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AI-Driven Sugarcane Leaf Disease Detection with Flask App using Machine Learning by Vidya Mahesh Huddar Link to download the project: https://vtupulse.com/product/ai-driven-sugarcane-diseases-detection-remedy-suggestion-with-flask-app-final-year-project/ Download All Projects: https://vtupulse.com/ Introduction: Sugarcane (Saccharum officinarum) is a major commercial crop impacted by leaf diseases like Red Rot and Red Rust, causing significant yield loss. Traditional diagnosis is slow and error-prone. This project applies AI—combining deep learning (DenseNet201) and machine learning (SVM)—to automatically detect and classify sugarcane leaf diseases from images. It also integrates actionable remedies with purchase links via a simple web app. Sugarcane Leaf Disease Detection – Methodology 1. Dataset collection 2. Data Augmentation 3. Preprocessing 4. Feature Extraction 5. Classification 6. Training 7. Deployment Sugarcane Leaf Disease Detection – Results Classification report shows ~94% precision, recall, and F1-score overall. Healthy leaves were classified most accurately; some overlap between Red Rot and Red Rust due to symptom similarity. Compared to baseline CNN models (78-91% accuracy), DenseNet201+SVM achieved superior generalization. Data augmentation was key to performance on small datasets. Web app enables real-time use by farmers, bridging diagnosis to action. Download Final Year Projects: https://vtupulse.com/download-final-year-projects/ Final Year Projects: https://www.youtube.com/watch?v=o0PdqzRTy1g&list=PL4gu8xQu0_5ILky1wSEzplFroALOJyKdY ******************************** Follow Us on: 1. Blog / Website: https://www.vtupulse.com/ 2. Download Final Year Project Source Code: https://vtupulse.com/download-final-year-projects/ 3. Like Facebook Page: https://www.facebook.com/VTUPulse 4. Follow us on Instagram: https://www.instagram.com/vtupulse/ 5. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates
