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This study presents an AI-based model utilizing deep learning techniques for the automated detection of sugarcane diseases, significantly improving agricultural productivity. The model, trained on a dataset of 8,456 images, achieved a high accuracy of 98% using the VGG16 architecture, and is integrated into a user-friendly web application that provides real-time disease predictions and agricultural guidance. The research highlights the potential of deep learning in enhancing early disease detection and prom

Author

Lohit Dalal

Language

EN