A COLOR AND TEXTURE BASED APPROACH FOR THE DETECTION AND CLASSIFICATION OF PLANT LEAF DISEASE USING KNN CLASSIFIER

Abstract

Modern organic farming is gaining popularity in the agriculture of many developing countries. There are many problems arise in farming due to various environmental factors and among these plant leaf disease is considered to be the strongest factor that causes the deficit of agricultural product quality. The goal is to mitigate this issue through computer vision and machine learning technique. This paper proposed a technique for plant leaf disease detection and classification using K-nearest neighbor (KNN) classifier. The texture features are extracted from the leaf disease images for the classification. In this work, KNN classifier will classify the diseases like alternaria alternata, anthracnose, bacterial blight, leaf spot, and canker of various plant species. The proposed approach can successfully detect and recognize the selected diseases with 96.76% accuracy.

Existing System

YcbCr, CIELAB and HIS

Proposed System

The proposed method is divided into two phases: the training phase and the testing phase. The training and testing phases comprise of five fundamental stages which are image acquisition, color conversion, color segmentation, morphological operation, and feature extraction. The dataset consists of five different types of plant leaf disease image. In training phase, the extracted feature of the segmented plant leaf images is used for the training of the classifier. After the creation of the trained model the testing phase takes an input image and complete all the processing steps on the image up to feature extraction. The new features are given to the classifier model for performing the comparison to give the correct recognition of the disease. Different plant leaves images have bee tested, to be classified into five classes- alternaria alternata, anthracnose, bacterial blight, leaf spot and citrus canker affected.

Architecture

BLOCK DIAGRAM