Developing an eAgriculture application for identification of fungal disease in plants through leaf images

Abstract

Development of automatic disease detection and classification system is significantly explored in precision agriculture. In the past few decades, researchers have studied several cultures exploiting different parts of a plant. The symptoms of plant diseases are evident in any part of a plant, however leaves are found to be the most commonly observed one for infection identification. Researchers have thus attempted to automate the process of plant disease detection and classification using leaf images. Several works utilized computer vision technologies effectively and contributed a lot in this domain. The work presented in this study summarizes the pros and cons of all such studies to throw light on various important research aspects. A discussion on commonly studied infections and research scenario in different phases of a disease detection system is presented. The performance of state-of-the-art techniques are analyzed to identify those that seem to work well across several crops or crop categories. Discovering a set of acceptable techniques, the study presents a discussion on several points of consideration along with the future research directions. Based on the understandings gained during the survey, a computer vision based systems for plant disease detection using leaf images are developed. The main culture focused in this study is soybean due to its several benefits. A rule-based system using concepts of kmeans is designed and implemented to distinguish healthy leaves from diseased leaves. The system works with a set of rules proposed in this study. Once a leaf is identified as unhealthy, it is classified into one of the three categories (downy mildew, frog eye, and septoria leaf blight) effectively by utilizing the framed rules. The efficacy of the system is proved by performing experiments separately on various color features, texture features and their combinations. Results are generated using thousands of images collected from PlantVillage dataset.