• Mr. T.Rajan Babu
• S.Bharathapriyan
• C.Savin Sangeerthan
• G. Parthasarathi

Potato cultivation faces significant threats from various diseases, leading to substantial yield losses worldwide. Timely detection and effective prevention of these diseases are crucial for ensuring food security and sustainable agricultural practices. In this study, a novel approach is proposed for the automatic detection and prevention of potato plant diseases using a Convolutional Neural Network (CNN), specifically the ResNet-101 architecture. By analyzing leaf images, the ResNet-101 accurately identifies common diseases like late blight, early blight, and potato virus Y (PVY). Training on a large dataset with data augmentation enhances the model's generalization and robustness. Integrating the disease detection system with a preventive management framework enables real-time monitoring and early intervention. Extensive experiments validate the effectiveness of this approach, showing superior performance compared to traditional methods. These findings highlight the potential of CNN-based models in transforming potato plant disease management, contributing to sustainable agriculture and increased crop productivity.

CNN, Deep learning, Convolutional layer and Potato disease

"EMPOWERING AGRICULTURE: RESNET 101 DRIVEN DIAGNOSIS FOR ENHANCED POTATO LEAF DISEASES DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 12, page no.121-130, December-2020, Available :http://www.jetir.org/papers/JETIR2012421.pdf

"EMPOWERING AGRICULTURE: RESNET 101 DRIVEN DIAGNOSIS FOR ENHANCED POTATO LEAF DISEASES DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 12, page no. pp121-130, December-2020, Available at : http://www.jetir.org/papers/JETIR2012421.pdf