Application of Machine Learning in Plant Disease Detection and Classification

Plant disease detection and monitoring is a crucial issue that needs to be addressed to increase agricultural productivity and ensure global food security. Conventional visual detection and monitoring methods of plant diseases are tedious and costly and require expert knowledge for accurate decisions. Machine learning and deep learning methods are gaining popularity among researchers as automatic disease detection methods which can provide quick and accurate disease detection and overcome several limitations of the traditional methods. This chapter covers the basic concepts of machine learning and deep learning, followed by the general method for detecting and monitoring plant diseases. The chapter also covers the basics of some recent and popular machine learning and deep learning classification methods, including support vector machine, K-nearest neighbours, random forest, artificial neural network, and convolutional neural network. The methods for assessing the machine learning models and their performances for agricultural crop disease detection were also covered. At the end, the major challenges in developing machine learning models for plant disease detection are presented.