Leaf Disease Detection and Classification

Abstract

The notion of smart farming is gaining traction in the agricultural industry these days, and it makes use of sensors and a variety of machine learning based technologies. According to recent surveys, 56 percent of the agricultural industry is facing significant losses because of diseases developing on plant leaves. It's critical to keep track of the disease's spread and enhance agricultural yields. To prevent the disease from spreading, we must first recognize it on time and prevent it. As a result, we may solve this problem by putting in place some algorithms for detecting sickness on leaves. This paper presents a comparative analysis between support vector machines (SVM) model, K-Nearest Neighbor (KNN) model and convolution neural network (CNN) model. The three different models are presented and examined in this research, and they can detect eight different leaf diseases. The CNN model has achieved an accuracy of 96 percent when trained with the images of soyabean leaf disease dataset, outperforms the KNN and SVM models, which have accuracy of 64 percent and 76 percent, respectively