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DOI: 10.14569/IJACSA.2024.0150873
PDF

Using Pretrained VGG19 Model and Image Segmentation for Rice Leaf Disease Classification

Author 1: Gulbakhram Beissenova
Author 2: Almira Madiyarova
Author 3: Akbayan Aliyeva
Author 4: Gulsara Mambetaliyeva
Author 5: Yerzhan Koshkarov
Author 6: Nagima Sarsenbiyeva
Author 7: Marzhan Chazhabayeva
Author 8: Gulnara Seidaliyeva

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 8, 2024.

  • Abstract and Keywords
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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.

Keywords: Rice crop diseases; convolutional neural networks; VGG19 model; image segmentation; disease classification; data augmentation; model generalization; sustainable farming

Gulbakhram Beissenova, Almira Madiyarova, Akbayan Aliyeva, Gulsara Mambetaliyeva, Yerzhan Koshkarov, Nagima Sarsenbiyeva, Marzhan Chazhabayeva and Gulnara Seidaliyeva, “Using Pretrained VGG19 Model and Image Segmentation for Rice Leaf Disease Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150873

@article{Beissenova2024,
title = {Using Pretrained VGG19 Model and Image Segmentation for Rice Leaf Disease Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150873},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150873},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Gulbakhram Beissenova and Almira Madiyarova and Akbayan Aliyeva and Gulsara Mambetaliyeva and Yerzhan Koshkarov and Nagima Sarsenbiyeva and Marzhan Chazhabayeva and Gulnara Seidaliyeva}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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