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

On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification
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Author(s): Muzaffer Kanaan (Erciyes University, Turkey), Rüştü Akay (Erciyes University, Turkey) and Canset Koçer Baykara (Turkish Grain Board (TMO), Turkey)
Copyright: 2021
Pages: 21
Source title: Precision Agriculture Technologies for Food Security and Sustainability
Source Author(s)/Editor(s): Sherine M. Abd El-Kader (Electronics Research Institute, Cairo, Egypt) and Basma M. Mohammad El-Basioni (Electronics Research Institute, Cairo, Egypt)
DOI: 10.4018/978-1-7998-5000-7.ch007

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Abstract

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.

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