A novel pixel replacement-based segmentation and double feature extraction techniques for efficient classification of plant leaf diseases

Abstract

Diseases in plants accounts to major crop losses which affects the economy of farmers and in turn the country. Recent advancements in technologies like Internet of Things and Machine learning has led to the development of automated systems for crucial applications. This paper focuses on developing such a system for agricultural application mainly for early detection of diseases in plant leaves and provides solutions to the farmers. The main contribution is the novel pixel replacement-based segmentation and double feature extraction techniques for enhancing classification process. Classification is done using Support vector machine classifier and the developed system was validated using pomegranate leaves. The system is evaluated using performance metrics such as detection accuracy and classification accuracy.