Sugarcane Leaf Disease Detection Using Deep Learning

Sugarcane is a vital cash crop grown in many tropical and subtropical regions. Its productivity, however, is significantly affected by various leaf diseases, which, if not detected early, can lead to major yield losses. Traditional disease identification methods are manual, time-consuming, and prone to inaccuracies. To overcome these limitations, this project proposes a deep learning-based system for automatic detection of sugarcane leaf diseases. Using Convolutional Neural Networks (CNNs), the model is trained on a dataset of sugarcane leaf images to accurately identify diseases such as red rot, leaf scald, and mosaic virus. The system provides efficient, real-time detection and can assist farmers in timely decision-making and disease management. This approach improves agricultural practices and contributes to sustainable crop production by integrating modern AI technologies into farming. INDEX TERMS: Sugarcane, Leaf Disease Detection, Deep Learning, CNN, Image Classification, Smart Agriculture, Crop Monitoring, AI in Farming.