Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search
Efficient crop disease management shows great promise in optimizing the agricultural industry. Accurate identification of infection levels is crucial for implementing effective and efficient disease treatments. However, accurately identifying and locating crop diseases in complex, unstructured field...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00032.pdf |
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| author | Slimani Hicham El Mhamdi Jamal Jilbab Abdelilah |
| author_facet | Slimani Hicham El Mhamdi Jamal Jilbab Abdelilah |
| author_sort | Slimani Hicham |
| collection | DOAJ |
| description | Efficient crop disease management shows great promise in optimizing the agricultural industry. Accurate identification of infection levels is crucial for implementing effective and efficient disease treatments. However, accurately identifying and locating crop diseases in complex, unstructured field environments remain challenging. This necessitates the utilization of large volumes of annotated data. This research paper comprehensively evaluates deep transfer learning techniques for identifying the degree of rust disease infection in Vicia faba L. production systems. We curate a vast dataset comprising images captured under natural lighting conditions and at different growth stages of the crop under study. We propose a deep learning model based on Neural Architecture Search (NAS) specifically designed for early detection and accurate classification of disease levels in crops. We compare the performance of our proposed model with nine other deep learning models using transfer learning. Remarkably, transfer learning based on the NAS method achieves high classification accuracy, consistently exceeding 90.84% F1 scores. Moreover, all models exhibit short training times, requiring less than 3 hours. Among the evaluated models, our NAS-based model emerges as the top performer, highlighting the importance and effectiveness of this method in developing state-of-the-art models. It achieves a mean average precision of 94.10% and an impressive overall recall of 96.96%. These results significantly contribute to developing robust and accurate disease management strategies, paving the way for improved agricultural practices and increased crop yields. Our approach enables early disease detection and precise classification, leveraging deep transfer learning and facilitating timely interventions and optimized treatments. With the help of this study, we can now better utilize cuttingedge agricultural technology, paving the way for sustainable crop production in the future. |
| format | Article |
| id | doaj-art-671770da94a24d47803fb101414f9c3c |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-671770da94a24d47803fb101414f9c3c2025-02-05T10:46:25ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010003210.1051/e3sconf/202560100032e3sconf_icegc2024_00032Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture SearchSlimani Hicham0El Mhamdi Jamal1Jilbab Abdelilah2Mohammed V University in Rabat, Department of E2SN in ENSAMMohammed V University in Rabat, Department of E2SN in ENSAMMohammed V University in Rabat, Department of E2SN in ENSAMEfficient crop disease management shows great promise in optimizing the agricultural industry. Accurate identification of infection levels is crucial for implementing effective and efficient disease treatments. However, accurately identifying and locating crop diseases in complex, unstructured field environments remain challenging. This necessitates the utilization of large volumes of annotated data. This research paper comprehensively evaluates deep transfer learning techniques for identifying the degree of rust disease infection in Vicia faba L. production systems. We curate a vast dataset comprising images captured under natural lighting conditions and at different growth stages of the crop under study. We propose a deep learning model based on Neural Architecture Search (NAS) specifically designed for early detection and accurate classification of disease levels in crops. We compare the performance of our proposed model with nine other deep learning models using transfer learning. Remarkably, transfer learning based on the NAS method achieves high classification accuracy, consistently exceeding 90.84% F1 scores. Moreover, all models exhibit short training times, requiring less than 3 hours. Among the evaluated models, our NAS-based model emerges as the top performer, highlighting the importance and effectiveness of this method in developing state-of-the-art models. It achieves a mean average precision of 94.10% and an impressive overall recall of 96.96%. These results significantly contribute to developing robust and accurate disease management strategies, paving the way for improved agricultural practices and increased crop yields. Our approach enables early disease detection and precise classification, leveraging deep transfer learning and facilitating timely interventions and optimized treatments. With the help of this study, we can now better utilize cuttingedge agricultural technology, paving the way for sustainable crop production in the future.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00032.pdf |
| spellingShingle | Slimani Hicham El Mhamdi Jamal Jilbab Abdelilah Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search E3S Web of Conferences |
| title | Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search |
| title_full | Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search |
| title_fullStr | Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search |
| title_full_unstemmed | Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search |
| title_short | Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search |
| title_sort | advanced algorithmic model for real time multi level crop disease detection using neural architecture search |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00032.pdf |
| work_keys_str_mv | AT slimanihicham advancedalgorithmicmodelforrealtimemultilevelcropdiseasedetectionusingneuralarchitecturesearch AT elmhamdijamal advancedalgorithmicmodelforrealtimemultilevelcropdiseasedetectionusingneuralarchitecturesearch AT jilbababdelilah advancedalgorithmicmodelforrealtimemultilevelcropdiseasedetectionusingneuralarchitecturesearch |