and modeling the quality-related parameters.
In this fashion, it should be mentioned again that all the selected journal papers are summarized in Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8 and Table A9, depending on their field of application, and presented in the Appendix A. The columns of the tables correspond (from left to right) to the “Reference number” (Ref), “Input Data”, “Functionality”, “Models/Algorithms”, and “Best Output”. One additional column exists for the sub-categories belonging in crop management, namely “Crop”, whereas the corresponding column in the sub-categories pertaining to livestock management refers to “Animal”. The present systematic review deals with a plethora of different ML models and algorithms. For the sake of brevity, the commonly used abbreviations are used instead of the entire names, which are summarized in Table A10 and Table A11 (presented also in the Appendix A). The list of the aforementioned Tables, along with their content, is listed in Table 2.

4.1.2. Geographical Distribution of the Contributing Organizations

The subject of this sub-section is to find out the geographical distribution of all the contributing organizations in ML applications in agriculture. To that end, the author’s affiliation was taken into account. In case a paper included more than one author, which was the most frequent scenario, each country could contribute only once in the final map chart (Figure 6), similarly to [83,84]. As can be gleaned from Figure 6, investigating ML in agriculture is distributed worldwide, including both developed and developing economies. Remarkably, out of the 55 contributing countries, the least contribution originated from African countries (3%), whereas the major contribution came from Asian countries (55%). The latter result is attributed mainly to the considerable contribution of Chinese (24.9%) as well as Indian organizations (10.1%). USA appeared to be the second most contributing country with 20.7% percentage, while Australia (9.5%), Spain (6.8%), Germany (5.9%), Brazil, UK, and Iran (5.62%) seem to be particularly interested in ML in agriculture. It should be stressed that livestock management, which is a relatively different sub-field comparing to crop, water, and soil management, was primary examined from studies coming from Australia, USA, China, and UK, while all the papers regarding Ireland were focused on animals. Finally, another noteworthy observation is that a large number of articles were a result of international collaboration, with the synergy of China and USA standing out.

4.1.3. Distribution of the Most Contributing Journal Papers

For the purpose of identifying the research areas that are mostly interested in ML in agriculture, the most frequently appeared international journal papers are depicted in Figure 7. In total, there were 129 relevant journals. However, in this bar chart, only the journals contributing with at least 4 papers are presented for brevity. As a general remark, remote sensing was of particular importance, since reliable data from satellites and UAV, for instance, constitute valuable input data for the ML algorithms. In addition, smart farming, environment, and agricultural sustainability were of central interest. Journals associated with computational techniques were also presented with considerable frequency. A typical example of such type of journals, which was presented in the majority of the studies with a percentage of 19.8%, was “Computers and Electronics in Agriculture”. This journal aims at providing the advances in relation to the application of computers and electronic systems for solving problems in plant and animal production.
The “Remote Sensing” and “Sensors” journals followed with approximately 11.8% and 6.5% of the total number of publications, respectively. These are cross-sectoral journals that are concentrated on applications of science and sensing technologies in various fields, including agriculture. Other journals, covering this research field, were also “IEEE Access” and “International Journal of Remote Sensing” with approximately 2.1% and 1.2% contribution, respectively. Moreover, agriculture-oriented journals were also presented in Figure 7, including “Precision Agriculture”, “Frontiers in Plant Science”, “Agricultural and Forest Meteorology”, and “Agricultural Water Management” with 1–3% percentage. These journals deal with several aspects of agriculture ranging from management strategies (so as to incorporate spatial and temporal data as a means of optimizing productivity, resource use efficiency, sustainability and profitability of agricultural production) up to crop molecular genetics and plant pathogens. An interdisciplinary journal concentrating on soil functions and processes also appeared with 2.1%, namely “Geoderma”, plausibly covering the soil management generic category. Finally, several journals focusing on physics and applied natural sciences, such as “Applied Sciences” (2.7%), “Scientific Reports” (1.8%), “Biosystems Engineering” (1.5%), and “PLOS ONE” (1.5%), had a notable contribution to ML studies. As a consequence, ML in agriculture concerns several disciplines and constitutes a fundamental area for developing various techniques, which can be beneficial to other fields as well.

4.2. Synopsis of the Main Features Associated with the Relative Literature

4.2.1. Machine Learning Models Providing the Best Results

A wide range of ML algorithms was implemented in the selected studies; their abbreviations are given in Table A11. The ML algorithms that were used by each study as well as those that provided the best output have been listed in the last two columns of Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8 and Table A9. These algorithms can be classified into the eight broad families of ML models, which are summarized in Table A10. Figure 8 focuses on the best performed ML models as a means of capturing a broad picture of the current situation and demonstrating advancement similarly to [12].
As can be demonstrated in Figure 8, the most frequent ML model providing the best output was, by far, Artificial Neural Networks (ANNs), which appeared in almost half of the reviewed studies (namely, 51.8%). More specifically, ANN models provided the best results in the majority of the studies concerning all sub-categories. ANNs have been inspired by the biological neural networks that comprise human brains [85], while they allow for learning via examples from representative data describing a physical phenomenon. A distinct characteristic of ANNs is that they can develop relationships between dependent and independent variables, and thus extract useful information from representative datasets. ANN models can offer several benefits, such as their ability to handle noisy data [86], a situation that is very common in agricultural measurements. Among the most popular ANNs are the Deep Neural Networks (DNNs), which utilize multiple hidden layers between input and output layers. DNNs can be unsupervised, semi-supervised, or supervised. A usual kind of DNNs are the Convolutional Neural Networks (CNNs), whose layers, unlike common neural networks, can set up neurons in three dimensions [87]. In fact, CNNs were presented as the algorithms that provide the best output in all sub-categories, with an almost 50% of the individual percentage of ANNs. As stressed in recent studies, such as that of Yang et al. [88], CNNs are receiving more and more attention because of their efficient results when it comes to detection through images’ processing.
Recurrent Neural Networks (RNNs) followed, representing approximately 10% of ANNs, with Long Short-Term Memory (LSTM) standing out. They are called “recurrent” as they carry out the same process for every element, with the previous computations determining the current output, while they have a “memory” that stores information pertaining to what has been calculated so far. RNNs can face problems concerning vanishing gradients and inability to “memorize” many sequential data. Towards addressing these issues, the cell structures of LSTM can control which part of information will be either stored in long memory or discarded, resulting in optimization of the memorizing process [51]. Moreover, Multi-Layer Perceptron (MLP), Fully Convolutional Networks (FCNs), and Radial Basis Function Networks (RBFNs) appeared to have the best performance in almost 3–5% of ANNs. Finally, ML algorithms, belonging to ANNs with low frequency, were Back-Propagation Neural Networks (BPNNs), Modular Artificial Neural Networks (MANNs), Deep Belief Networks (DBNs), Adaptive-Neuro Fuzzy Inference System (ANFIS), Subtractive Clustering Fuzzy Inference System (SCFIS), Takagi-Sugeno Fuzzy Neural Networks (TS-FNN), and Feed Forward Neural Networks (FFNNs).
The second most accurate ML model was Ensemble Learning (EL), contributing to the ML models used in agricultural systems with approximately 22.2%. EL is a concise term for methods that integrate multiple inducers for the purpose of making a decision, normally in supervised ML tasks. An inducer is an algorithm, which gets as an input a number of labeled examples and creates a model that can generalize these examples. Thus, predictions can be made for a set of new unlabeled examples. The key feature of EL is that via combining various models, the errors coming from a single inducer is likely to be compensated from other inducers. Accordingly, the prediction of the overall performance would be superior comparing to a single inducer [89]. This type of ML model was presented in all sub-categories, apart from crop quality, perhaps owing to the small number of papers belonging in this subcategory. Support Vector Machine (SVM) followed, contributing in approximately 11.5% of the studies. The strength of the SVM stems from its capability to accurately learn data patterns while showing reproducibility. Despite the fact that it can also be applied for regression applications, SVM is a commonly used methodology for classification extending across numerous data science settings [90], including agricultural research.
Decision Trees (DT) and Regression models came next with equal percentage, namely 4.7%. Both these ML models were presented in all generic categories. As far as DT are concerned, they are either regression or classification models structured in a tree-like architecture. Interestingly, handling missing data in DT is a well-established problem. By implementing DT, the dataset can be gradually organized into smaller subsets, whereas, in parallel, a tree graph is created. In particular, each tree’s node denotes a dissimilar pairwise comparison regarding a certain feature, while each branch corresponds to the result of this comparison. As regards leaf nodes, they stand for the final decision/prediction provided after following a certain rule [91,92]. As for Regression, it is used for supervised learning models intending to model a target value on the basis of independent predictors. In particular, the output can be any number based on what it predicts. Regression is typically applied for time series modeling, prediction, and defining the relationships between the variables.
Finally, the ML models, leading to optimal performance (although with lower contribution to literature), were those of Instance Based Models (IBM) (2.7%), Dimensionality Reduction (DR) (1.5%), Bayesian Models (BM) (0.9%), and Clustering (0.3%). IBM appeared only in crop, water, and livestock management, whereas BM only in crop and soil management. On the other hand, DR and Clustering appeared as the best solution only in crop management. In brief, IBM are memory-based ML models that can learn through comparison of the new instances with examples within the training database. DR can be executed both in unsupervised and supervised learning types, while it is typically carried out in advance of classification/regression so as to prevent dimensionality effects. Concerning the case of BM, they are a family of probabilistic models whose analysis is performed within the Bayesian inference framework. BM can be implemented in both classification and regression problems and belong to the broad category of supervised learning. Finally, Clustering belongs to unsupervised ML models. It contains automatically discovering of natural grouping of data [12].

4.2.2. Most Studied Crops and Animals

In this sub-section, the most examined crops and animals that were used in the ML models are discussed as a result of our searching within the four sub-categories of crop management similarly to [12]. These sub-categories refer to yield prediction, disease detection, crop recognition, and crop quality. Overall, approximately 80 different crop species were investigated. The 10 most utilized crops are summarized in Figure 9. Specifically, the remarkable interest on maize (also known as corn) can be attributed to the fact that it is cultivated in many parts across the globe as well as its versatile usage (for example, direct consumption by humans, animal feed, producing ethanol, and other biofuels). Wheat and rice follow, which are two of the most widely consumed cereal grains. According to the Food and Agriculture Organization (FAO) [93], the trade in wheat worldwide is more than the summation of all other crops. Concerning rice, it is the cereal grain with the third-highest production and constitutes the most consumed staple food in Asia [94]. The large contribution of Asian countries presented in Figure 6, like China and India, justifies the interest in this crop. In the same vein, soybeans, which are broadly distributed in East Asia, USA, Africa, and Australia [95], were presented in many studies. Finally, tomato, grape, canola/rapeseed (cultivated primarily for its oil-rich seed), potato, cotton, and barley complete the top 10 examined crops. All these species are widely cultivated all over the world. Some other indicative species, which were investigated at least five times in the present reviewed studies, were also alfalfa, citrus, sunflower, pepper, pea, apple, squash, sugarcane, and rye.
As far as livestock management is concerned, the examined animal species can be classified, in descending order of frequency, into the categories of cattle (58.5%), sheep and goats (26.8%), swine (14.6%), poultry (4.9%), and sheepdog (2.4%). As can be depicted in Figure 10, the last animal, which is historically utilized with regard to the raising of sheep, was investigated only in one study belonging to animal welfare, whereas all the other animals were examined in both categories of livestock management. In particular, the most investigated animal in both animal welfare and livestock production was cattle. Sheep and goats came next, which included nine studies for sheep and two studies for goats. Cattles are usually raised as livestock aimed at meat, milk, and hide used for leather. Similarly, sheep are raised for meat and milk as well as fleece. Finally, swine (often called domestic pigs) and poultry (for example, chicken, turkey, and duck), which are used mainly for their meat or eggs (poultry), had equal contribution from the two livestock sub-categories.

