Machine Learning in Agriculture: A Review

Liakos, Konstantinos and Busato, Patrizia and Moshou, Dimitrios and Pearson, Simon and Bochtis, Dionysis (2018) Machine Learning in Agriculture: A Review. Sensors, 18 (8). p. 2674. ISSN 1424-8220

Full content URL: http://doi.org/10.3390/s18082674

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Machine Learning in Agriculture: A Review
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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

Additional Information:This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Keywords:crop management, water management, soil management, livestock management, artificial intelligence, planning, agriculture
Subjects:D Veterinary Sciences, Agriculture and related subjects > D400 Agriculture
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > National Centre for Food Manufacturing > Lincoln Institute for Agri-Food Technology
ID Code:33015
Deposited On:23 Aug 2018 08:04

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