Leaf Classification For Sustainable Agriculture and In Depth species Analysis

The proposed leaf classification and disease detection system using Convolutional Neural Networks (CNN) provides an efficient and accurate solution for sustainable agriculture. The system successfully identifies different plant species and detects leaf diseases through image analysis techniques. By using deep learning algorithms, the proposed model improves classification accuracy and reduces the limitations of traditional manual disease identification methods.The system helps farmers and agricultural experts monitor plant health at an early stage, thereby reducing crop losses and improving agricultural productivity. The inclusion of in-depth species analysis provides additional information about plant characteristics, disease symptoms, and preventive measures for better crop management. Experimental results demonstrate that the CNN-based approach achieves high accuracy and reliable performance in leaf classification and disease prediction.Overall, the proposed system supports smart farming practices and contributes to sustainable agriculture by providing an automated, time-efficient, and user-friendly solution for plant health monitoring.

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