Machine Learning in Agriculture: A Review

Konstantinos Liakos; Patrizia Busato; Dimitrios Moshou; Simon Pearson; Dionysis Bochtis

doi:10.3390/s18082674

journal article Open Access Aug 14, 2018

Machine Learning in Agriculture: A Review

Konstantinos Liakos Patrizia Busato Dimitrios Moshou Simon Pearson

Dionysis Bochtis

Sensors Vol. 18 No. 8 pp. 2674 · MDPI AG

View at Publisher Save 10.3390/s18082674

Abstract

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

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Details

Published: Aug 14, 2018
Vol/Issue: 18(8)
Pages: 2674
License: View

Authors

K

Konstantinos Liakos

Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology—Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece

P

Patrizia Busato

Department of Agriculture, Forestry and Food Sciences (DISAFA), Faculty of Agriculture, University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy

D

Dimitrios Moshou

Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology—Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

S

Simon Pearson

Lincoln Institute for Agri-food Technology (LIAT), University of Lincoln, Brayford Way, Brayford Pool, Lincoln LN6 7TS, UK

D

Dionysis Bochtis

Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology—Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece

Cite This Article

Konstantinos Liakos, Patrizia Busato, Dimitrios Moshou, et al. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674

Machine Learning in Agriculture: A Review

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