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.
Highlights
Agriculture plays a critical role in the global economy
In the first study [91], authors presented a new method based on counter propagation (CP)-Artificial neural networks (ANNs) and multispectral images captured by unmanned aircraft systems (UAS) for the identification of Silybum marianum, a weed that is hard to eradicate and causes major loss on crop yield
In the first article [98], a method is presented for the classification of cattle behaviour based on Machine learning (ML) models using data collected by collar sensors with magnetometers and three-axis accelerometers
Summary
Agriculture plays a critical role in the global economy. Pressure on the agricultural system will increase with the continuing expansion of the human population. The data generated in modern agricultural operations is provided by a variety of different sensors that enable a better understanding of the operational environment (an interaction of dynamic crop, soil, and weather conditions) and the operation itself (machinery data), leading to more accurate and faster decision making. Machine learning (ML) has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments. The structure of the present work is as follows: the ML terminology, definition, learning tasks, and analysis are initially given, along with the most popular learning models and algorithms. Because of the large number of abbreviations used in the relative scientific works, Tables 1–4 list the abbreviations that appear in this work, categorized to ML models, algorithms, statistical measures, and general abbreviations, respectively
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