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.

Machine learning in agriculture: A review

Busato, Patrizia;Bochtis, Dionysis
2018-01-01

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.
2018
18
8
2674
2702
http://www.mdpi.com/1424-8220/18/8/2674/pdf
Artificial intelligence; Crop management; Livestock management; Planning; Precision agriculture; Soil management; Water management; Analytical Chemistry; Atomic and Molecular Physics, and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering
Liakos, Konstantinos G.; Busato, Patrizia; Moshou, Dimitrios; Pearson, Simon; Bochtis, Dionysis*
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1679884
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