Paddycrops Disease Prediction Using Image Processing by Using 3DCNN

Paddy plant diseases seriously displace agricultural production, thereby causing heavy economic losses. An effective response to any disease management would depend upon the timely and accurate diagnosis of some of the common diseases, such as leaf smut, bacterial leaf blight, and brown spot. However, conventional means of manual detection are generally laborious, time-consuming, and may invite errors. The proposal for an automated paddy disease detection system with deep learning and high-resolution image analysis aims to address these very challenges. Since the feature extraction and classification of infected paddy leaves are carried out through a 3D Convolutional Neural Network (3DCNN), image preprocessing techniques were utilized to enhance image visibility before modeling and training. Its performance of over 99.80% accuracy has surpassed that of conventional methods by a margin. This research offers a precision agriculture tool that provides disease detection with enhanced reliability, scalability, and efficiency. The findings validate the role of deep-learning technologies in diagnostic capabilities for agriculture and are increasingly helping early recognition of diseases to assist with subsequent management decisions.