4.2.3. Most Studied Features and Technologies

As mentioned in the beginning of this study, modern agriculture has to incorporate large amounts of heterogeneous data, which have originated from a variety of sensors over large areas at various spatial scale and resolution. Subsequently, such data are used as input into ML algorithms for their iterative learning up until modeling of the process in the most effective way possible. Figure 11 shows the features and technologies that were used in the reviewed studies, separately for each category, for the sake of better comprehending the results of the analysis.
Data coming from remote sensing were the most common in the yield prediction sub-category. Remote sensing, in turn, was primarily based on data derived from satellites (40.6% of the total studies published in this sub-category) and, secondarily, from UAVs (23.2% of the total studies published in this sub-category). A remarkable observation is the rapid increase of the usage of UAVs versus satellites from the year 2018 towards 2020, as UAVs seem to be a reliable alternative that can give faster and cheaper results, usually in higher resolution and independent of the weather conditions. Therefore, UAVs allow for discriminating details of localized circumscribed regions that the satellites’ lowest resolution may miss, especially under cloudy conditions. This explosion in the use of UAV systems in agriculture is a result of the developing market of drones and sensing solutions attached to them, rendering them economically affordable. In addition, the establishment of formal regulations for UAV operations and the simplification and automatization of the operational and analysis processes had a significant contribution on the increasing popularity of these systems. Data pertaining to the weather conditions of the investigated area were also of great importance as well as soil parameters of the farm at hand. An additional way of getting the data was via in situ manual measurements, involving measurements such as crop height, plant growth, and crop maturity. Finally, data concerning topographic, irrigation, and fertilization aspects were presented with approximately equal frequency.
As far as disease detection is concerned, Red-Green-Blue (RGB) images appear to be the most usual input data for the ML algorithms (in 62% of the publications). Normally, deep learning methods like CNNs are implemented with the intention of training a classifier to discriminate images depicting healthy leaves, for example, from infected ones. CNNs use some particular operations to transform the RGB images so that the desired features are enhanced. Subsequently, higher weights are given to the images having the most suitable features. This characteristic constitutes a significant advantage of CNNs as compared to other ML algorithms, when it comes to image classification [79]. The second most common input data came from either multispectral or hyperspectral measurements originated from spectroradiometers, UAVs, and satellites. Concerning the investigated diseases, fungal diseases were the most common ones with diseases from bacteria following, as is illustrated in Figure 12a. This kind of disease can cause major problems in agriculture with detrimental economic consequences [96]. Other examined origins of crop diseases were, in descending order of frequency, pests, viruses, toxicity, and deficiencies.
Images were also the most used input data for weed detection purposes. These images were RGB images that originated mainly from in situ measurements as well as from UGVs and UAVs and, secondarily, multispectral images from the aforementioned sources. Finally, other parameters that were observed, although with lower frequency, were satellite multispectral images, mainly due to the considerably low resolution they provide, video recordings, and hyperspectral and greyscale images. Concerning crop recognition, the majority of the studies used data coming mostly from satellites and, secondarily, from in situ manual measurements. This is attributed to the fact that most of the studies in this category concern crop classification, a sector where satellite imaging is the most widely used data source owing to its potential for analysis of time series of extremely large surfaces of cultivated land. Laboratory measurements followed, while RGB and greyscale images as well as hyperspectral and multispectral measurements from UAVs were observed with lower incidence.
The input data pertaining to crop quality consisted mainly of RGB images, while X-ray images were also utilized (for seed germination monitoring). Additionally, quality parameters, such as color, mass, and flesh firmness, were used. There were also two studies using spectral data either from satellites or spectroradiometers. In general, the studies belonging in this sub-category dealt with either crop quality (80%) or seed germination potential (20%) (Figure 12b). The latter refers to the seed quality assessment that is essential for the seed production industry. Two studies were found about germination that both combined X-ray images analysis and ML.
Concerning soil management, various soil properties were taken into account in 65.7% of the studies. These properties included salinity, organic matter content, and electrical conductivity of soil and soil organic carbon. Usage of weather data was also very common (in 48.6% of the studies), while topographic and data pertaining to the soil moisture content (namely the ratio of the water mass over the dry soil) and crop properties were presented with lower frequency. Additionally, remote sensing, including satellite and UAV multispectral and hyperspectral data, as well as proximal sensing, to a lesser extent, were very frequent choices (in 40% of the studies). Finally, properties associated with soil temperature, land type, land cover, root microbial dynamics, and groundwater salinity make up the rest of data, which are labeled as “other” in the corresponding graph of Figure 11.
In water management, weather data stood for the most common input data (appeared in the 75% of the studies), with ET being used in the vast majority of them. In many cases, accurate estimation of ET (the summation of the transpiration via the plant canopy and the evaporation from plant, soil, and open water surface) is among the most central elements of hydrologic cycle for optimal management of water resources [97]. Data from remote sensors and measurements of soil water content were also broadly used in this category. Soil water availability has a central impact on crops’ root growth by affecting soil aeration and nutrient availability [98]. Stem water potential, appearing in three studies, is actually a measure of water tension within the xylem of the plant, therefore functioning as an indicator of the crop’s water status. Furthermore, in situ measurements, soil, and other parameters related to cumulative water infiltration, soil and water quality, field topography, and crop yield were also used, as can be seen in Figure 11.
Finally, in what concerns livestock management, motion capture sensors, including accelerometers, gyroscopes, and pedometers, were the most common devices giving information about the daily activities of animals. This kind of sensors was used solely in the studies investigating animal welfare. Images, audio, and video recordings came next, however, appearing in both animal welfare and livestock production sub-categories. Physical and growth characteristics followed, with slightly less incidence, by appearing mainly in livestock production sub-category. These characteristics included the animal’s weight, gender, age, metabolites, biometric traits, backfat and muscle thickness, and heat stress. The final characteristic may have detrimental consequences in livestock health and product quality [99], while through the measurement of backfat and muscle thickness, estimations of the carcass lean yield can be made [100].

5. Discussion and Main Conclusions

The present systematic review study deals with ML in agriculture, an ever-increasing topic worldwide. To that end, a comprehensive analysis of the present status was conducted concerning the four generic categories that had been identified in the previous review by Liakos et al. [12]. These categories pertain to crop, water, soil, and livestock management. Thus, by reviewing the relative literature of the last three years (2018–2020), several aspects were analyzed on the basis of an integrated approach. In summary, the following main conclusions can be drawn:
  • The majority of the journal papers focused on crop management, whereas the other three generic categories contributed almost with equal percentage. Considering the review paper of [12] as a reference study, it can be deduced that the above picture remains, more or less, the same, with the only difference being the decrease of the percentage of the articles regarding livestock from 19% to 12% in favor of those referring to crop management. Nonetheless, this reveals just one side of the coin. Taking into account the tremendous increase in the number of relative papers published within the last three years (in particular, 40 articles were identified in [12] comparing to the 338 of the present literature survey), approximately 400% more publications were found on livestock management. Another important finding was the increasing research interest on crop recognition.
  • Several ML algorithms have been developed for the purpose of handling the heterogeneous data coming from agricultural fields. These algorithms can be classified in families of ML models. Similar to [12], the most efficient ML models proved to be ANNs. Nevertheless, in contrast to [12], the interest also been shifted towards EL, which can combine the predictions that originated from more than one model. SVM completes the group with the three most accurate ML models in agriculture, due to some advantages, such as its high performance when it works with image data [101].
  • As far as the most investigated crops are concerned, mainly maize and, secondarily, wheat, rice, and soybean were widely studied by using ML. In livestock management, cattle along with sheep and goats stood out constituting almost 85% of the studies. Comparing to [12], more species have been included, while wheat and rice as well as cattle, remain important specimens for ML applications.
  • A very important result of the present review study was the demonstration of the input data used in the ML algorithms and the corresponding sensors. RGB images constituted the most common choice, thus, justifying the broad usage of CNNs due to their ability to handle this type of data more efficiently. Moreover, a wide range of parameters pertaining to weather as well as soil, water, and crop quality was used. The most common means of acquiring measurements for ML applications was remote sensing, including imaging from satellites, UAVs and UGVs, while in situ and laboratory measurements were also used. As highlighted above, UAVs are constantly gaining ground against satellites mainly because of their flexibility and ability to provide images with high resolution under any weather conditions. Satellites, on the other hand, can supply time-series over large areas [102]. Finally, animal welfare-related studies used mainly devices such as accelerometers for activity recognition, whereas those ones referring to livestock production utilized primary physical and growth characteristics of the animal.
As can be inferred from the geographical distribution (illustrated in Figure 6) in tandem with the broad spectrum of research fields, ML applications for facilitating various aspects of management in the agricultural sector is an important issue on an international scale. As a matter of fact, its versatile nature favors convergence research. Convergence research is a relatively recently introduced approach that is based on shared knowledge between different research fields and can have a positive impact on the society. This can refer to several aspects, including improvement of the environmental footprint and assuring human’s health. Towards this direction, ML in agriculture has a considerable potential to create value.
Another noteworthy finding of the present analysis is the capturing of the increasing interest on topics concerning ML analyses in agricultural applications. More specifically, as can be shown in Figure 13, an approximately 26% increase was presented in the total number of the relevant studies, if a comparison is made between 2018 and 2019. The next year (i.e., 2020), the corresponding increase jumped to 109% against 2019 findings; thus, resulting in an overall 164% rise comparing with 2018. The accelerating rate of the research interest on ML in agriculture is a consequence of various factors, following the considerable advancements of ICT systems in agriculture. Moreover, there exists a vital need for increasing the efficiency of agricultural practices while reducing the environmental burden. This calls for both reliable measurements and handling of large volumes of data as a means of providing a wide overview of the processes taking place in agriculture. The currently observed technological outbreak has a great potential to strengthen agriculture in the direction of enhancing food security and responding to the rising consumers’ demands.
In a nutshell, ICT in combination with ML, seem to constitute one of our best hopes to meet the emerging challenges. Taking into account the rate of today’s data accumulation along with the advancement of various technologies, farms will certainly need to advance their management practices by adopting Decision Support Systems (DSSs) tailored to the needs of each cultivation system. These DSSs use algorithms, which have the ability to work on a wider set of cases by considering a vast amount of data and parameters that the farmers would be impossible to handle. However, the majority of ICT necessitates upfront costs to be paid, namely the high infrastructure investment costs that frequently prevent farmers from adopting these technologies. This is going to be a pressing issue, mainly in developing economies, where agriculture is an essential economic factor. Nevertheless, having a tangible impact is a long-haul game. A different mentality is required by all stakeholders so as to learn new skills, be aware of the potential profits of handling big data, and assert sufficient funding. Overall, considering the constantly increasing recognition of the value of artificial intelligence in agriculture, ML will definitely become a behind-the-scenes enabler for the establishment of a sustainable and more productive agriculture. It is anticipated that the present systematic effort is going to constitute a beneficial guide to researchers, manufacturers, engineers, ICT system developers, policymakers, and farmers and, consequently, contribute towards a more systematic research on ML in agriculture.

Author Contributions

Conceptualization, D.B.; methodology, L.B., G.D., R.B., D.K. and A.C.T.; investigation, L.B. and G.D.; writing—original draft preparation, L.B. and A.C.T.; writing—review and editing, L.B., G.D., D.K., A.C.T., R.B. and D.B.; visualization, L.B.; supervision, D.B. All authors have read and agreed to the published version of the manuscript.


This work has been partly supported by the Project “BioCircular: Bio-production System for Circular Precision Farming” (project code: T1EDK- 03987) co-financed by the European Union and the Greek national funds through the Operational Programme Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

