Staff View: Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset

Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset

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, t...

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Main Authors:	Ramadan, Syed Taha Yeasin, Sakib, Tanjim, Farid, Fahmid Al, Islam, Md Shofiqul, Junaidi, Abdullah, Bhuiyan, Md Roman, Mansor, Sarina, Hezerul, Abdul Karim
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2024
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/41594/1/Improving%20wheat%20leaf%20disease%20classification_Evaluating%20augmentation.pdf http://umpir.ump.edu.my/id/eprint/41594/ https://doi.org/10.1109/ACCESS.2024.3397570 https://doi.org/10.1109/ACCESS.2024.3397570
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spelling	my.ump.umpir.415942024-07-31T01:52:46Z http://umpir.ump.edu.my/id/eprint/41594/ Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset Ramadan, Syed Taha Yeasin Sakib, Tanjim Farid, Fahmid Al Islam, Md Shofiqul Junaidi, Abdullah Bhuiyan, Md Roman Mansor, Sarina Hezerul, Abdul Karim QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) 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. Institute of Electrical and Electronics Engineers Inc. 2024 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/41594/1/Improving%20wheat%20leaf%20disease%20classification_Evaluating%20augmentation.pdf 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) https://doi.org/10.1109/ACCESS.2024.3397570 https://doi.org/10.1109/ACCESS.2024.3397570
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Ramadan, Syed Taha Yeasin Sakib, Tanjim Farid, Fahmid Al Islam, Md Shofiqul Junaidi, Abdullah Bhuiyan, Md Roman Mansor, Sarina Hezerul, Abdul Karim Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset
description	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.
format	Article
author	Ramadan, Syed Taha Yeasin Sakib, Tanjim Farid, Fahmid Al Islam, Md Shofiqul Junaidi, Abdullah Bhuiyan, Md Roman Mansor, Sarina Hezerul, Abdul Karim
author_facet	Ramadan, Syed Taha Yeasin Sakib, Tanjim Farid, Fahmid Al Islam, Md Shofiqul Junaidi, Abdullah Bhuiyan, Md Roman Mansor, Sarina Hezerul, Abdul Karim
author_sort	Ramadan, Syed Taha Yeasin
title	Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset
title_short	Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset
title_full	Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset
title_fullStr	Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset
title_full_unstemmed	Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset
title_sort	improving wheat leaf disease classification : evaluating augmentation strategies and cnn-based models with limited dataset
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2024
url	http://umpir.ump.edu.my/id/eprint/41594/1/Improving%20wheat%20leaf%20disease%20classification_Evaluating%20augmentation.pdf http://umpir.ump.edu.my/id/eprint/41594/ https://doi.org/10.1109/ACCESS.2024.3397570 https://doi.org/10.1109/ACCESS.2024.3397570
_version_	1822924550020530176
score	13.232414

Improving wheat leaf disease classification : Evaluating augmentation strategies and CNN-Based models with limited dataset

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