Machine Learning in Agriculture: A Review

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.

Authors: Konstantinos G. Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson and Dionysis Bochtis
 
Journal: Sensors 2018, 18(8), 2674
 
Publisher: MDPI 
 
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. 
 
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).
 

Illustration Photo: The International Institute of Tropical Agriculture (IITA) and Pennsylvania State University, USA, have collaborated to develop and launch Nuru, an Artificially Intelligent Assistant. Nuru (Swahili for light) uses machine learning to accurately recognize leaves damaged by two important viral diseases of cassava (Cassava Mosaic Disease and Cassava Brown Streak Disease) as well as damage by red and green mites. (credit: IITA)

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