In this section, the reviewed articles are summarized within the corresponding Tables as described in Table 2.
Table A1. Crop Management: Yield Prediction.
Table A1. Crop Management: Yield Prediction.
RefCropInput DataFunctionalityModels/AlgorithmsBest Output
[103]CoffeeWeather data, soil fertilityPrediction of Robusta coffee yield by using various soil fertility propertiesELM, RF, MLRELM: Model with SOM, K, S:
RMSE = 496.35 kgha−1, MAE = 326.40 kgha−1
[104]MaizeWeather and satellite spectral dataSilage maize yield estimation via Landsat 8 OLI dataBRT, RFR, SVR, GPRBRT: R = 0.89, RMSE = 4.66
[105]MaizeSoil properties, topographic, multispectral aerial imagesPrediction of corn yield and soil properties (SOM, CEC, Mg, K, pH)RF, ANN, SVM, GBM, Cubist(1) Corn yield: RF (R2 = 0.53); (2) SOM: NN (R2 = 0.64); (3) CEC: NN (R2 = 0.67); (4) K: SVM (R2 = 0.21); (5) Mg: SVM (R2 = 0.22); (6) pH: GBM (R2 = 0.15)
[106]CottonSatellite spectral dataCotton yield estimationANN(1) 2013: Yield vs. CI (R = −0.2–0.60), best ANN (R = 0.68); (2) 2014: Yield vs. CI (R = −0.79–0.84), best ANN (R = 0.86)
[107]AppleRGB imagesDetection and estimation of the number of apples in canopy imagesMLRYield relative error = −10–13%,
Yield relative error STD = 28% of average tree yield
[108]MaizeCrop data—CERES model, satellite spectral dataForecasting spring maize yield from Landsat-8 imagesSVM, RF, DT, LDA, KNNRS: SVM: Acc = 97%, RMSE = 397 kgha−1
[109]Maize, soybeanSatellite spectral dataEstimation of corn and soybean yield via Landsat and SPOT imagesMLR, ANNR2 values: (1) Maize: ANN: 0.92, (2) Soybean: ANN: 0.90
[110]TurmericSoil fertility, weather dataForecasting oil yield produced from turmeric rhizomesANNΜultilayer-feed-forward NN with 12 nodes: R2 = 0.88
[111]SunflowerPlant height, SPADPrediction of sunflower seed yieldPLSR, ANN(1) ANN: RMSE = 0.66 tha−1, R2 = 0.86; (2) PLSR: RMSE = 0.93 tha−1, R2 = 0.69
[112]PistachioIrrigation, soil characteristicsEstimation of pistachio yield in orchardsMLR, ANNAcc values: ANN: 90%, MLR: 28%
[113]RiceWeather data, irrigation, planting area, fertilizationEvaluation of feature subsets for prediction of paddy crop yieldANN, SVR, KNN, RFForward Feature Selection:
RF: RMSE = 0.085, MAE = 0.055, R = 0.93
[114]PotatoSatellite spectral dataPrediction of potato yield via Sentinel 2 satellite dataMLR, RQL, LB, SVM, RF, MARS, KNN, ANN(1) Reduced dataset: LB: MAE = 8.95%, R2 = 0.89; (2) No feature selection: SVM: MAE = 8.64%, R2 = 0.93; (3) 1–2 months prior to harvest: RF: MAE = 8.71%, R2 = 0.89
[115]WheatSatellite spectral dataPrediction of wheat yieldSVM, RF, ANNR2 values: (1) SVM: 0.74; (2) RF: 0.68; (3) ANN: 0.68
[116]Soybean, MaizeHydrological, weather and satellite spectral dataPrediction of soybean and corn yieldsDNN, RF, SVM, MARS, ERT, ANNDNN (1) Corn: 21–33% more accurate (2) Soybean: 17–22% more accurate
[117]Wheat, barleyMultispectral images from UAVPrediction of barley and wheat yieldsCNN(1) Early growth phase(<25%):
MAE = 484.3 kgha−1, MAPE = 8.8%; (2) Later growth phase(>25%): MAE = 484.3 kgha−1, MAPE = 8.8%
[118]StrawberryMultispectral images from UAVDetection and counting of strawberry species for yield predictionCNNFaster RCNN: (1) Detection: MaP = 0.83 (at 2 m), MaP = 0.72 (at 3 m); (2) Count: Acc = 84.1%, Average occlusion = 13.5%
[119]RiceWeather data, irrigation, planting area, fertilizationPrediction of paddy fields yieldANN, MLR, SVR, KNN, RFANN-MLR: R = 0.99, RMSE = 0.051, MAE = 0.041
[120]SoybeanWeather and satellite spectral dataPrediction of soybean yield in 15 states of USACNN, LSTM2011–2015: End-of-season
RMSE = 329.53 kgha−1, R2 = 0.78
[121]MaizeSatellite spectral dataPrediction of maize yieldMLR, RF, SVMRF: (1) yield: R2 = 0.6; (2) GNDVI: R2 = 0.48;
Best monitoring period:
Crop age = 105–135 days
[122]MangoMultispectral data from UGVEstimation of mango maturity level by simulating imaging devices of optical filtersSVMEstimation of dry matter by using a 4-sensor device with 4 filters: R2 = 0.69
[123]Rapeseed, barley, wheatEC, STI, gamma radiometrics and weather dataForecasting crop yieldRFRMSE = 0.36–0.42 t/ha, Lin’s CCC = 0.89–0.92
[53]MaizeGenetic information of hybrids, soil and weather dataPrediction of maize yieldDNN(1) With predicted weather data: RMSE = 12% of average yield, 50% of STD; (2) Using ideal weather data: RMSE = 11% of average yield, 46% of STD
[124]RiceRGB leaf imagesPrediction of nutrient deficiencies (P, N, K) in image leaves from paddy fieldsANNAcc = 77%
[125]RiceRGB and multispectral images from UAVEstimation of rice grain yieldCNNR2 values: (1) Only RGB images: 0.424–0.499; (2) RGB and multispectral images: 0.464–0.511
[126]MaizeSatellite spectral data, crop modeling dataEstimation of end-of-season and early maize yieldRF(1) Early maize yield: R2 = 0.53, RMSE = 271 kgha−1, MAE = 202 kgha−1; (2) End-of-season maize yield: R2 = 0.59, RMSE = 258 kg ha−1, MAE = 201 kgha−1
[127]PotatoSoil parameters and tillage treatmentsForecasting of organic potato yieldANN, MLR(1) MLR: R2 = 0.894, RMSE = 0.431, MAE = 0.327; (2) ANN: R2 = 0.95, RMSE = 0.431, MAE = 0.327
[128]MaizeSimulations data, weather and soil dataPrediction of crop yield based on gridded crop meta-modelsRF, XGBoost(1) XGBoost: (a) growing season climate: R2 = 0.91, MAE = 0.74, (b) annual climate: R2 = 0.92, MAE = 0.66: (2) RF: (a) growing season climate: R2 = 0.94, MAE = 0.71, (b) annual climate: R2 = 0.95, MAE = 0.58
[129]SoybeanSatellite spectral data, precipitation and daytimeForecasting soybean yieldRF, multivariate OLS, LSTM(1) DOY 16: OLS: MAE = 0.42 Mgha−1; (2) DOY 32: LSTM: MAE = 0.42 Mgha−1; (3) DOY 48: LSTM: MAE = 0.25 Mgha−1; (4) DOY 64: LSTM: MAE = 0.24 Mgha−1
[130]PotatoTopography, soil EC, soil chemistry and multispectral data from ground based sensorsPotato tuber yield prediction via ground based proximal sensingLR, KNN, EN, SVRBest models: (1) SVR: 2017: (a) New Brunswick: RMSE = 5.97 tha−1, (b) Prince Edward Island: RMSE = 6.60 tha−1; (2) 2018: (a) New Brunswick RMSE = 4.62 tha−1, (b) Prince Edward Island: RMSE = 6.17 tha−1
[131]Rice, maize,
millet, ragi
Weather dataPrediction of various kharif crops yieldMANN, SVROverall RMSE = 79.85%
[132]WheatSoil, weather, and satellite spectral dataWinter wheat prediction from four mid-season timingsRF, GPR, SVM, ANN, KNN, DT, BT(1) RF: R2 = 0.81, RMSE = 910–920 kgha−1, MAE = 740 kgha−1; (2) GPR: R2 = 0.78, RMSE = 920–960 kgha−1, MAE = 735–767 kgha−1
[133]MaizeData derived from various cropping systemsMaize grain yield prediction from CA and conventional cropping systems LDA, MLR, GNB, KNN, CART, SVMBest results: LDA: Acc = 0.61, Precision = 0.59, Recall = 0.59, F1-score = 0.59
[134]SoybeanMultispectral, RGB and thermal images from UAVEstimation of soybean grain yieldDNN, PLSR, RFR, SVRDNN: (1) Intermediate-level feature fusion: R2 = 0.720, Relative RMSE = 15.9%; (2) input-level feature fusion: R2 = 0.691,
Relative RMSE = 16.8%
[135]Soybean, MaizeWeather data and soil dataSoybean and corn yield forecastingCNN-RNN, RF, LASSO, DNNCNN-RNN: RMSE values (bushels/acre): (1) Soybean: 2016: 4.15, 2017: 4.32, 2018: 4.91; (2) Maize: 2016: 16.48, 2017: 15.74, 2018: 17.64
[136]GrapeMultispectral images from UAVEstimation of vineyard final yieldMLP(1) Only NDVI: RMSE = 1.2 kg/vine, Relative error = 28.7%; (2) Both NDVI ANF VFC: RMSE = 0.9 kg/vine,
Relative error = 21.8%
[137]RiceSatellite spectral dataPrediction of rice crop yieldRF, SVM(1) HD NDVI: RF: RMSE = 11.2%,
MAE = 9.1%, SVM: RMSE = 8.7%, MAE = 5.6%; (2) HDM NDVI: RF: RMSE = 11.3%, MAE = 9.2%, SVM: RMSE = 8.7%, MAE = 5.6%
[138]MaizeFertilization, planting density, soil EC, satellite spectral dataPrediction of corn yield response to nitrogen and seed rate managementCNNAverage value for 9 fields in the USA: RMSE = 0.7
[139]SugarcaneMonthly precipitation data Forecasting of sugarcane yield RNNRMSE = 0.31 tha−1, MAE = 0.39 tha−1, MAPE = 5.18%
[140]WheatSatellite spectral and weather dataEstimation of wheat yieldSVR, RF, Cubist, XGBoost, MLP, GPR, KNN, MARSSVR: RMSE = 0.55 tha−1, R2 = 0.77
[141]Maize, SoybeanSatellite spectral dataForecasting of maize and soybean yield MLR, ANNANN: (1) Corn: RMSE = 4.83–8.41, R = 0.91–0.99; (2) Soybean: RMSE = 5.18–7.77, R = 0.79–0.99
[142]MaizeSatellite spectral and weather dataPrediction of maize yield under severe weather conditions DNN(1) Drought cases: R = 0.954; (2) Heatwave cases: R = 0.887–0.914
[143]RiceWeather dataPaddy yield predictionANNR = 0.78–1.00,
MSE = 0.040–0.204
[144]MaizePlant population, soil and weather dataMaize yield forecasting in 3 US states of Corn BeltXGBoost, RF, LASSO, GBM, WELWEL: RMSE = 1.138 kgha−1
[145]MaizeSatellite spectral and weather dataEstimation of maize yield DLSR2 = 0.76, RMSE = 0.038 tha−1
[146]Various cropsSatellite spectral and weather dataPrediction of autumn crops yieldSVR, RF, DNNRMSE values (×104 tons)
SVR = 501.98; RF = 477.45; DNN = 253.74
[147]WheatMultispectral images from UAVGrowth monitoring and yield prediction of wheat in key growth stagesLR, SMLR, PLSR, ANN, RFBest results: RF:
R2 = 0.78, RMSE = 0.103
[148]CottonTopographic, weather, soil and satellite spectral dataWithin-field yield predictionRF, GBBest results: RF: RMSE = 0.20 tha−1, CCC = 0.50–0.66
[149]CottonSatellite spectral dataYield prediction RF, CARTRF: RMSE = 62.77 Kg ha−1, MAPE = 0.32
[150]RiceMultispectral images from UAVPrediction of rice grain yieldRFRMSE = 62.77 Kg ha−1, MAPE = 0.32
[151]SoybeanMultispectral images from UAVYield estimation in soybeanMLPR = 0.92
[152]PotatoWeather, irrigation, and satellite spectral dataForecasting of yield in potato fields at municipal levelRF, SVM, GLM(1) winter cycle: R2 = 0.757, %RMSE = 18.9; (2) summer cycle; R2 = 0.858, %RMSE = 14.9
[153]SugarcaneSatellite spectral dataPrediction of sugarcane yieldMLRR2 = 0.92–0.99
[154]CottonMultispectral images from UAVEstimation of cotton yieldANN, SVR, RFRANN: R2 = 0.9
[155]RiceWeather and soil dataPrediction of rice yields from Blockchain nodesRF, MLR, GBR, DTRRF: R2 = 0.941, %RMSE = 0.62, MAE = 0.72
[156]MaizeMultispectral images from UAVPrediction of maize yield at specific phenological stagesGBStage V10: R2 = 0.90; Stage VT: R2 = 0.93
[157]WheatSatellite spectral and weather data, soil hydraulic propertiesForecasting of wheat yieldRF, MLRRF: 1 month before harvest: R = 0.85, RMSE = 0.70 tha−1, ROC = 0.90
[158]MaizeSoil and weather dataEstimation of maize yield with publicly available dataLSTM, LASSO, RF, SVR, AdaBoostLSTM: MAE = 0.83 (buac−1), MAPE = 0.48%
[159]RiceSoil and weather dataFinding optimal features gathering for forecasting paddy yieldRF, DT, GBMRF: MSE = 0.07, R2 = 0.67;
[160]AlfalfaHyperspectral data from UAVIn-season alfalfa yield forecastCombination of RF,
R2 = 0.874
[161]MaizeMultispectral images from UAVYield prediction of maizeBPNN, SVM, RF, ELMSVM: RMSE = 1.099, MAE = 0.886
[162]MenthaSatellite spectral data, field inventory data (soil, plant height, biomass)Mentha crop biomass forecastingMLPR2 = 0.762, RMSE = 2.74 th−1
[163]WheatMultispectral images from UAVPrediction of wheat grain yieldLR, RF, SVM, ANNLR: RMSE = 972 kgha−1, R2 = 0.62
[164]MaizeMultispectral images from UAVPrediction of maize yieldRF, RF+R, RF+BAG, SVM, LR, KNN, ANNRF: R = 0.78, MAE = 853.11 kgha−1
[165]PotatoHyperspectral data from UAVYield prediction at two growth stagesRF, PLSRRF: R2 = 0.63, MAE = 853.11 kgha−1
[166]CarrotSatellite spectral dataCarrot yield MappingRFR2 = 0.82, RMSE = 2.64 Mgha−1; MAE = 1.74 Mgha−1
[167]Soybeanmultispectral images from UAVPredicting yieldDTRMSE = 196 kgha−1
[168]WheatSatellite spectral, soil and weather dataWinter wheat yield prediction at a regional levelCombination of LSTM and CNNR2 = 0.75, RMSE = 732 kgha−1;
[169]PotatoHyperspectral data from UAVYield prediction at two growth stagesRF, PLSRR2 values: RF: 0.63; PLSR: 0.81
[170]WheatSatellite spectral and weather dataWinter yield prediction in the Conterminous United StatesOLS, LASSO, SVM, RF, AdaBoost, DNNAdaBoost: R2 = 0.86, RMSE = 0.51 tha−1, MAE = 0.39 tha−1
