Machine Learning in Agriculture: A Comprehensive Updated Review

Abstract

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.

Keywords: artificial intelligence; crop management; livestock management; machine learning; precision agriculture; precision livestock farming; soil management; water management.

Figure 1

The four generic categories in agriculture exploiting machine learning techniques, as presented in [12].

Figure 2

A graphical illustration of a typical machine learning system.

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 4

Representative illustration of a simplified confusion matrix.

Figure 5

The classification of the reviewed studies according to the field of application.

Figure 6

Geographical distribution of the contribution of each country to the research field focusing on 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.

Figure 8

Machine Learning models giving the best output.

Figure 9

The 10 most investigated crops using machine learning models; the results refer to crop management.

Figure 10

Frequency of animal species in studies concerning livestock management by using machine learning models.

Figure 11

Distribution of the most usual features implemented as input data in the machine learning algorithms for each category/sub-category.

Figure 12

Distribution of the most usual output features of the machine learning algorithms regarding: (a) Disease detection and (b) Crop quality.

Figure 13

Temporal distribution of the reviewed studies focusing on machine learning in agriculture, which were published within 2018–2020.