Design and Evaluation of Plant Leaf Disease Detection Based on the CNN Classification System

To increase agricultural growth, there is a requirement for the detection of plant leaves in the starting stage. In agriculture, disease detection in plants is an important task and having diseases is quite a common thing. To identify the diseases in plants, a continuous observation is needed, which consumes time and necessitates significant human efforts. Programming technique is used to solve this problem and make it as simple as possible. This chapter proposes the design and evaluation of plant leaf disease detection based on the convolution neural network (CNN) classification system, which contributes to a safe, accurate, and reliable system for disease detection in leaves. Here, k-means clustering algorithm and Gray Level Co-occurrence Matrix (GLCM) are used for feature extraction. The CNN Classification technique is used for leaf disease detection, and the accuracy rate is calculated. The leaf disease detection based on CNN presented in this chapter is compared with existing methods and parameters in the result analysis. This system differentiates diseases in plants and provides accurate classification and compares it with existing methods.