Acc: Accuracy: CA: Conservation Agriculture; CI: Crop Indices; CEC: Cation Exchange Capacity; CCC: Concordance Correlation Coefficient; DOY: Day Of Year; EC: Electrical Conductivity; HD: Heading Date; HDM: Heading Date to Maturity; K: Potassium; Mg: Magnesium; N: Nitrogen; OLI: Operational Land Imager; P: Phosphorus; RGB: Red-Green-Blue; S: Sulphur; SOM: Soil Organic Matter; SPAD: Soil and Plant Analyzer Development; STI: Soil Texture Information; STD: Standard Deviation; UAV: Unmanned Aerial Vehicle; UGV: Unmanned Ground Vehicle.
Table A2. Crop Management: Disease Detection.
Table A2. Crop Management: Disease Detection.
RefCropInput DataFunctionalityModels/AlgorithmsBest Output
[171]Various cropsRGB imagesDetection and diagnosis of plant diseasesCNNAcc = 99.53%
[172]MelonFluorescence, thermal imagesDetection of Dickeya dadantii in melon plantsLR, SVM, ANNANN: Whole leaves: Acc = 96%; F1 score = 0.99
[173]TomatoRGB imagesRecognition of 10 plant diseases and pests in tomato plantsCNNRecognition rate = 96%
[174]AvocandoHyperspectral imagesDetection of nitrogen and iron deficiencies and laurel wilt disease in avocandoDT, MLPMLP: Detection at early stage: Acc = 100%
[175]MaizeRGB imagesExamination of nine factors affecting disease detection in maize fieldsCNNAcc values: (1) Original dataset: 76%; Background removed: 79%; (2) Subdivided (full): 87%; (3) Subdivided (reduced): 81%
[176]Milk thistleSpectral measurements form spectroradiometerIdentification of Microbotryum silybum in milk thistle plantsMLP-ARDAcc = 90.32%
[177]TomatoSpectral measurements form spectroradiometerDetection of leaf diseases (target, bacterial spots and late blight) in tomatoKNNAcc values: (1) Healthy leaves: 100%, (2) Asymptomatic: 100%, (3) Early stage: 97.8%, (4) Late stage: 100%
[178]MaizeRGB imagesIdentification of eight types of leaf diseases in maizeCNN(1) GoogLeNet:
Acc = 98.9%; (2) Cifar10: Acc = 98.8%
[179]Various cropsRGB imagesIdentification of six plant leaf diseasesRBFN(1) Early blight: Acc = 0.8914; (2) Common rusts: Acc = 0.8871
[180]CitrusRGB imagesDetection and classification of citrus diseasesSVMAcc values: 1st dataset: 97%; 1st and 2nd dataset: 89%; 3rd dataset: 90.4%
[181]GrapeMultispectral images from UAVIdentification of infected areasCNN(1) Color space YUV: Acc = 95.84%; (2) Color space YUV and ExGR: Acc = 95.92%
[182]SoybeeanRGB imagesDetection and classification of three leaf diseases in soybeansSVM(1) Healthy: Acc = 82%; (2) Downy mildew: Acc = 79%; (3) Frog eye: Acc = 95.9%; (4) Septoria leaf blight: Acc = 90%
[183]MilletRGB imagesIdentification of fungal disease (mildew) in pearl milletCNNAcc = 95.00%, Precision = 90.50%, Recall = 94.50%, F1 score = 91.75%
[184]MaizeRGB images from UAVDetection of northern leaf blight in maizeCNNAcc = 95.1%
[185]WheatRGB images from UAVClassification of helminthosporium leaf blotch in wheatCNNAcc = 91.43%,
[186]AvocadoRGB images, multispectral imagesDetection of laurel wilt disease in healthy and stressed avocado plants in early stageMLP, KNNHealthy vs. Nitrogen deficiency using 6 bands images: (1) MLP: Acc = 98%; (2) KNN: Acc = 86%
[187]BasilRGB imagesIdentification and classification of five types of leave diseases in four kinds of basil leavesDT, RF, SVM, AdaBoost, GLM, ANN, NB, KNN, LDARF: Acc = 98.4%
[188]Various cropsRGB imagesIdentification of several diseases on leavesCNNAcc values: (1) Healthy: 89%; (2) Mildly diseased: 31%; (3) Moderately diseased: 87%; (4) Severely diseased: 94%
[189]TeaRGB images from UAVIdentification of tea red Scab, tea leaf blight and tea red leaf spot diseases in tea leavesSVM, DT, RF, CNNCNN: Acc values: (1) tea red Scab: 0.7; (2) tea leaf blight: 1.0; (3)tea red leaf spot: 1.0
[190]WheatHyperspectral images from UAVDetection of yellow rust in wheat plotsCNNAcc = 0.85
[191]GrapeRGB imagesDetection of grapevine yellows in red grapesCNNSensitivity = 98.96%
Specificity = 99.40%
[192]MaizeRGB images from UAVDetection of northern leaf blight in maizeCNNAcc = 0.9979,
F1 score = 0.7153
[193]Sugar beetRGB imagesDetection and classification of diseased leaf spots in sugar beetCNNAcc = 95.48%
[194]Various cropsRGB imagesIdentification of various plant leaf diseasesCNNAcc = 96.46%
[195]StrawberryRGB imagesDetection of powdery mildew in strawberry leavesLDA(1) Artificial lighting conditions:
recall = 95.26%, precision = 95.45%, F1 score = 95.37%; (2) Natural lighting conditions: recall = 81.54%, precision = 72%, F1 score = 75.95%
[196]Various different cropsRGB imagesDetection of diseased plantsDLAcc = 93.67%
[197]CitrusHyperspectral images from UAVDetection of canker disease on leaves and immature fruitsRBFN,
RBFN: Acc values: (a) asymptomatic: 94%, (b) early stage: 96%, (c) late stage: 100%
[198]GrapeRGB imagesDetection of diseased vine on leavesSVMAcc = 95%
[199]WheatRGB imagesIdentification of three leaf diseases in wheatCNNAcc values: (1) Septoria: 100%; (2) Tan Spot: 99.32%; (3) Rust: 99.29%
[200]GrapeSpectral measurements form spectroradiometerClassification of Flavescence dorée disease in grapevinesSVM, LDASVM: Acc = 96%
[201]PapayaRGB imagesRecognition of five papaya diseasesSVMAcc = 90%, Precision = 85.6%
[202]RiceRGB imagesRecognition and classification of rice infected leavesKNN, ANNANN: Acc = 90%, Recall = 88%
[203]TomatoHyperspectral images from UAVDetection of bacterial spot and target spot on tomato leavesMLP, STDAMLP: Acc values: (a) bacterial spot: 98%, (b) target spot: 97%
[204]SquashHyperspectral images from UAV and laboratory measurementsClassification of powdery mildew in squashRBFN Acc values: (1) Laboratory: Asymptomatic: 82%, Late stage: 99%; (2) Field conditions: Early stage: 89%, Late disease stage: 96%
[205]TomatoHyperspectral images from UAV and laboratory measurementsDetection of bacterial spot and target spot on tomato leavesRBFN, STDA Field conditions: Acc values: (a) Healthy vs. BS: 98%, (b) Healthy vs. TS: 96%, (c) Healthy vs. TYLC: 100%
[206]TomatoRGB imagesIdentification of various diseases in tomatoCNNAcc values: (1) PV dataset: 98.4%; (2) 2nd dataset: 98.7%; (3) Field data: 86.27%
[79]WalnutRGB imagesIdentification of anthracnose infected leavesCNNAcc values: (1) RGB: 95.97%; (2) Grayscale: 92.47%; (3) Fast Fourier: 92.94%
[207]Various cropsRGB imagesClassification of infected leavesDBNAcc = 0.877, Sensitivity = 0.862, Specificity = 0.877
[208]GrapeMultispectral images from UAVDetection of Mildew disease in vineyardsCNNAcc values: (1) Grapevine-level: 92%; (2) Leaf level: 87%
[209]RiceRGB images, videosVideo detection of brown spot, stem borer and sheath blight in riceCNN(1) Brown spot: Recall = 75.0%,
Precision = 90.0%; (2) Stem borer:
Recall = 45.5%, Precision = 71.4%;
(3) Sheath blight: Recall = 74.1%,
Precision = 90.9%
[210]CassavaRGB imagesDetection and classification of diseased leaves of fine-grain cassavaCNNAcc = 93%
[211]BananaSatellite spectral data, Multispectral images from UAV, RGB images from UAVDetection of banana diseases in different African landscapesRF, SVMRF: Acc = 97%, omissions error = 10%; commission error = 10%; Kappa coefficient = 0.96
[212]TomatoRGB imagesDetection of early blight, leaf mold and late blight on tomato leavesCNNAcc = 98%
[213]PepperSpectral reflectance at 350–2500 nmDetection of fusarium disease in pepper leavesANN, NB, KNNΚNN: Average success rate = 100%
[214]TomatoSpectral measurements form spectroradiometerDetection of fusarium disease on pepper leavesCNNAcc = 98.6%
[215]CitrusMultispectral images from UAVDetection of citrus greening in citrus orchardsSVM, KNN, MLR, NB, AdaBoost, ANNAdaBoost: Acc = 100%
[216]SoybeanRGB imagesPrediction of charcoal rot disease in soybeanGBTSensitivity = 96.25%, specificity = 97.33%
[217]WheatRGB images from UAVDetection of wheat lodging RF, CNN, SVMCNN: Acc = 93%
[218]TomatoWeather dataPrediction of powdery mildew disease in tomato plantsELMAcc = 89.19%, AUC = 88.57%
[219]SoybeanRGB imagesDiagnosis of soybean leaf diseasesCNNAcc = 98.14%
[220]PotatoRGB imagesIdentification of early and late blight diseaseNB, KNN, SVMSVM: Average Acc = 99.67%
[221]Various cropsRGB imagesQuantification of uncertainty in detection of plant diseasesBDLMean softmax probability values: (1) Healthy: 0.68; (2) Non-Healthy: 0.72;
[222]CoffeeSatellite spectral dataIdentification of coffee berry necrosis via satellite imageryMLP, RF, NBNB: Acc = 0.534
[223]TomatoRGB imagesRecognition of blight, powdery mildew, leaf mold fungus and tobacco mosaic virus diseasesCNNFaster RCNN:
mAP = 97.01%
[224]MaizeRGB imagesDiagnosis of northern leaf blight, gray leaf spot, and common rust diseases CNNAcc = 98.2%; macro average precision = 0.98
[225]GrapeRGB imagesDetection of black measles, black rot, leaf blight and mites on leavesCNNmAP = 81.1%
[226]GrapeWeather data, expert input (disease incidence form visual inspection)Forecasting downy mildew in vineyardsGLM, LASSO, RF, GBGB: AUC = 0.85
[227]MaizeRGB imagesDetection of northern leaf blight in maizeCNNmAP = 91.83%
[228]OnionRGB imagesDetection of downy mildew symptoms in onions field imagesWSL[email protected] = 74.1–87.2%
[229]CoffeeRGB imagesDetection of coffee leaf rust via remote sensing and wireless sensor networksCNNF1 score = 0.775, p-value = 0.231
[230]TomatoWeather data, multispectral images captured from UAVDetection of late blight diseaseCNNAcc values: AlexNet: (1) Transfer learning: 89.69%; (2) Feature extraction: 93.4%,
[231]RiceRGB imagesDetection of brown rice planthopperCNNAverage recall rate = 81.92%, average Acc = 94.64%
[232]GrapeUAV multispectral images, depth map informationDetection of vine diseasesCNNVddNet: Accuracy = 93.72%
[233]AppleRGB imagesIdentification of apple leaf diseases (S, FS, CR)CNNImproved VGG16: Acc = 99.40%(H), 98.04% (S), 98.33%(FS), 100%(CR)
[234]CottonUAV multispectral imagesDisease classification of cotton root rotKM, SVMKM: Acc = 88.39%, Kappa = 0.7198
Acc: Accuracy; AUC: Area Under Curve; CR: Cedar Rust; ExGR: Excess Green Minus Excess Red; FS: Frogeye Spot; H: Healthy; mAP: mean Average Precision; RGB: Red-Green-Blue; S: Scab; TYLC: Tomato Yellow Leaf Curl; UAV: Unmanned Aerial Vehicle; VddNet: Vine Disease Detection Network.
Table A3. Crop Management: Weed Detection.
Table A3. Crop Management: Weed Detection.
RefInput DataFunctionalityModels/AlgorithmsBest Output
[235]RGB imagesClassification of thinleaf (monocots), broa leaf (dicots) weedsAdaBoost with NBAcc values: (1) Original dataset: 98.40%; (2) expanded dataset: 94.72%
[236]RGB images from UAVDetection of weeds in bean, spinach fieldsCNNAcc values: (1) Bean field: 88.73%;
(2) Spinach field: 94.34%
[237]RGB imagesDetection of four weed species in sugar beet fieldsSVN, ANNOverall Acc: SVM: 95.00%; Weed classification: SVM: 93.33%; Sugar beet plants: SVM: 96.67%
[238]RGB images from UAV, multispectral imagesDetection of Gramineae weed in rice fieldsANNBest system:
80% < M/MGT < 108%, 70% < MP < 85%
[239]RGB imagesClassification of crops (three species) and weeds (nine species)CNNAverage Acc: 98.21±0.55%
[240]Multispectral and RGB images from UAVWeed mapping between and within crop rows, (1) cotton; (2) sunflowerRFWeed detection Acc:
(1) Cotton: 84%
(2) Sunflower: 87.9%
[241]Hyperspectral imagesRecognition of three weed species in maize cropsRFMean correct classification rate: (1) Zea mays: 1.0; (2) Convolvulus arvensis: 0.789; Rumex: 0.691; Cirsium arvense 0.752
[242]RGB images from UAVDetection of weeds in early season maize fieldsRF Overall Acc = 0.945, Kappa = 0.912
[243]RGB images from UAVWeed mapping and prescription map generation in rice fieldFCNOverall Acc = 0.9196,
mean intersection over union (mean IU) = 0.8473
[244]Handheld multispectral dataWeed detection in maize and sugar beet row-crops with:
(1) spectral method; (2) spatial; (3) both methods
SVMMean detection rate: (1) spectral method: 75%; (2) spatial: 79%; (3) both methods: 89%
[245]Multispectral images from UAVDevelopment of Weed/crop segmentation, mapping framework in sugar beet fieldsDNNAUC: (1) background: 0.839; (2) crop: 0.681; (3) weed: 0.576
[246]RGB imagesClassification of potato plant and three weed speciesANNAcc = 98.1%
[247]RGB imagesEstimation of weed growth stage (18 species)CNNMaximum Acc = 78% (Polygonum spp.), minimum Acc = 46% (blackgrass), average Acc = 70% (the number of leaves) and 96% for deviation of two leaves
[248]Multispectral imagesClassification of corn (crop) and silver beet (weed)SVMPrecision = 98%, Acc = 98%
