A Novel Smart Approach to Plant Health - Automated Detection and Diagnosis of Leaf Diseases

Plant diseases pose a significant threat to global food security, resulting in substantial crop losses. This research study introduces an advanced plant disease prediction system leveraging Convolutional Neural Networks (CNN), ResNet, and YOLOv7-X algorithms for accurate diagnosis. Despite recent advancements, challenges persist in capturing complex disease patterns. The proposed architecture, specifically designed for plant disease identification, aims to address these challenges. Extensive datasets, preprocessing techniques, and meticulous hyperparameter tuning enhance the model's accuracy and robustness. Evaluation on an independent test dataset demonstrates the superior performance of the YOLOv7-X algorithm in predicting plant diseases. This study not only highlights existing challenges in plant disease prediction but also offers a comprehensive solution for improved accuracy and effectiveness in disease diagnosis.