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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Main Authors: Sarah M. Alhammad, Doaa Sami Khafaga, Walaa M. El-hady, Farid M. Samy, Khalid M. Hosny
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
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.
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institution Kabale University
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publisher Frontiers Media S.A.
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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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AT walaamelhady deeplearningandexplainableaiforclassificationofpotatoleafdiseases
AT faridmsamy deeplearningandexplainableaiforclassificationofpotatoleafdiseases
AT khalidmhosny deeplearningandexplainableaiforclassificationofpotatoleafdiseases