[249]RGB imagesClassification of Carolina Geranium within strawberry plantsCNN F1 score values: (1) DetectNet: (0.94, highest);
(2) VGGNet: 0.77;
(3) GoogLeNet: 0.62
[250]RGB imagesClassification of weeds in organic carrot productionCNNPlant-based evaluation:
Acc = 94.6%,
Precision = 93.20%,
Recall = 97.5%,
F1 Score = 95.32%
[251]Grayscale images from UGVRecognition of Broad-leaved dock in grasslandsCNN, SVMVGG-F: Acc = 96.8%
[252]Multispectral images from UAVMapping of Black-grass weed in winter wheat fieldsCNNBaseline model:
AUC = 0.78; Weighted kappa = 0.59; Average misclasssification rate = 17.8%
[253]RGB imagesSegmentation of rice and weed images at seedling stage in paddy fieldsFCNSemantic segmentation:
Average Acc rate = 92.7%
[254]RGB images from UGVCreation of multiclass dataset for classification of eight Australian rangelands weed speciesCNNRS-50: Average Acc = 95.7%, average inference time = 53.4 ms per image
[255]RGB imagesEvaluation of weed detection, spraying and mapping system. Two Scenarios: (1) artificial weeds, plants; (2) real weeds, plantsCNNScenario: (1) Acc = 91%, Recall = 91%; (2) Acc = 71%, Precision = 78% (for plant detection and spraying Acc)
[256]RGB imagesDetection of goldenrod weed in wild blueberry cropsLC, QCQC: Acc = 93.80%
[257]RGB imagesDetection of five weed species in turfgrassCNNPrecision values: Dollar weed: VGGNet (0.97); old world diamond-flower: VGGNet (0.99); Florida pusley: VGGNet (0.98); annual bluegrass: DetectNet (1.00)
[258]RGB imagesDetection of three weed species in perennial ryegrassCNNPrecision values: Dandelion: DetectNet (0.99); ground ivy: VGGNet (0.99), spotted spurge:
AlexNet (0.87)
[259]RGB images, multispectral images from UGVCrop-weed classification along with stem detectionFCNOverall: Mean precision = 91.3%, Mean recall = 96.3%
[260]RGB imagesIdentification of crops (cotton, tomato) and weeds (velvetleaf and nightsade)CNN, SVM, XGBoost, LRDensenet and SVM:
micro F1 score = 99.29%
[261]Videos recordingsClassification of two weeds species in rice fieldANN, KNNAcc values: Right channel (76.62%), Left channel (85.59%)
[262]RGB imagesWeed and crop discrimination in paddy fieldsMCS, SRF, SVMAcc values: Right channel (76.62%), Left channel (85.59%)
[263]Gray-scale and RGB imagesWeed and crop discrimination in carrot fieldsRFAcc = 94%
[264]Multispectral and RGB imagesDiscrimination of weed and crops with similar morphologiesCNNAcc = 98.6%
[265]RGB imagesDetection of C. sepium weed and sugar beet plantsCNNmAP = 0.751–0.829
[email protected] = 0.761–0.897
[266]RGB imagesRecognition of eight types of weeds in rangelandsCNN, RNNDeepWeeds dataset:
Acc = 98.1%
[267]Multispectral images from UAVWeed estimation on lettuce cropsSVM, CNNF1 score values: (1) SVM: 88%; (2) CNN-YOLOv3: 94%; (3) Mask R-CNN: 94%
[268]RGB imagesExamination of pre-trained DNN for improvements in weed identificationCNN(1) Xception: improvement = 0.51%; (2) Inception-Resnet: improvement = 1.89%
[269]RGB images from UAVDetection of five weeds in soybean fields CNNFaster RCNN: precision = 065, recall = 0.68, F1 score = 0.66, IoU = 0.85
[270]RGB imagesDetection of goose grass weed in tomato, strawberry fieldsCNN(1) Strawberry: (a) entire plant: F1 score = 0.75, (b) leaf blade: F1 score = 0.85;
(2) Tomato: (a) entire plant: F1 score = 0.56, (b) leaf blade: F1 score = 0.65
[271]Video recordingsDetection of five weed species in Marfona potato fieldsANNCorrect classification rate = 98.33%
[272]In situ measurements, satellite spectral dataIdentification of gamba grass in pasture fieldsXGBoostBalanced Acc = 86.9%
[273]RGB images from UAV, satellite spectral dataWeed maps creation in oat fieldsRFAcc values: (1) Subset A: 89.0%; (2) Subset B: 87.1%
[274]In situ measurements, RGB images from UAVIdentification of Italian ryegrass in early growth wheatDNNPresicion = 95.44%, recall = 95.48%, F score = 95.56%
[275]RGB images from UGVWeed detection evaluation of a spraying robot in potato fields on: (1) Image-level; (2) application-level; (3) field-levelCNNYOLOv3: (1) Image-level: recall = 57%, precision = 84%; (2) application-level: plants detected = 83%; (3) field-level: correct spraying = 96%
[276]RGB images from UGVDetection of four weed species in maize and bean cropsCNNAverage precision = 0.15–0.73
[277]RGB images from UAVDetection of Colchicum autumnale in grassland sitesCNNU-Net: Precision = 0.692, Recall = 0.886, F2 score = 0.839
[278]RGB images from UAVWeed mapping of Rumex obtusifolius in native grasslandsCNNVGG16: Acc = 92.1%, F1 score = 78.7%
Acc: Accuracy; AUC: Area under Curve; IoU: Intersection over Union; mAP: mean Average Precision; RGB: Red-Green-Blue; UAV: Unmanned Aerial Vehicle; UGV: Unmanned Ground Vehicle.
Table A4. Crop Management: Crop Recognition.
Table A4. Crop Management: Crop Recognition.
RefCropInput DataFunctionalityModels/AlgorithmsBest Output
[279]Various cropsSatellite spectral dataClassification of early-season cropsRFBeginning of growth stage: acc = 97.1%, kappa = 93.5%
[280]Various cropsSatellite spectral and phenological dataIdentification of various crops from remote sensing imagerySVM, RF, DFDF: (1) 2015: overall acc = 88%; (2) 2016: overall acc = 85%
[281]Maize, Rice, SoybeanSatellite spectral dataThree-dimensional classification of various cropsCNN, SVM, KNNCNN: (1) 2015: overall acc = 0.939, kappa = 0.902; (2) 2016: overall acc = 0.959, kappa = 0.924
[282]Various cropsSatellite spectral data, in situ dataIdentification of crops growing under plastic covered greenhousesDTOverall acc = 75.87%, Kappa = 0.63
[283]Various cropsSatellite data, phenological, in situ dataClassification of various cropsNB, DT, KMKM: overall acc = 92.04%, Kappa = 0.7998
[284]Cabbage, PotatoRGB images from UAV, in situ dataClassification of potato and cabbage cropsSVM, RFSVM: overall acc = 90.85%
[285]Various cropsSatellite spectral dataClassification of various cropsSVMOverall acc = 94.32%
[286]Various cropsSatellite spectral data, in situ dataClassification of various crops in large areasEBT, DT, WNNEBT: overall acc = 87%
[287]Various cropsSatellite spectral data, in situ dataClassification of various cropsSVMoverall acc = 92.64%
[288]Various cropsField location, in situ and satellite spectral dataClassification of six crops with small sample sizesFFNN, ELM, MKL, SVMMKL: accuracy = 92.1%
[289]Wolfberry, Maize, VegetablesSatellite spectral dataCrop classification in cloudy and rainy areasRNNLandsat-8: overall acc = 88.3%, Kappa = 0.86
[290]Maize, Canola, WheatSatellite spectral data, in situ dataCrop classificationRF, ANN, SVMRF: overall acc = 0.93, Kappa = 0.91
[291]Various cropsSatellite spectral dataClassification of various crop typesCombination of FCN-LSTMAcc = 86%, IoU = 0.64
[292]Various cropsSatellite spectral dataCrop classification of various cropsLightGBMHighest acc: 92.07%
[293]Maize, Peanut, Soybeans, RiceSatellite spectral and in situ dataPrediction of different crop typesFCN, SVM, RFBest crop mapping: FCN: acc = 85%, Kappa = 0.82
[294]Various cropsSatellite spectral and in situ dataClassification of early growth cropsCNN, RNN, RFHighest Kappa: 1D CNN: 0.942
[295]Various cropsSatellite spectral and in situ dataClassification of various cropsCNN, LSTM, RF, XGBoost, SVMCNN: acc = 85.54%, F1 score = 0.73
[296]Various cropsSatellite spectral dataClassification of parcel-based cropsLSTM, DCNDCN: overall acc = 89.41%
[297]Various cropsSatellite spectral dataClassification of crops in farmland parcel mapsLSTM, RF, SVMLSTM: overall acc = 83.67%, kappa = 80.91%
[298]Various cropsSatellite spectral data, in situ dataCrop classificationSVM, RF, CNN-RNN, GBMPixel R-CNN: acc = 96.5%
[299]Zea mays,
Canola, radish
Grayscale testbed dataClassification of the crops SVMQuadratic SVM: Precision = 91.87%, Recall = 91.85%, F1 score = 91.83%
[300]RiceMorphological dataClassification of two rice species (Osmancik-97 and Cammeo)LR, MLP, SVM, DT, RF, NB, KNNLR: acc = 93.02%
[301]SoybeanHyperspectral data, seed propertiesDiscrimination of 10 soybean seed varietiesTS-FFNN, SIMCA, PLS-DA, BPNNTS-FFNN in terms of identification Acc, stability and computational cost
[302]CottonHyperspectral data, seed propertiesIdentification of seven cotton seed varieties: (1) Full spectra, (2) Effective wavelengthsPLS-DA, LGR, SVM, CNN(1) Full spectra:
CNN-SoftMax: 88.838%;
(2) Effective wavelengths:
CNN-SVM: 84.260%
[303]Various plantsRGB images of leavesRecognition of 15 plant species of Swedish leaf datasetCNNMacro average: (1) Precision = 0.97, (2) Recall = 0.97, (3) F1 score = 0.97
[304]Various shrubs and treesRGB images of leavesIdentification of 30 shrub and trees speciesRF, SVM, AdaBoost, ANNSVM: acc = 96.5–98.4%
[305]Various plantsRGB images of leavesIdentification of seven plant speciesBPNN, SOM, KNN, SVMBPNN: Recognition rate = 92.47%
[306]Various crops Satellite spectral dataCrop classificationSVMSVM (RBF): overall acc values: (1) 2016: 88.3%; (2) 2017: 91%; (3) 2018: 85.00%
[307]Various cropsSatellite spectral dataCrop classificationFCN3D FCN: overall acc = 97.56%, Kappa = 95.85%
[308]Cotton, Rice, Wheat, GramSatellite spectral dataCrop classificationRF, KMRF: acc = 95.06%
[309]Various cropsSatellite spectral dataCrop classificationSVM, RF, CARTRF: overall acc = 97.85%, Kappa = 0.95
[310]Various cropsSatellite spectral data, in situ dataCrop classificationRFoverall acc = 75%, Kappa = 72%
[311]Maize, SoybeanSatellite spectral dataCrop classificationRF, MLP, LSTMLSTM: confidence interval = 95%
[312]Various cropsSatellite spectral and in situ dataCrop classificationXGBoost, SVM, RF, MLP, CNN, RNNCNN: overall acc = 96.65%
[313]RiceSatellite spectral dataCrop classificationCNN, SVM, RF, XGboost, MLPCNN: overall acc = 93.14%, F1 score = 0.8552
[314]Various cropsSatellite spectral and in situ dataCrop classificationRFOverall acc = 0.94, Kappa = 0.93
[315]Various cropsSatellite spectral dataCrop classificationCNN, LSTM, SVMCNN: overall acc = 95.44%, Kappa = 94.51%
[316]Various cropsSatellite spectral dataCrop classification prior to harvestingDT, KNN, RF, SVMRF: overall acc = 81.5%, Kappa = 0.75
[317]Various cropsSatellite spectral dataCrop classificationCNNOverall acc = 98.19%
[318]Various cropsSatellite spectral dataCrop classificationSVM, DA, DT, NNLNNL: F1 score = 0.88
[319]Banana, Rice, Sugarcane, CottonSatellite spectral and in situ dataCrop classificationSVMOverall acc = 89%
[320]Various cropsSatellite spectral and in situ dataCrop classificationRFOverall acc = 93.1%
Acc: Accuracy; IoU: Intersection over Union; RGB: Red-Green-Blue; UAV: Unmanned Aerial Vehicle.
Table A5. Crop Management: Crop Quality.
Table A5. Crop Management: Crop Quality.
RefCropInput DataFunctionalityModels/AlgorithmsBest Output
[64]ApplesQuality features, (flesh firmness, soluble solids, fruit mass and skin color)Classification of apple total quality: very poor, poor, medium, good and excellentFIS, ANFISFIS: acc values: (1) 2005: 83.54%; 2006: 92.73%; 2007: 96.36%
[321]PepperRGB images, quality features (color, mass and density of peppers)Recognition of pepper seed qualityBLR, MLPMLP: 15 traits, stability = 99.4%, predicted germination = 79.1%, predicted selection rate = 90.0%
[322]Soybeans Satellite spectral and soil dataEstimation of crop gross primary productivityRF, ANNANN: R2 = 0.92, RMSE = 1.38 gCdm−2
[323]WheatRGB images captured by UAVEstimation of aboveground nitrogen content combining various VI and WFsPLSR, PSO-SVRPSO-SVR: R2 = 0.9025, RMSE = 0.3287
[324]Millet, rye, maizeRGB images captured in laboratoryAssessment of grain crops seed qualityCNNFaster R-CNN: (1) Pearl millet: mAP = 94.3%; (2) rye: mAP = 94.2%, (3) Maize: mAP = 97.9%
[325]Jatropha curcasX-ray imagingPrediction of vigor and germinationLDAAcc values:
Fast germination: 82.08%;
Slow germination: 76.00%;
Non-germinated: 88.24%
[326]Various legumesSpectral data form spectroradiomenerEstimation of five warm-season legumes forage qualityPLS, SVM, GPSVM: All five crops: Acc = R c v 2 R v 2 = 0.92–0.99, IVTD: Acc = R c v 2 R v 2 = 0.42–0.98
[327]Forage grassX-ray imagingPrediction of vigor and seed germinationLDA, PLS-DA, RF, NB, SVMPLS-DA: Acc values:
(1) Vigor: FT-NIR: 0.61, X-ray: 0.68,
Combination: 0.58;
