Fuzzy and Neural Network based Tomato Plant Disease Classification using Natural Outdoor Images

Abstract

Objectives: The aim of the study is to automate the plant disease recognition and classification process by using image processing and soft computing techniques. Methods/Analysis: The proposed method examined the five types of tomato plant diseases using natural outdoor images in the study. The tomato plant images categorized into six categories including five disease infected that are bacterial leaf spot, fungal septoria leaf spot, bacterial canker, fungal lateblight, tomato leaf curl and one non-infected (healthy). The total 180 images of the dataset used for training and testing purpose. The total thirteen features computed by using CIE XYZ color space conversions that included color moments, histogram, and color coherence vector features. For classification, computed features are fed into three classifiers, i.e., “Fuzzy Inference System based on subtractive clustering”, “Adaptive neuro-fuzzy inference system using hybrid learning algorithm and multi-layer feed forward back propagation neural network” for classification of six injured and healthy tomato plant disease. Finding: The classification accuracy is best yielded with multi-layer feed forward back propagation classifier of 87.2%. Novelty/Improvement: Usually, in the studies the only one type of plant disease considered for the recognition and classification purpose. The current study considered five different types of tomato plant diseases including fungal, bacterial and viral. It indicates that the proposed algorithm could reliably classify the different types of plant diseases in digital images.