Ramadan, Syed Taha Yeasin and Sakib, Tanjim and Farid, Fahmid Al and Islam, Md Shofiqul and Junaidi, Abdullah and Bhuiyan, Md Roman and Mansor, Sarina and Hezerul, Abdul Karim (2024) Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset. IEEE Access, 12. pp. 69853-69874. ISSN 2169-3536. (Published)
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Abstract
Global food security is seriously threatened by wheat leaf disease, which makes effective and precise disease detection and classification techniques necessary. For efficient disease control and the best possible crop health, timely identification and precise classification are essential. However, the limited availability of datasets for wheat leaf diseases hinders the development of effective and robust classification models. This research emphasizes the importance of precise wheat leaf disease diagnosis for global food security. The existing methods face challenges with limited data and computational demands. The research explores the potential of deep learning for automated disease detection, considering these challenges. CycleGAN proved to be the most effective among various augmentation techniques, enhancing the performance of classifiers DenseNet121, ResNet50V2, DenseNet169, Xception, ResNet152V2, and MobileNetV2. ADASYN also significantly improved classification accuracy, with MobileNetV2 consistently outperforming across different augmentation methods. This technique excels in overcoming challenges posed by limited datasets and class imbalances. Using CycleGAN for data augmentation notably enhanced classifier performance, addressing the scarcity of real-world samples. Evaluation through confusion matrix analysis revealed a minimal number of misclassified images - possibly as low as 0 to 3 images over the test dataset. The exceptional 100% accuracy achieved by the MobileNetV2 model on both CycleGAN and ADASYN augmented datasets highlights the potential of these techniques to unlock new levels of accuracy in wheat disease classification. This augmentation technique fine-tuned the classifier, reducing errors and highlighting the crucial role of CycleGAN in enhancing the accuracy and precision of wheat disease classification models. The proposed method establishes CycleGAN's effectiveness in augmenting wheat leaf disease classification and recognizes ADASYN's potential. The developed technique shows promise for automated disease detection in agriculture, enhancing global food security. Future research may optimize computational efficiency and explore integrating emerging technologies such as edge computing.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Accuracy; ADASYN; Agricultural automation; Augmentation techniques; CycleGAN; Deep learning; Disease diagnosis; SMOTE; SMOTETomek; Wheat leaf disease |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 31 Jul 2024 01:52 |
| Last Modified: | 31 Jul 2024 01:52 |
| URI: | http://umpir.ump.edu.my/id/eprint/41594 |
| Download Statistic: | View Download Statistics |
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