(2) Germination: FT-NIR: 0.82, X-ray: 0.86, Combination: 0.82
[328]TomatoRGB imagesDimensions and mass estimation for quality inspection(1) DSM, (2) Dimensions (CNN), (3) Mass estimation on: (a) MMD (BET, GPR, SVR, ANN, GPR), (b) EDG (BET, GPR, SVR, ANN)(1) DSM: precision = 99.7%; MAE values: (2) Width (2.38), Length (2.58); (3) Mass estimation: (a) MMD (4.71), (b) EDG (13.04)
[329]PeachHyperspectral imagesEstimation of soluble solids contentSAE-RFR2 = 0.9184, RMSE = 0.6693
Acc: Accuracy; DSM: Detection and Segmentation Module; EDG: Estimated Dimensions Geometry; IVTD: In Vitro True Digestibility; RGB; Red-Green-Blue; MMD: Manually Measured Dimensions; mAP: mean Average Precision; PSO: Particle Swarm Optimization; RGB; Red-Green-Blue; SAE: Stacked AutoEncoder; VI: Vegetation Indices; WF: Wavelet Features.
Table A6. Water management.
Table A6. Water management.
RefPropertyInput DataFunctionalityModels/AlgorithmsBest Output
[330]Crop water statusWeather data, crop water status, thermal imagesPrediction of vineyard’s water status. Scenario A: with RT; Scenario B: without RTREPTree(1) Scenario A: prediction: R2 = 0.58, RMSE = 0.204 MPa; (2) Scenario B: prediction: R2 = 0.65, RMSE = 0.184 MPa.
[331]Crop water statusCrop water status, hyperspectral dataDiscrimination of stressed and non-stressed vinesRF, XGBoostRF: Acc = 83.3%, Kappa = 0.67
[332]Groundwater levelWater table depth, weather dataPrediction of water table depthLSTM, FFNN,LSTM: R2 = 0.789–0.952
[333]Irrigation schedulingWeather, irrigation, soil moisture, yield dataPrediction of weekly irrigation plan in jojoba orchardsDTR, RFR, GBRT, MLR, BTC(1) Regression: GBRT: Acc = 93%; (2) Classification: GBRT: Acc = 95%
[334]Crop water statusWater status, multispectral UAV dataEstimation of vineyard water statusMLR, ANNANN: R2 = 0.83
[335]ETWeather dataEstimation of daily EToELM, WANNELM: RMSE values: Region case A: 0.1785 mm/day; Region case B: 0.359 mm/day
[336]ETWeather dataEstimation of daily EToRF, M5Tree, GBDT, XGBoost, SVM, RFXGBoost: RMSE = 0.185–0.817 mmday−1
[337]Soil water contentWeather data, volumetric soil moisture contentPrediction of one-day-ahead volumetric soil moisture contentFFNN, LSTMLSTM: R2 > 0.94
[338]InfiltrationField data, moisture content, cumulative infiltration of soilEstimation of cumulative infiltration of soilSVM, ANN, ANFISANFIS: RMSE = 0.8165 cm, CC = 0.9943
[339]Soil water contentWeather data, soil moisture difference, ultraviolet radiationPrediction of soil moistureSVRR = 0.98, R2 = 0.96, MSE = 0.10
[340]Soil water contentSimulated soil moisture data, weather dataForecasting of monthly soil moisture for: Scenario A: upper; Scenario B: lower layersELM(1) Scenario A: RRMSE = 19.16%;
(2) Scenario B: RRMSE = 18.99%
[341]ETWeather and in situ crop dataEstimation of actual ET
Scenario A: rainfed maize field under non-mulching; Scenario B: partial plastic film mulching
ANN, SVMANN: Scenario A: ET = 399.3 mm, RMSE = 0.469, MAE = 0.376;
Scenario B: ET = 361.2 mm, RMSE = 0.421, MAE = 0.322
[342]Infiltration and infiltration rateSoil and hydraulic dataPrediction of cumulative infiltration and infiltration rate in arid areasANFIS, SVM, RFSVM: RMSE values: cumulative infiltration: 0.2791 cm, infiltration rate: 0.0633 cmh−1
[343]Water qualityNIR spectroscopy.Estimation of water pollution levelCNNRMSE = 25.47 mgL−1
[344]ETWeather data, simulated ET dataEstimation of ETo: (1) 2011–2015; (2) 2016–2017LSTM(1) Predictions in 3 sites: R2 > 0.90; (2) All sites: RMSE = 0.38–0.58 mmday−1
[345]Soil water contentWeather data, potential ET, simulated soil moisture dataEstimation of soil moistureFFNN, Ross-IESFFNN: RMSE = 0.15–0.25, NSE = 0.71–0.91
[346]ETWeather data, simulated ET data, soil dataEstimation of daily kikuyu grass crop ETRT, SVR, MLP, KNN, LGR, MLR, BN, RFCRFC: R = 0.9936, RMSE = 0.183 mmday−1, MRE = 6.52%
[347]DroughtWeather dataEvaluation of farmers’ draught perception RF, DTMost influential parameters: farmer’s age, education level, years of experience and number of cultivated land plots
[348]ETWeather and soil data; simulated ETPrediction of daily potato ETANN,
AdaBoost, KNN
KNN: R2 = 0.8965, RMSE = 0.355 mm day−1, MSE = 0.126 mm day−1
[349]Soil water erosionIn situ data, geological, and weather dataSusceptibility mapping of soil erosion from waterRF, GP, NBRF: Acc = 0.91, kappa = 0.94, POD = 0.94
[350]ET, droughtWeather data, simulated ET indexPrediction of droughtSVRFuzzy-SVR: R2 = 0.903, RMSE = 0.137, MAE = 0.105
[351]ETWeather data, simulated EToEstimation of daily EToCNN, ANN, XGBoost, RFCNN: (1) Regional: R2 = 0.91, RMSE = 0.47; (2) Local: R2 = 0.92, RMSE = 0.37
[352]ETWeather dataEstimation of daily EToELM, ANN, RFELM: R2 = 0.920, MAE = 0.394 mmday−1
[353]ETWeather dataPrediction of ET in semi-arid and arid regionsCART, CCNN, SVMSVM: (1) Station I: R2 = 0.92; (1) Station II: R2 = 0.97
[354]Pan evaporationWeather dataPrediction of monthly pan evaporationELM, ANN, M5TreeELM: R2 = 0.864–0.924, RMSE = 0.3069–0.4212
[355]ETWeather data, simulated EToEvaluation of ML algorithms in daily reference ET predictionCubist, SVM, ANN, MLRCubist: R2 = 0.99, RMSE = 0.10 mmday−1, MAE = 0.07 mmday−1
[356]ETWeather data, simulated ETEstimation of EToSVM, MLP, CNN, GRNN, GMDHSVM: R = 0.96–1.00, ME = 95–99%
[357]DroughtWeather data, simulated Palmer Z-index valuesEstimation of Palmer drought severity indexANN, DT, LR, SVMANN: R = 0.98, MSE = 0.40, RMSE = 0.56
[358]Water qualityIn-situ water quality data, hyperspectral, satellite data.Estimation of inland water quality.LSTM, PLSR, SVR, DNNDNN: R2 = 0.81, MSE = 0.29, RMSE = 0.54
[359]GroundwaterIn-situ water quality data, hyperspectral, satellite spectral dataEstimation of water qualityDTAcc = 81.49%, ROC = 87.75%
[360]GroundwaterWeather data, ET, satellite spectral data, land useEstimation of groundwater withdrawalsRFR2 = 0.93, MAE = 4.31 mm, RMSE = 13.50 mm
[361]Groundwater nitrate concentrationVarious geo-environmental dataComparison of different ML models for estimating nitrate concentrationSVM, Cubist, RF, Bayesian-ANNRF: R2 = 0.89, RMSE = 4.24, NSE = 0.87
Acc: Accuracy; CC: Coefficient of Correlation; ET: Evapotranspiration; ETo: reference EvapoTranspiration; ROC: Receiver Operating Characteristic; ME: Model Efficiency; NSE: Nash-Sutcliffe model efficiency Coefficient; POD: Probability Of Detection.
Table A7. Soil management.
Table A7. Soil management.
RefPropertyInput DataFunctionalityModels/AlgorithmsBest Output
[362]Soil organic matterSoil properties, spectrometer NIR dataEstimation of soil organic matterELM, SVMTRI-ELM: R2 = 0.83, RPIQ = 3.49
[363]Soil microbial dynamicsMicrobial dynamics measurements from root samplesPrediction of microbial dynamics: (1) BP; (2) PS and (3) ACCAANN, SVR, FISSCFIS: (1) BP: RMSE = 1350000, R2 = 1.00; (2) PS: RMSE = 45.28, R2 = 1.00; (3) ACCA: RMSE = 271, R2 = 0.52
[364]Soil salinitySoil salinity, hyperspectral data, satellite dataPrediction of soil salinityBootstrap
BPNN with hyperspectral data: R2 = 0.95, RMSE = 4.38 g/kg
[365]Soil propertiesSimulated topographic attributes, satellite dataPrediction of SOC, CCE, clay contentCu, RF, RT, MLR(1) CCE: Cu: R2 = 0.30, RMSE = 9.52; (2) SOC: Cu, RF: R2 = 0.55; (3) Clay contents: RF: R2 = 0.15, RMSE = 7.86
[366]Soil organic matterSoil properties, weather data, terrain, satellite spectral dataPrediction of soil organic matterDT, BDT, RF, GBRTGBRT: ME = 1.26 g/kg, RMSE = 5.41 g/kg, CCC = 0.72
[367]Soil organic mattersoil properties, satellite, land cover, topographic, weather dataPrediction of soil organic matterCNN, RF, XGBoostXGBoost: ME = 0.3663 g/kg, MSE = 1.0996 g/kg
[368]Electrical conductivitysoil properties, simulated electrical conductivityPrediction of soil electrical conductivityMLPMLP: WI = 0.780, ENS = 0.725,
ELM = 0.552
[369]Soil moisture contentHyperspectral images data, UAV, soil moisture content data samplesEstimation of soil moisture contentRF, ELMRF: R2 = 0.907,RMSEP = 1.477, RPD = 3.396
[370]Soil temperatureWeather dataEstimation of soil temperature at various depthsELM, GRNN, BPNN, RFELM: RMSE = 2.26–2.95 °C, MAE = 1.76–2.26 °C, NSE = 0.856–0.930, CC = 0.925–0.965
[371]SOCSoil properties, vis-NIR spectral dataEstimation of SOCRFR2 = 0.74–0.84,
RMSEP = 0.14–0.18%, RPD = 1.98–2.5
[372]Soil propertiesSoil properties, visible-NIR, MIR spectral dataPrediction of total carbon, cation exchange capacity and SOCPLSR, Cu, CNNCNN: R2 = 0.95–0.98
[373]Soil propertiesSoil properties, simulated organic, mineral samples, soil spectral data Estimation of various soil propertiesCNNRMSE values: OC: 28.83 g/kg, CEC: 8.68 cmol+/kg, Clay: 7.47%, Sand: 18.03%,
pH: 0.5 g/kg, N: 1.52 g/kg
[374]Soil moisture content, soil ETSoil properties, water, weather and crop dataEstimation of soil moisture content and soil ETNN-RBFSoil MC: RMSE = 0.428, RSE = 0.985, MSE = 0.183, RPD = 8.251
[375]Soil salinitySoil salinity, crop field temperatureEstimation of leaching water requirements for saline soilsNaive Bayes classifierAcc = 85%
[376]Soil erosionWeather data, satellite, soil chemical dataEstimation of soil erosion susceptibilityCombination of GWR-ANNGWR-ANN: AUC = 91.64%
[377]Soil fertilitySpectral, weather data, EC, soil propertiesPrediction of soil fertility and productivityPLS(1) Productivity: RMSEC = 0.20 T/ha, RMSECV = 0.54 T/ha, R2 = 0.9189;
(2) Organic matter: R2 = 0.9345, RMSECV = 0.54%; (3) Clay: R2 = 0.9239, RMSECV = 5.28%
[378]Soil moistureMultispectral images from UAV, in situ soil moisture, weather data.Retrieval of surface soil moistureBRT, RF, SVR, RVRBRT: MAE = 3.8%
[379]Soil moistureSoil samples, simulated PWP, field capacity dataEstimation of PWP and field capacityANN, KNN, DLR2 = 0.829, R = 0.911, MAE = 0.027
[380]Soil temperatureWeather dataEstimation of soil temperatureGMDH, ELM, ANN, CART, MLRELM: R = 0.99
[381]Soil moistureSoil samples, on-field thermal, simulated soil moisture dataEstimation of soil moisture contentANN, SVM, ANFISSVM: R = 0.849, RMSE = 0.0131
[382]Gully erosionGeological, environmental, geographical dataEvaluation of gully erosion susceptibility mappingRF, CDTree, BFTree, KLRRF: AUC = 0.893
[383]Groundwater salinityTopographic, groundwater salinity dataEvaluation of groundwater salinity susceptibility mapsStoGB, RotFor, BGLMBGLM: Kappa = 0.85
[384]Heavy metals transferSoil and crop propertiesIdentification of factors related to heavy metals transferRF, GBM, GLMRF: R2 = 0.17–0.84
[385]Land suitabilitySoil properties, weather, topography dataPrediction of land suitability mapsSVM, RFRF: Kappa = 0.77, overall acc = 0.79
[386]SOCSoil properties, satellite, simulated environmental dataPrediction of SOCMLR, SVM, Cu, RF, ANNRF: R2 = 0.68
[387]Electrical conductivity, SOCSoil properties, weather dataElectrical conductivity and SOC predictionGLM(1) EC: MSPE = 0.686, MAPE = 0.635; (2) OC: MSPE = 0.413, MAPE = 0.474
[388]SOC, soil moistureProximal spectral data, electrical conductivity, soil samples dataPrediction of SOC and soil moisture 3D mapsCu, RFCu: R2 = 0.76, CCC = 0.84, RMSE = 0.38%
[389]Soil aggregate stabilitySoil samples dataPrediction of soil aggregate stabilityGLM, ANNANN: R2 = 0.82
[390]SOCSoil samples, weather, topographic, satellite dataPrediction of SOCCu, RF, SVM, XGBoost, KNNBest SOC prediction: RF: RMSE = 0.35%, R2 = 0.6
[391]Soil moistureIn situ soil moisture, satellite dataEstimation of surface soil moistureSVM, RF, ANN, ENRF: NSE = 0.73
[392]SOCComposite surface soil, satellite, weather dataPrediction of SOCSVM, ANN, RT, RF, XGBoost, DNNDNN: MAE = 0.59%, RMSE = 0.75%, R2 = 0.65, CCC = 0.83
[393]Gully erosionTopographic, weather, soil dataMapping of gully erosion susceptibilityLMT, NBTree, ADTreeLMT: AUC = 0.944
[394]Gully erosionSatellite spectral dataIdentification of gully erosionLDA, SVM, RFBest overall acc: RF: 98.7%
[395]Gully erosionSatellite, weather, land type maps dataGully erosion mappingLGRAcc = 68%, Kappa = 0.42
ACCA: Aminoyclopropane-1-carboxylate; AUC: Area Under Curve; BP: Bacterial Population; CC: Coefficient of Correlation; CCC: Concordance Correlation Coefficient; CCE: Calcium Carbonate Equivalent; ET: EvaporoTransporation; MIR: Mid InfraRed; NSE: Nash-Sutcliffe model efficiency Coefficient; NIR: Near-InfraRed; PS: Phosphate Solubilization; PWP: Permanent Wilting Point; RPIQ: Ratio of Performance to Interquartile Range; RPD: Relative Percent Deviation; SOC: Soil Organic Carbon; WI: Willmott’s Index.
