Enhancing potato crop yield with AI-powered CNN-based leaf disease detection and tracking

Abstract

While plant diseases continue to have a severe impact on food production, farmers face a formidable challenge in trying to meet the escalating demands of a population that is expanding quickly for agricultural items like potatoes. Despite spending billions on disease management, farmers frequently struggle to effectively control disease without the aid of cutting-edge technology. The paper examines a disease diagnosis method based on deep learning. To be more precise, it uses a Convolutional Neural Network (CNN) method for the disease's detection and classification. This study examines the impact of data augmentation while conducting an extensive performance evaluation of the hyper-parameter in the setting of detecting plant diseases with a focus on potatoes. The experimental findings demonstrate the effectiveness of the suggested model's 98% accuracy. Considering growing global issues, this research aims to open new pathways for more efficient plant disease management and, eventually, increase agricultural output.