Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections
This study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved residual skip connections. In light of increasing global challenges related to food security and sustai...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1505857/full |
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| author | Asadulla Y. Ashurov Mehdhar S. A. M. Al-Gaashani Nagwan A. Samee Reem Alkanhel Ghada Atteia Hanaa A. Abdallah Mohammed Saleh Ali Muthanna |
| author_facet | Asadulla Y. Ashurov Mehdhar S. A. M. Al-Gaashani Nagwan A. Samee Reem Alkanhel Ghada Atteia Hanaa A. Abdallah Mohammed Saleh Ali Muthanna |
| author_sort | Asadulla Y. Ashurov |
| collection | DOAJ |
| description | This study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved residual skip connections. In light of increasing global challenges related to food security and sustainable agriculture, this research focuses on developing a highly efficient and accurate automated system for identifying plant diseases, thereby contributing to enhanced crop protection and yield optimization. The proposed model is trained on a comprehensive dataset encompassing various plant species and disease categories, ensuring robust performance and adaptability. By evaluating the model with online random images, demonstrate its significant adaptability and effectiveness in overcoming key challenges, such as achieving high accuracy and meeting the practical demands of agricultural applications. The architectural modifications are specifically designed to enhance feature extraction and classification performance, all while maintaining computational efficiency. The evaluation results further highlight the model’s effectiveness, achieving an accuracy of 98% and an F1 score of 98.2%. These findings emphasize the model’s potential as a practical tool for disease identification in agricultural applications, supporting timely and informed decision-making for crop protection. |
| format | Article |
| id | doaj-art-e448af2955524e5ea3aa325d279a5e96 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-e448af2955524e5ea3aa325d279a5e962025-01-24T14:19:29ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.15058571505857Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connectionsAsadulla Y. Ashurov0Mehdhar S. A. M. Al-Gaashani1Nagwan A. Samee2Reem Alkanhel3Ghada Atteia4Hanaa A. Abdallah5Mohammed Saleh Ali Muthanna6School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, ChinaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of International Business Management, Tashkent State University of Economics, Tashkent, UzbekistanThis study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved residual skip connections. In light of increasing global challenges related to food security and sustainable agriculture, this research focuses on developing a highly efficient and accurate automated system for identifying plant diseases, thereby contributing to enhanced crop protection and yield optimization. The proposed model is trained on a comprehensive dataset encompassing various plant species and disease categories, ensuring robust performance and adaptability. By evaluating the model with online random images, demonstrate its significant adaptability and effectiveness in overcoming key challenges, such as achieving high accuracy and meeting the practical demands of agricultural applications. The architectural modifications are specifically designed to enhance feature extraction and classification performance, all while maintaining computational efficiency. The evaluation results further highlight the model’s effectiveness, achieving an accuracy of 98% and an F1 score of 98.2%. These findings emphasize the model’s potential as a practical tool for disease identification in agricultural applications, supporting timely and informed decision-making for crop protection.https://www.frontiersin.org/articles/10.3389/fpls.2024.1505857/fulldeep learningplant disease detectionconvolutional neural networksqueeze and excitation (SE) blocksresidual skip connection |
| spellingShingle | Asadulla Y. Ashurov Mehdhar S. A. M. Al-Gaashani Nagwan A. Samee Reem Alkanhel Ghada Atteia Hanaa A. Abdallah Mohammed Saleh Ali Muthanna Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections Frontiers in Plant Science deep learning plant disease detection convolutional neural network squeeze and excitation (SE) blocks residual skip connection |
| title | Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections |
| title_full | Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections |
| title_fullStr | Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections |
| title_full_unstemmed | Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections |
| title_short | Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections |
| title_sort | enhancing plant disease detection through deep learning a depthwise cnn with squeeze and excitation integration and residual skip connections |
| topic | deep learning plant disease detection convolutional neural network squeeze and excitation (SE) blocks residual skip connection |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1505857/full |
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