Abstract
This study explores the application of the VGG19 convolutional neural network (CNN) model, pre-trained on ImageNet, for the classification of rice crop diseases using image segmentation techniques. The research aims to enhance disease detection accuracy by integrating a robust deep learning framework tailored to the specific challenges of agricultural pathology. A dataset comprising 200 images categorized into four disease classes was employed to train and validate the model. Techniques such as data augmentation, dropout, and dynamic learning rate adjustments were utilized to improve model training and prevent overfitting. The model's performance was evaluated using metrics including accuracy, precision, recall, and F1-score, with a particular focus on the ability to generalize to unseen data. Results indicated a high overall accuracy exceeding 90%, showcasing the model’s capability to effectively classify rice crop diseases. Challenges such as class-specific misclassification were addressed through the model’s learning strategy, highlighting areas for further enhancement. The research contributes to the field by demonstrating the potential of using pre-trained CNN models, adapted through fine-tuning and robust training techniques, to significantly improve disease detection in crops, thereby supporting sustainable agricultural practices and enhancing food security. Future work will explore the integration of multimodal data and real-time detection systems to broaden the applicability and effectiveness of the technology in diverse agricultural settings.