Table A8. Livestock Management: Animal Welfare.
Table A8. Livestock Management: Animal Welfare.
RefAnimalInput DataFunctionalityModels/AlgorithmsBest Output
[396]Swine3D, 2D video imagesDetection of pigs tail posture as a sign of tail bitingLMMLow vs. not low tails: Acc = 73.9%, Sensitivity = 88.4%, Specificity = 66.8%
[397]SheepAccelerometer and gyroscope attached to the ear and collar of sheepClassification of Grazing and Rumination Behavior in SheepRF, SVM, KNN, AdaboostRF: Highest overall acc: collar: 92%; ear: 91%
[398]SheepAccelerometer, gyroscope dataClassification of sheep behavior (lying, standing and walking)RFAcc = 95%, F1-score = 91–97% for: ear: 32 Hz, 7 s, collar: 32 Hz, 5 s
[399]SwineRGB imagesRecognition of pigs feeding behaviorCNNFaster R-CNN: Precision = 99.6%, recall = 86.93%
[400]SwineRGB images, depth imagesRecognition of lactating sow posturesCNNFaster R-CNN: Sow posture:
(1) Recumbency: night: 92.9%, daytime: 84.1%;
(2) Standing: at night: 0.4%, daytime: 10.5%
(3) Sitting: night: 0.55%, daytime: 3.4%
[401]Cattle, Sheep, sheepdogAudio field recordings dataClassification of animals’ vocalizationSVMAcc: cattle: 95.78%, sheep: 99.29%, dogs: 99.67%
[402]CattleAccelerometer dataDetection of sheep rumination.SVMAcc = 86.1%
[403]SheepEar-borne accelerometer data, observation recordingsClassification of grazed sheep behavior Scenario A: walking, standing, lying, grazing
Scenario B: active/inactive
Scenario C: body posture
CART, SVM, LDA, QDA(1) Scenario A: SVMAcc: 76.9%;
(2) Scenario B: CART
Acc: 98.1%;
(3) Scenario C:
Acc: LDA 90.6%
[404]GoatOn-farm videos, weather dataClassification of goats behavior
(1) Anomaly detection (2) Feeding/non-feeding
KNN, SVR, CNN(1) Most accurate: KNN: Acc = 95.02–96.5%; (2) Faster R-CNN: Eating: 55.91–61.33 %, Non-feeding (Resting): 79.91–81.53 %
[405]Cattle, sheep UAV Video dataCounting and classification of cattle, sheepCNNMask R-CNN: Cattle: Acc = 96%; Sheep: Acc = 92%
[406]CattleAccelerometer dataPrediction of dairy cows behavior at pastureXGBoost, SVM, AdaBoost, RFBest predictions for most behaviours: XGBoost: sensitivity = 0.78
[407]CattlePedometersDetection of early lameness in dairy cattleRF, KNNRF: acc = 91%
[408]CattleEnvironmental heat stressors dataEvaluation of heat stressors influence in dairy cows physiological responsesRF, GBM, ANN, PLRRF: (1) RR: RMSE = 9.695 respmin−1; (2) ST: RMSE = 0.334 °C
[409]CattleDiets nutrient levels dataPrediction of dairy cows diet energy digestionELM, LR, ANN, SVMBest performance: kernel-ELM: (1) DE: R2 = 08879, MAE = 4.0606; (2) ED: R2 = 0899, MAE = 2.3272
[410]CattleRoutine herd dataDetection of lameness in dairy herdsGLM, RF, GBM, XGBoost, CARTGBM: AUC = 0.75, Sensitivity = 0.58, Specificity = 0.83
[411]PoultryAir quality dataEarly prediction of Coccidiosis in poultry farmsKNNAUC = 0.897–0.967
[412]CattleOn-farm questionnaires, clinical and milk recordsPrediction of mastitis infection in dairy herdsRFCONT vs. ENV: Acc = 95%, PPV = 100%, NPV = 95%
[413]CattleLocation (transceiver) and accelerometer dataDetection of dairy cows in estrusKNN, LDA, CART, BPNN, KNNBPNN: specificity = 85.71%
[414]CattleMid-NIR spectral data using spectrometerPrediction of bovine tuberculosis in dairy cowsCNNAccuracy = 71%, sensitivity = 0.79, specificity = 0.65
[415]CattleMetabolomics data from serum samplesEvaluation of metabotypes existence in overconditioned dairy cowsRF, NB, SMO, ADTADT: acc = 84.2%
[416]CattleAccelerometer dataClassification of cows’ behavior GBDT, SVM, RF, KNNGBDT: acc = 86.3%, sensitivity = 80.6%
[417]SheepGyroscope and accelerometer ear sensorsDetection of lame and non-lame sheep in three activitiesRF, SVM, MLP, AdaBoostRF: overall acc = 80%
[418]CattleActivity and rumination dataPrediction of calving day in cattleRNN, RF, LDA, KNN, SVMRNN/LSTM: Sensitivity = 0.72, Specificity = 0.98
AUC: Area Under Curve; Cont: Contagious; DE: Digestible Energy; ED: Energy Digestibility; ENV: Environmental; DWT: Discrete Wavelet Transform; MFCCs: Mel-Frequency Cepstral Coefficients; NIR: Near InfraRed; NPV: Negative Predictive Value; PTZ: Pan-Tilt-Zoom; PPV: Positive Predictive Value; RGB: Red-Green-Blue; RR: Respiration Rate; ST: Skin Temperature.
Table A9. Livestock Management: Livestock Production.
Table A9. Livestock Management: Livestock Production.
RefAnimalInput DataFunctionalityModels/AlgorithmsBest Output
[419]CattleDepth images in situ BCS evaluation dataEstimation of BCS, Scenario A: HER = 0.25; Scenario B: HER = 0.5CNNScenario A: Acc = 78%; Scenario B: Acc = 94%
[420]SwineWeather, physiological dataPrediction of piglets temperature
Scenario A: skin-surface; Scenario B: hair-coat; Scenario C: core
DNN, GBR, RF, GLRBest prediction: Scenario C: DNN: error = 0.36%
[421]PoultryDepth, RGB images dataClassification of flock of chickens’ behaviorCNNAcc = 99.17%
[422]CattleAccelerometer, observations recordings dataClassification of cattle behaviour
Scenario A: grazing; Scenario B: standing; Scenario C: ruminating
RFHighest F-scores: RF: Scenario A: 0.914; Scenario B: 0.89; Scenario C: 0.932
[423]SheepPhenotypic, weather dataPrediction of on-farm water and electricity consumption on pasture based Irish dairy farmsBAG, ANN, MTScenario 3: MT: R = 0.95, MAE = 0.88 μm, RMSE = 1.19
[424]CattleMilk production, environmental dataPrediction of on-farm water and electricity consumption on pasture based Irish dairy farmsCART, RF, ANN, SVMElectricity consumption prediction: SVM: relative prediction error = 12%
[425]GoatRGB dataDetection of dairy goats from surveillance videoCNNFaster R-CNN: Acc = 92.49 %
[426]CattleAnimal feed, machinery, milk yield dataEstimation of energy use targets for buffalo farmsANN30.5 % of total energy input can be saved if targeted inputs are followed
[427]Cattle3D images dataPrediction of liveweight and carcass characteristicsANN, SLRANN: Liveweight: R2 = 0.7, RMSE = 42; CCW:
R2 = 0.88, RMSE = 14; SMY: R2 = 0.72, RMSE = 14
[428]SwineRGB imagesDetection and pig counting on farms CNNMAE = 1.67, RMSE = 2.13, detection speed = 42 ms per image
[429]SheepBiometric traits, body condition score dataPrediction of commercial meat cuts and carcass traitsMLR, ANN, SVR, BNSVM: Neck weight: R2 = 0.63, RMSE = 0.09 kg; HCW: R2 = 0.84, RMSE = 0.64
[430]CattleData produced by REIMSPrediction of beef attributes (muscle tenderness, production background, breed type and quality grade)SVM, RF, KNN, LDA, PDA, XGBoost, LogitBoost, PLS-DABest Acc: SVM: 99%
[431]SheepCarcass, live weight and environmental recordsEstimation of sheep carcass traits (IMF, HCW, CTLEAN, GRFAT, LW)DL, GBT, KNN, MT, RFHighest prediction of all traits: RF: (1) IMF: R = 0.56, MAE = 0.74; (2) HCW: R = 0.88, MAE = 1.19; (3) CTLEAN: R = 0.88, MAE = 0.76
[432]SwineADG, breed, MT, gender and BBFTIdentification of pigs’ limb conditionRF, KNN, ANN, SVM, NB, GLM, Boost, LDARF: Acc = 0.8846, Kappa = 0.7693
[433]CattleActivity, weather dataPrediction of cows protein and fat content, milk yield and actual concentrate feed intake, Scenario (1) only cows with similar heat tolerance; Scenario (2) all cowsANN (1) Scenario A: n = 116, 456; R = 0.87; slope = 0.76;
(2) Scenario B: n = 665, 836; R = 0.86; slope = 0.74
[434]CattleAnimal behavior, feed intake, estrus events dataDetection of estrus in dairy heifersGLM, ANN, RFRF: Acc = 76.3–96.5%
[435]CattleInfrared thermal imagesEstimation of deep body temperatureLRM, QRMHigher correlation: QRM: R2 = 0.922
[436]CattleLiveweight, biophysical measurements dataPrediction of Carcass traits and marbling score in beef cattleLR, MLP, MT, RF, SVMSVM: carcass weight: R = 0.945, MAE = 0.139; EMA: R = 0.676, MAE = 4.793; MS: R = 0.631, MAE = 1.11
ACFW: Adult Clean Fleece Weight; ADG: Average Daily Gain; AFD: Adult Fibre Diameter; AGFW: Adult Greasy Fleece Weight; ASL: Adult Staple Length; ASS: Adult Staple Strength; BBFT: Bacon/BackFat Thickness; BCS: Body Condition Score; CCW: Cold Carcass Weights; CTLEAN: Computed Tomography Lean Meat Yield; DBT: Deep Body Temperature; EMA: Eye Muscle Area; GWAS: Genome-Wide Association Studies; GRFAT: Greville Rule Fat Depth; HER: Human Error Range; IMF: IntraMuscular Fat; HCW: Hot Carcass Weight; LW: Loin Weight; MS: Marbling Score; MT: Muscle Thickness; REIMS: Rapid Evaporative Ionization Mass Spectrometry; RGB: Red-Green-Blue; SMY: Saleable Meat Yield.
Table A10. Abbreviations for machine learning models.
Table A10. Abbreviations for machine learning models.
ANNArtificial Neural Network
BMBayesian Models
DLDeep Learning
DRDimensionality Reduction
DTDecision Trees
ELEnsemble Learning
IBMInstance Based Models
SVMSupport Vector Machine
Table A11. Abbreviations for machine learning algorithms.
Table A11. Abbreviations for machine learning algorithms.
AdaBoostELAdaptive Boosting
ADTDTAlternating Decision Trees
ANFISANNAdaptive-Neuro Fuzzy Inference Systems
ARDBMAutomatic Relevance Determination
Bayesian-ANNANNBayesian Artificial Neural Network
BAGELBagging Algorithm
BDTDTBagging Decision Trees
BDLBM,ANNBayesian Deep Learning
BETELBagged Ensemble Tree
BGLMBM, RegressionBayesian Generalized Linear Model
BLRRegressionBinary Logistic Regression
BNBMBayesian Network
BPNNANNBack-Propagation Neural Networks
BRTDT,ELBoosted Regression Trees
BTCELBoosted Trees Classifiers
CARTDTClassification And Regression Trees
CCNNANNCascade Correlation Neural Networks
CDTreeDTCredal Decision Trees
CNNANNConvolutional Neural Networks
DBNANNDeep Belief Networks
DFEL,SVMDecision Fusion
DLSRegressionDamped Least Squares
DNNANNDeep Neural Networks
DTRDT, RegressionDecision Tree Regression
EBTDT,ELEnsemble Bagged Trees
ERTDTExtremely Randomized Trees
ELMANNExtreme Learning Machines
ENRegressionElastic Net
FCNANNFully Convolutional Networks
FISANNFuzzy Inference System
FFNNANNFeed Forward Neural Networks
GBMELGradient Boosting Model
GBTDTGradient Tree Boosting
GBRRegressionGradient Boosted Regression
GBRTDT, RegressionGradient Boosted Regression Trees
GBDTDT,ELGradient Boosted Decision Trees
GLMRegressionGeneral Linear Model
GMDHDRGroup Method of Data Handling
GNBBMGaussian Naive Bayes
GPΒΜGaussian Processes
GPRΒΜGaussian Process Regression
GRNNANNGeneralized Regression Neural Networks
GWRRegressionGeographically Weighted Regression
KNNIBMK-Nearest Neighbors
LASSORegressionLeast Absolute Shrinkage and Selection Operator
LDADRLinear Discriminant Analysis
LightGBMELLight Gradient Boosting Machine
LMTRegression, DTLogistic Model Trees
LGRRegressionLoGistic Regression
LMMRegressionLinear Mixed Model
LRRegressionLinear Regression
LSTMANNLong-Short Term Memory
LogitBoostELLogistic Boosting
M5TreeDTM5 model Trees
MANNANNModular Artificial Neural Networks
MARSRegressionMultivariate Adaptive Regression Splines
MCSELMultiple Classifier System
MKLDRMultiple Kernel Learning
MLPANNMulti-Layer Perceptron
MLRRegressionMultiple Linear Regression
MTDTModel Trees
NBBMNaïve Bayes
NBTreeBM, DTNaïve Bayes Trees
NNLIBMNearest Neighbor Learner
OLSRegressionOrdinary Least Squares
PLSRRegressionPartial Least Squares Regression
PLS-DARegression, DRPartial Least Squares Discriminant Analysis
QCRegressionQuadratic Classifier
QDADRQuadratic Discriminant Analysis
QRMRegressionQuadratic Regression Model
RBFNANNRadial Basis Function Networks
REPTreeDTReduced Error Pruning Tree
RFCELRandomizable Filtered Classifier
RFREL, RegressionRandom Forest Regression
RNNANNRecurrent Neural Network
RQLRegressionRegression Quantile LASSO
RFELRandom Forest
Ross-IESELRoss Iterative Ensemble Smoother
RotForELRotation Forest
RVMRRegressionRelevance Vector Machine Regression
SCFISANNSubtractive Clustering Fuzzy Inference System
STDADRStepwise Discriminant Analysis
SMOSVMSequential Minimal Optimization
SMLRRegressionStepwise Multiple Linear Regression
SOMDRSelf-Organising Maps
StoGBELStochastic Gradient Boosting
SVRSVMSupport Vector Regression
TS-FNNANNTakagi-Sugeno Fuzzy Neural Networks
XGBoostELExtreme Gradient Boosting
WANNANNWavelet Artificial Neural Networks
WELELWeighted Ensemble Learning
WNNIBMWeighted Nearest Neighbors
WSLELWeakly Supervised Learning


