Precision Agriculture Technologies for Food Security and Sustainability, Sherine M. Abd El-Kader and Basma M. Mohammad El-Basioni, Editör, IGI Global, Pennsylvania, ss.166-186, 2020
The use of technology for the purpose of improving crop yields, quality and quantity of the harvest, as
well as maintaining the quality of the crop against adverse environmental elements (such as rodent or
insect infestation, as well as microbial disease agents) is becoming more critical for farming practice
worldwide. One of the technology areas that is proving to be most promising in this area is artificial
intelligence, or more specifically, machine learning techniques. This chapter aims to give the reader an
overview of how machine learning techniques can help solve the problem of monitoring crop quality and
disease identification. The fundamental principles are illustrated through two different case studies, one
involving the use of artificial neural networks for harvested grain condition monitoring and the other
concerning crop disease identification using support vector machines and k-nearest neighbor algorithm.