Plant Disease Classification Using Image Processing Technique

Abstract

Agriculture remains pivotal to our economy, with farming playing a central role in revenue generation. Challenges such as pests, plant diseases, and evolving climate patterns pose threats to crop yield and production. Addressing these challenges, timely and accurate detection of plant diseases emerges as imperative. Manual detection, however, remains resource-intensive and often lags. Addressing this gap, this project proposes an innovative image processing-based system for rapidly detecting plant diseases. The system proficiently identifies specific diseases by analyzing images of plant leaves against a curated dataset. The emphasis of this study was on three major diseases: Bacterial Blight (with an accuracy of 98.6%), Alternaria Alternata (98.5714%), and Cercospora Leaf Spot (97.5%). The compelling results underline the system's capacity to swiftly and effectively categorize diseases, offering monoculture farmers an indispensable tool for obtaining prompt, disease-specific insights.