Deep Learning-Based Detection and Classification of Rice Diseases Using Residual Networks (ResNet50)

Timely detection and classification of crop diseases are essential for maintaining agricultural productivity as well as the quality of food. Many traditional disease identification methods are labor-intensive, time-consuming, and human-error- prone. Recently developed techniques based on computer vision and deep learning add efficient, automated alternatives for disease detection. This work proposes a deep learning-based system utilizing a Residual Network (ResNet50) for automatically diagnosing and classifying rice diseases, a specialized Convolu- tional Neural Network (CNN) form. Rice, ranking third after wheat and maize in global cereal production, is critical for food security. The proposed ResNet50-based method outperforms all aspects of existing processes in accuracy, recognizing and categorizing multiple rice disease types. Experimental results reveal that the system leads to early diagnosis and intervention, eventually leading to better crop management, higher yield, and enhanced grain production quality. In addition, the proven robust performance of the model signifies its real-world applicability for agricultural purposes. Thus, integrating deep learning techniques such as ResNet50 into agricultural disease management practices will contribute to more sustainable farming practices and lower crop loss outcomes. This research underscores the transforma- tional potential of AI in precision agriculture and global food security sustainability.