Machine Learning in Agriculture: A Comprehensive Updated Review.

Lefteris Benos,Aristotelis C Tagarakis,Georgios Dolias,Remigio Berruto,Dimitrios Kateris,Dionysis Bochtis

doi:10.3390/s21113758

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.

Highlights

Significant results can be deduced in an effort to identify: (a) the most contributing authors and organizations, (b) the most contributing international journals, and (c) the current trends in this field [82]
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
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

Summary

Introduction

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 These two essentials have driven the transformation of agriculture into precision agriculture. 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. 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. By relying on training data, within the learning phase, the machine learns to perform the task from experience. The model that was developed through the training process can be used to classify, cluster, or predict

Methods

Results

Conclusion