Silk Leaf Disease Detection Using Convolutional Neural Networks

The production of silk is essential to the agricultural economy, especially in nations like India. However, leaf diseases in silk plants can cause major losses in both yield and quality. This research proposes a machine learning-based approach for automatic silk leaf disease detection using image processing and Convolutional Neural Networks (CNNs). The system processes leaf images and identifies diseases with high accuracy, providing real-time feedback through a user interface. The model was trained on a labeled dataset of multiple silk leaf diseases and achieved a classification accuracy exceeding 92%. This approach supports early diagnosis, enabling timely interventions, reducing pesticide usage, and promoting precision farming.