Disease detection and diagonsis system for Grand Nain and Elaichi Banana plants

ELECTRONICS & COMMUNICATION ENGINEERING

Banana cultivation, particularly of the Grand Nain and Elaichi varieties, is vital to the agricultural economy in many tropical and subtropical regions. However, diseases such as Panama Wilt, Sigatoka, and Bunchy Top Virus pose serious threats to crop yield and quality.This project presents an intelligent disease detection and diagnosis system aimed at early identification and classification of banana plant diseases in Grand Nain and Elaichi cultivars using image processing and machine learning techniques. High-resolution images of banana leaves and pseudostems are captured and analyzed to identify visual symptoms like lesions, discoloration, or deformities. A convolutional neural network (CNN) is developed and optimized using a supervised learning approach on an annotated dataset of diseased and healthy banana plant images to ensure accurate recognition. The system provides real-time diagnostic feedback and disease severity ratings, enabling farmers to take timely and effective treatment measures. This approach enhances precision farming, reduces crop loss, and supports sustainable agriculture practices by minimizing unnecessary pesticide.

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