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Figure 1. The four generic categories in agriculture exploiting machine learning techniques, as presented in [12].
Figure 1. The four generic categories in agriculture exploiting machine learning techniques, as presented in [12].
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Figure 2. A graphical illustration of a typical machine learning system.
Figure 2. A graphical illustration of a typical machine learning system.
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Figure 3. The flowchart of the methodology of the present systematic review along with the flow of information regarding the exclusive criteria, based on PRISMA guidelines [75].
Figure 3. The flowchart of the methodology of the present systematic review along with the flow of information regarding the exclusive criteria, based on PRISMA guidelines [75].
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Figure 4. Representative illustration of a simplified confusion matrix.
Figure 4. Representative illustration of a simplified confusion matrix.
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Figure 5. The classification of the reviewed studies according to the field of application.
Figure 5. The classification of the reviewed studies according to the field of application.
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Figure 6. Geographical distribution of the contribution of each country to the research field focusing on machine learning in agriculture.
Figure 6. Geographical distribution of the contribution of each country to the research field focusing on machine learning in agriculture.
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Figure 7. Distribution of the most contributing international journals (published at least four articles) concerning applications of machine learning in agriculture.
Figure 7. Distribution of the most contributing international journals (published at least four articles) concerning applications of machine learning in agriculture.
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Figure 8. Machine Learning models giving the best output.
Figure 8. Machine Learning models giving the best output.
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Figure 9. The 10 most investigated crops using machine learning models; the results refer to crop management.
Figure 9. The 10 most investigated crops using machine learning models; the results refer to crop management.
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Figure 10. Frequency of animal species in studies concerning livestock management by using machine learning models.
Figure 10. Frequency of animal species in studies concerning livestock management by using machine learning models.
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Figure 11. Distribution of the most usual features implemented as input data in the machine learning algorithms for each category/sub-category.
Figure 11. Distribution of the most usual features implemented as input data in the machine learning algorithms for each category/sub-category.
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Figure 12. Distribution of the most usual output features of the machine learning algorithms regarding: (a) Disease detection and (b) Crop quality.
Figure 12. Distribution of the most usual output features of the machine learning algorithms regarding: (a) Disease detection and (b) Crop quality.
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Figure 13. Temporal distribution of the reviewed studies focusing on machine learning in agriculture, which were published within 2018–2020.
Figure 13. Temporal distribution of the reviewed studies focusing on machine learning in agriculture, which were published within 2018–2020.
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Table 1. Summary of the most commonly used evaluation metrics of the reviewed studies.
Table 1. Summary of the most commonly used evaluation metrics of the reviewed studies.
Accuracy(TP + TN)/(TP + FP + FN + TN)
RecallTP/(TP + FN)
PrecisionTP/(TP + FP)
SpecificityTN/(TN + FP)
F1 score(2 × Recall × Precision)/(Recall + Precision)
Table 2. List of the tables appearing in the Appendix A related to: (a) the categories and sub-categories of the machine learning applications in agriculture (Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8 and Table A9) and (b) the abbreviations of machine learning models and algorithms (Table A10 and Table A11, respectively).
Table 2. List of the tables appearing in the Appendix A related to: (a) the categories and sub-categories of the machine learning applications in agriculture (Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8 and Table A9) and (b) the abbreviations of machine learning models and algorithms (Table A10 and Table A11, respectively).
A1Crop Management: Yield Prediction
A2Crop Management: Disease Detection
A3Crop Management: Weed Detection
A4Crop Management: Crop Recognition
A5Crop Management: Crop Quality
A6Water Management
A7Soil Management
A8Livestock Management: Animal Welfare
A9Livestock Management: Livestock Production
A10Abbreviations of machine learning models
A11Abbreviations of machine learning algorithms
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Benos, L.; Tagarakis, A.C.; Dolias, G.; Berruto, R.; Kateris, D.; Bochtis, D. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors 2021, 21, 3758.

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Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors. 2021; 21(11):3758.

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Benos, Lefteris, Aristotelis C. Tagarakis, Georgios Dolias, Remigio Berruto, Dimitrios Kateris, and Dionysis Bochtis. 2021. "Machine Learning in Agriculture: A Comprehensive Updated Review" Sensors 21, no. 11: 3758.

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