AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases
Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to...
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MDPI AG
2024-12-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/5/4/142 |
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| author | Oluwatoyin Joy Omole Renata Lopes Rosa Muhammad Saadi Demóstenes Zegarra Rodriguez |
| author_facet | Oluwatoyin Joy Omole Renata Lopes Rosa Muhammad Saadi Demóstenes Zegarra Rodriguez |
| author_sort | Oluwatoyin Joy Omole |
| collection | DOAJ |
| description | Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to substantial production losses. To address this challenge, we propose AgriNAS, a method that integrates a Neural Architecture Search (NAS) framework with an adaptive convolutional architecture specifically designed for plant pathology. AgriNAS employs a novel data augmentation strategy and a Spatial–Time Augmentation (STA) method, and it utilizes a multi-stage convolutional network that dynamically adapts to the complexity of the input data. The proposed AgriNAS leverages powerful GPU resources to handle the intensive computational tasks involved in NAS and model training. The framework incorporates a bi-level optimization strategy and entropy-based regularization to enhance model robustness and prevent overfitting. AgriNAS achieves classification accuracies superior to VGG-19 and a transfer learning method using convolutional neural networks. |
| format | Article |
| id | doaj-art-9ae6ce37aa1340bf969e6a94a90f2d5d |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-9ae6ce37aa1340bf969e6a94a90f2d5d2024-12-27T14:05:08ZengMDPI AGAI2673-26882024-12-01542945296610.3390/ai5040142AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean DiseasesOluwatoyin Joy Omole0Renata Lopes Rosa1Muhammad Saadi2Demóstenes Zegarra Rodriguez3Department of Computer Science, Federal University of Lavras, Lavras 37200-000, Minas Gerais, BrazilDepartment of Computer Science, Federal University of Lavras, Lavras 37200-000, Minas Gerais, BrazilSchool of Science and Technology, Department of Computer Science, Nottingham Trent University, Nottingham NG11 8NS , UKDepartment of Computer Science, Federal University of Lavras, Lavras 37200-000, Minas Gerais, BrazilSoybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to substantial production losses. To address this challenge, we propose AgriNAS, a method that integrates a Neural Architecture Search (NAS) framework with an adaptive convolutional architecture specifically designed for plant pathology. AgriNAS employs a novel data augmentation strategy and a Spatial–Time Augmentation (STA) method, and it utilizes a multi-stage convolutional network that dynamically adapts to the complexity of the input data. The proposed AgriNAS leverages powerful GPU resources to handle the intensive computational tasks involved in NAS and model training. The framework incorporates a bi-level optimization strategy and entropy-based regularization to enhance model robustness and prevent overfitting. AgriNAS achieves classification accuracies superior to VGG-19 and a transfer learning method using convolutional neural networks.https://www.mdpi.com/2673-2688/5/4/142neural architecture searchadaptive convolutional architecturedata augmentationsoybean disease and pest detection |
| spellingShingle | Oluwatoyin Joy Omole Renata Lopes Rosa Muhammad Saadi Demóstenes Zegarra Rodriguez AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases AI neural architecture search adaptive convolutional architecture data augmentation soybean disease and pest detection |
| title | AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases |
| title_full | AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases |
| title_fullStr | AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases |
| title_full_unstemmed | AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases |
| title_short | AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases |
| title_sort | agrinas neural architecture search with adaptive convolution and spatial time augmentation method for soybean diseases |
| topic | neural architecture search adaptive convolutional architecture data augmentation soybean disease and pest detection |
| url | https://www.mdpi.com/2673-2688/5/4/142 |
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