Using Pretrained VGG19 Model and Image Segmentation for Rice Leaf Disease Classification
Beissenova G. Madiyarova A. Aliyeva A. Mambetaliyeva G. Koshkarov Y. Sarsenbiyeva N. Chazhabayeva M. Seidaliyeva G.
2024Science and Information Organization
International Journal of Advanced Computer Science and Applications
2024#15Issue 8743 - 752 pp.
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.
convolutional neural networks , data augmentation , disease classification , image segmentation , model generalization , Rice crop diseases , sustainable farming , VGG19 model
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M. Auezov South Kazakhstan University, Shymkent, Kazakhstan
Caspian University of Technology and Engineering named after Sh. Yessenov, Aktau, Kazakhstan
South Kazakhstan Pedagogical University named after Ozbekali Zhanibekov, Shymkent, Kazakhstan
Astana IT University, Astana, Kazakhstan
Kazakh National Agrarian Research University, Almaty, Kazakhstan
M. Auezov South Kazakhstan University
Caspian University of Technology and Engineering named after Sh. Yessenov
South Kazakhstan Pedagogical University named after Ozbekali Zhanibekov
Astana IT University
Kazakh National Agrarian Research University
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