On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification

Kanaan M. , Akay R., Baykara C. K.

in: Precision Agriculture Technologies for Food Security and Sustainability, Sherine M. Abd El-Kader and Basma M. Mohammad El-Basioni, Editor, IGI Global, Pennsylvania, pp.166-186, 2020

  • Publication Type: Book Chapter / Chapter Research Book
  • Publication Date: 2020
  • Publisher: IGI Global
  • City: Pennsylvania
  • Page Numbers: pp.166-186
  • Editors: Sherine M. Abd El-Kader and Basma M. Mohammad El-Basioni, Editor


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.