Tomato Leaf Disease Detection using K-Means, SVM Classifier & Neural Networks

Abstract

Agriculture is that the mainstay of the Indian economy. Nearly 56% of individuals depend on it & shares major a neighborhood of the Gross domestic product. Out of that tomato is one among the simplest common food crops in Asian nation. Diseases in crops wholly on the leaves affects on the reduction of every quality and quantity of agricultural merchandise. Perception of human eye is not such a great deal stronger so on observe minute variation inside the infected a part of leaf. Throughout this paper providing software package resolution to automatically observe and classify plant leaf diseases. Throughout this we have a tendency to area unit exploitation image method techniques to classify malady’s & quickly designation are administrated as per disease. This approach will enhance productivity of crops. Throughout this project four leaf diseases area unit supported. It includes several steps wise image acquisition, image pre-processing [9], segmentation, options extraction, K-means, neural network & SVM classification. The look and implementation of Otsu segmentation technologies area unit absolutely automatic and it provides accumulated productivity. For tremendous use of chemical and to scale back the economic loss, the identification of disease severity is main issue. Inside the context of sensible farming, we address the challenge of event IOT with Raspberry pi and sensors with image method to reinforce the efficiency of the agriculture.