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Sensors (Basel). 2018 Aug 14;18(8). pii: E2674. doi: 10.3390/s18082674.

Machine Learning in Agriculture: A Review.

Author information

1
Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece. k.liakos@certh.gr.
2
Department of Agriculture, Forestry and Food Sciences (DISAFA), Faculty of Agriculture, University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy. patrizia.busato@unito.it.
3
Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece. dmoshou@auth.gr.
4
Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. dmoshou@auth.gr.
5
Lincoln Institute for Agri-food Technology (LIAT), University of Lincoln, Brayford Way, Brayford Pool, Lincoln LN6 7TS, UK, spearson@lincoln.ac.uk. spearson@lincoln.ac.uk.
6
Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece. d.bochtis@certh.gr.

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.

KEYWORDS:

artificial intelligence; crop management; livestock management; planning; precision agriculture; soil management; water management

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