Deep learning and explainable AI for classification of potato leaf diseases
The accurate classification of potato leaf diseases plays a pivotal role in ensuring the health and productivity of crops. This study presents a unified approach for addressing this challenge by leveraging the power of Explainable AI (XAI) and transfer learning within a deep Learning framework. In t...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1449329/full |
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| author | Sarah M. Alhammad Doaa Sami Khafaga Walaa M. El-hady Farid M. Samy Khalid M. Hosny |
| author_facet | Sarah M. Alhammad Doaa Sami Khafaga Walaa M. El-hady Farid M. Samy Khalid M. Hosny |
| author_sort | Sarah M. Alhammad |
| collection | DOAJ |
| description | The accurate classification of potato leaf diseases plays a pivotal role in ensuring the health and productivity of crops. This study presents a unified approach for addressing this challenge by leveraging the power of Explainable AI (XAI) and transfer learning within a deep Learning framework. In this research, we propose a transfer learning-based deep learning model that is tailored for potato leaf disease classification. Transfer learning enables the model to benefit from pre-trained neural network architectures and weights, enhancing its ability to learn meaningful representations from limited labeled data. Additionally, Explainable AI techniques are integrated into the model to provide interpretable insights into its decision-making process, contributing to its transparency and usability. We used a publicly available potato leaf disease dataset to train the model. The results obtained are 97% for validation accuracy and 98% for testing accuracy. This study applies gradient-weighted class activation mapping (Grad-CAM) to enhance model interpretability. This interpretability is vital for improving predictive performance, fostering trust, and ensuring seamless integration into agricultural practices. |
| format | Article |
| id | doaj-art-9a756c5d96a543e4ac1286873fe0cbed |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-9a756c5d96a543e4ac1286873fe0cbed2025-02-03T06:33:23ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-02-01710.3389/frai.2024.14493291449329Deep learning and explainable AI for classification of potato leaf diseasesSarah M. Alhammad0Doaa Sami Khafaga1Walaa M. El-hady2Farid M. Samy3Khalid M. Hosny4Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, EgyptDepartment of Horti Culture, Faculty of Agriculture, Zagazig University, Zagazig, EgyptDepartment of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, EgyptThe accurate classification of potato leaf diseases plays a pivotal role in ensuring the health and productivity of crops. This study presents a unified approach for addressing this challenge by leveraging the power of Explainable AI (XAI) and transfer learning within a deep Learning framework. In this research, we propose a transfer learning-based deep learning model that is tailored for potato leaf disease classification. Transfer learning enables the model to benefit from pre-trained neural network architectures and weights, enhancing its ability to learn meaningful representations from limited labeled data. Additionally, Explainable AI techniques are integrated into the model to provide interpretable insights into its decision-making process, contributing to its transparency and usability. We used a publicly available potato leaf disease dataset to train the model. The results obtained are 97% for validation accuracy and 98% for testing accuracy. This study applies gradient-weighted class activation mapping (Grad-CAM) to enhance model interpretability. This interpretability is vital for improving predictive performance, fostering trust, and ensuring seamless integration into agricultural practices.https://www.frontiersin.org/articles/10.3389/frai.2024.1449329/fulldeep learningexplainable AIgrad-CAMpotato leaf disease classificationtransfer learning |
| spellingShingle | Sarah M. Alhammad Doaa Sami Khafaga Walaa M. El-hady Farid M. Samy Khalid M. Hosny Deep learning and explainable AI for classification of potato leaf diseases Frontiers in Artificial Intelligence deep learning explainable AI grad-CAM potato leaf disease classification transfer learning |
| title | Deep learning and explainable AI for classification of potato leaf diseases |
| title_full | Deep learning and explainable AI for classification of potato leaf diseases |
| title_fullStr | Deep learning and explainable AI for classification of potato leaf diseases |
| title_full_unstemmed | Deep learning and explainable AI for classification of potato leaf diseases |
| title_short | Deep learning and explainable AI for classification of potato leaf diseases |
| title_sort | deep learning and explainable ai for classification of potato leaf diseases |
| topic | deep learning explainable AI grad-CAM potato leaf disease classification transfer learning |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1449329/full |
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