Identification of chilli leaf disease using contrast limited histogram equalisation and k-means clustering

Abstract

Plant disease diagnosis is crucial for preventing productivity and quality losses in agricultural products. Because plants are continually attacked by insects, bacterial infections, and smaller scale organisms it is necessary for early diagnosis disease control is a vital part of profitable chilli crop production, hence early diagnosis of disease identification is an important aspect of crop management. This paper discusses strategies for detecting disease effectively in order to improve chilli plant product quality. An image processing technique based on identification of chilli leaf disease using contrast limited histogram equalisation and k-means clustering (KMC). The approach was carried out in five stages: acquiring the image, preprocessing, extracting features, classifying the diseases, and showing the outcome. This work offers a thorough implementation of CLAHE for preprocessing, k-means cluster for feature extraction and support vector machine (SVM) for classification of chilli leaf diseases. The accuracy was tested for standard chilli dataset for major 2 types of diseases including anthracnose and bacterial blight form kaggle dataset with varying samples of 70:30 and 60:40 respectively and it is observed that the average accuracy improved to 98% compared to existing techniques.