Detection and Classification of Rice Plant Diseases using Image Processing Techniques

Abstract

Plant leaf disease detection and classification is one of the interesting research areas in the agriculture sector. An approach based on image processing and machine learning techniques is proposed in this paper to detect and classify paddy leaf diseases. Leaf Blast, Brown Spot and Bacterial Leaf Blight diseases are considered to calculate the performance of this proposed methodology. Colour thresholding is applied to identify the disease area in the paddy leaf. Hence, various feature categories such as colour, texture, and shape features are extracted from the affected area of the diseased image. Support Vector Machine (SVM) and k-nearest neighbors (k- NN) algorithms are used as classifiers and the performances of the proposed methodology are evaluated using these classifiers. The experimental results are compared with state-of-the-art work approaches. Our proposed approach achieves 89.19%, 82.86%, and 89.19% of accuracy for detecting the rice plant diseases such as leaf blight, brown spot and leaf blast respectively.