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Review

Machine Learning in Agriculture: A Comprehensive Updated Review

1
Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece
2
Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy
3
FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Academic Editors: Giovanni Agati and Asim Biswas
Sensors 2021, 21(11), 3758; https://doi.org/10.3390/s21113758
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. View Full-Text
Keywords: machine learning; crop management; water management; soil management; livestock management; artificial intelligence; precision agriculture; precision livestock farming machine learning; crop management; water management; soil management; livestock management; artificial intelligence; precision agriculture; precision livestock farming
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MDPI and ACS Style

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. https://doi.org/10.3390/s21113758

AMA Style

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. https://doi.org/10.3390/s21113758

Chicago/Turabian Style

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. https://doi.org/10.3390/s21113758

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