Issue |
E3S Web Conf.
Volume 601, 2025
The 3rd International Conference on Energy and Green Computing (ICEGC’2024)
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---|---|---|
Article Number | 00032 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/e3sconf/202560100032 | |
Published online | 16 January 2025 |
Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search
Mohammed V University in Rabat, Department of E2SN in ENSAM, B.P: 8007.N.U, Avenue des Nations Unies, Agdal, Rabat, Morocco
* Corresponding author: hicham_slimani2@um5.ac.ma
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.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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