A Deep Learning Based Model for Plant Disease Detection - Haris, Mohd; Tripathi, Ashish; Pandey, Sudhans Shekhar; Tiwari, Himanshu; Ansari, Ashar Ahmad (Journal of Neonatal Surgery, 2025)
Plant diseases present increasing threats to agriculture which require precise early disease detection systems and powerful management solutions to preserve crop health and maximize productivity. This research studies the application of CNNs and combines it with ML and DL while investigating their capability to identify plant diseases accurately. Through precise detection capability the system incorporates Artificial Intelligence (AI)-based treatment recommendations which suggest optimal solutions. These innovative technologies enable better agricultural decisions which result in higher crop production with lower losses. Nothing works as effectively toward plant disease management as the early and accurate identification of diseases. Traditional expansive plant disease assessment requires specialized personnel and operates at slow speeds which restricts its practical use on extensive areas. This research develops a computing solution which combines image processing techniques and machine learning for plant disease detection automation. The system performs feature extraction through texture analysis alongside color attribute examination and shape descriptor evaluation applied to images containing healthy and infected plant leaves. A dataset made up of healthy and diseased plant leaf images supports the proposed system that utilizes feature extraction through texture analysis with color properties combined with shape descriptors. A proposed CNN is used on plant leaves to identify healthy versus diseased classes, producing an average accuracy of 94.65% on 14 types of plants having 38 types of diseases.