Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic.
Citrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like ca...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316081 |
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| author | Nouman Butt Muhammad Munwar Iqbal Shabana Ramzan Ali Raza Laith Abualigah Norma Latif Fitriyani Yeonghyeon Gu Muhammad Syafrudin |
| author_facet | Nouman Butt Muhammad Munwar Iqbal Shabana Ramzan Ali Raza Laith Abualigah Norma Latif Fitriyani Yeonghyeon Gu Muhammad Syafrudin |
| author_sort | Nouman Butt |
| collection | DOAJ |
| description | Citrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like canker, scab, and black spot, which lower fruit quality and yield. Traditional manual disease diagnosis is not only slow, less accurate, and expensive but also relies heavily on expert intervention. To address these issues, this research examines the implementation of an automated disease classification system using deep learning and optimal feature selection. The system incorporates data augmentation and transfer learning with pre-trained models such as DenseNet-201 and AlexNet to improve diagnostic accuracy, efficiency, and cost-effectiveness. Experimental results on a citrus leaves dataset show an impressive 99.6% classification accuracy. The proposed framework outperforms existing methods, offering a robust and scalable solution for disease detection in citrus farming, contributing to more sustainable agricultural practices. |
| format | Article |
| id | doaj-art-e8f2ffa86f3e40f48e5aff1f7eb42269 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e8f2ffa86f3e40f48e5aff1f7eb422692025-02-05T05:31:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031608110.1371/journal.pone.0316081Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic.Nouman ButtMuhammad Munwar IqbalShabana RamzanAli RazaLaith AbualigahNorma Latif FitriyaniYeonghyeon GuMuhammad SyafrudinCitrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like canker, scab, and black spot, which lower fruit quality and yield. Traditional manual disease diagnosis is not only slow, less accurate, and expensive but also relies heavily on expert intervention. To address these issues, this research examines the implementation of an automated disease classification system using deep learning and optimal feature selection. The system incorporates data augmentation and transfer learning with pre-trained models such as DenseNet-201 and AlexNet to improve diagnostic accuracy, efficiency, and cost-effectiveness. Experimental results on a citrus leaves dataset show an impressive 99.6% classification accuracy. The proposed framework outperforms existing methods, offering a robust and scalable solution for disease detection in citrus farming, contributing to more sustainable agricultural practices.https://doi.org/10.1371/journal.pone.0316081 |
| spellingShingle | Nouman Butt Muhammad Munwar Iqbal Shabana Ramzan Ali Raza Laith Abualigah Norma Latif Fitriyani Yeonghyeon Gu Muhammad Syafrudin Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic. PLoS ONE |
| title | Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic. |
| title_full | Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic. |
| title_fullStr | Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic. |
| title_full_unstemmed | Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic. |
| title_short | Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic. |
| title_sort | citrus diseases detection using innovative deep learning approach and hybrid meta heuristic |
| url | https://doi.org/10.1371/journal.pone.0316081 |
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