Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton
Thermal imaging combined with deep learning algorithms offers an efficient and non-invasive method for monitoring crop water status, facilitating precise irrigation management over large agricultural areas. This study introduces a method for identifying the moisture state of cotton using an enhanced...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425000794 |
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| author | Kaijun Jin Jihong Zhang Ningning Liu Miao Li Zhanli Ma Zhenhua Wang Jinzhu Zhang Feihu Yin |
| author_facet | Kaijun Jin Jihong Zhang Ningning Liu Miao Li Zhanli Ma Zhenhua Wang Jinzhu Zhang Feihu Yin |
| author_sort | Kaijun Jin |
| collection | DOAJ |
| description | Thermal imaging combined with deep learning algorithms offers an efficient and non-invasive method for monitoring crop water status, facilitating precise irrigation management over large agricultural areas. This study introduces a method for identifying the moisture state of cotton using an enhanced MobileVit deep learning algorithm. This approach incorporates the Efficient Channel Attention (ECA) mechanism into the Fusion component of the MobileVit model, optimizes the first convolution in the Fusion component by replacing it with Depthwise Separable Convolution (DsConv), and substitutes the Local representation with the MobileOne block. These enhancements aim to improve model performance while maintaining its compact size. A dataset of thermal images of cotton canopies representing three different water states was developed for this study. Ablation studies were performed to evaluate the effect of each modification. Grad-CAM was utilized to illustrate the final layer features of the proposed algorithm. Various deep learning models were also trained, tested, and validated, allowing for a comparative analysis of the proposed model against traditional deep learning models in identifying cotton moisture states. The results show that the F1-score of the proposed model reaches 0.9677, achieving a recognition speed of 50.370 ms while maintaining a size of 4.94 M, outperforming other classical deep learning models. The findings of this study provide technical support for the development of future precision irrigation systems. The relevant code and datasets will be made available on GitHub (https://github.com/kingcuzamu/identifying-cotton-water-state) upon publication. |
| format | Article |
| id | doaj-art-33fef0704dd14d7aadeaf37e638ea662 |
| institution | DOAJ |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-33fef0704dd14d7aadeaf37e638ea6622025-08-20T03:05:09ZengElsevierAgricultural Water Management1873-22832025-04-0131010936510.1016/j.agwat.2025.109365Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cottonKaijun Jin0Jihong Zhang1Ningning Liu2Miao Li3Zhanli Ma4Zhenhua Wang5Jinzhu Zhang6Feihu Yin7College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832000, China; Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group, Shihezi University, Shihezi, Xinjiang 832000, China; Technology Innovation Center for Agricultural Water and Fertilizer Efficiency Equipment of Xinjiang Production & Construction Corps, Shihezi, Xinjiang 832000, China; Key Laboratory of Northwest Oasis Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Shihezi, Xinjiang 832000, ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832000, China; Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group, Shihezi University, Shihezi, Xinjiang 832000, China; Technology Innovation Center for Agricultural Water and Fertilizer Efficiency Equipment of Xinjiang Production & Construction Corps, Shihezi, Xinjiang 832000, China; Key Laboratory of Northwest Oasis Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Shihezi, Xinjiang 832000, ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832000, China; Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group, Shihezi University, Shihezi, Xinjiang 832000, China; Technology Innovation Center for Agricultural Water and Fertilizer Efficiency Equipment of Xinjiang Production & Construction Corps, Shihezi, Xinjiang 832000, China; Key Laboratory of Northwest Oasis Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Shihezi, Xinjiang 832000, ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832000, China; Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group, Shihezi University, Shihezi, Xinjiang 832000, China; Technology Innovation Center for Agricultural Water and Fertilizer Efficiency Equipment of Xinjiang Production & Construction Corps, Shihezi, Xinjiang 832000, China; Key Laboratory of Northwest Oasis Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Shihezi, Xinjiang 832000, ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832000, China; Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group, Shihezi University, Shihezi, Xinjiang 832000, China; Technology Innovation Center for Agricultural Water and Fertilizer Efficiency Equipment of Xinjiang Production & Construction Corps, Shihezi, Xinjiang 832000, China; Key Laboratory of Northwest Oasis Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Shihezi, Xinjiang 832000, ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832000, China; Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group, Shihezi University, Shihezi, Xinjiang 832000, China; Technology Innovation Center for Agricultural Water and Fertilizer Efficiency Equipment of Xinjiang Production & Construction Corps, Shihezi, Xinjiang 832000, China; Key Laboratory of Northwest Oasis Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Shihezi, Xinjiang 832000, China; Corresponding authors at: College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China.College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832000, China; Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group, Shihezi University, Shihezi, Xinjiang 832000, China; Technology Innovation Center for Agricultural Water and Fertilizer Efficiency Equipment of Xinjiang Production & Construction Corps, Shihezi, Xinjiang 832000, China; Key Laboratory of Northwest Oasis Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Shihezi, Xinjiang 832000, China; Corresponding authors at: College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China.College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832000, China; Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, ChinaThermal imaging combined with deep learning algorithms offers an efficient and non-invasive method for monitoring crop water status, facilitating precise irrigation management over large agricultural areas. This study introduces a method for identifying the moisture state of cotton using an enhanced MobileVit deep learning algorithm. This approach incorporates the Efficient Channel Attention (ECA) mechanism into the Fusion component of the MobileVit model, optimizes the first convolution in the Fusion component by replacing it with Depthwise Separable Convolution (DsConv), and substitutes the Local representation with the MobileOne block. These enhancements aim to improve model performance while maintaining its compact size. A dataset of thermal images of cotton canopies representing three different water states was developed for this study. Ablation studies were performed to evaluate the effect of each modification. Grad-CAM was utilized to illustrate the final layer features of the proposed algorithm. Various deep learning models were also trained, tested, and validated, allowing for a comparative analysis of the proposed model against traditional deep learning models in identifying cotton moisture states. The results show that the F1-score of the proposed model reaches 0.9677, achieving a recognition speed of 50.370 ms while maintaining a size of 4.94 M, outperforming other classical deep learning models. The findings of this study provide technical support for the development of future precision irrigation systems. The relevant code and datasets will be made available on GitHub (https://github.com/kingcuzamu/identifying-cotton-water-state) upon publication.http://www.sciencedirect.com/science/article/pii/S0378377425000794CottonWater state identificationComputer visionImproved MobileVit algorithmDeep learning |
| spellingShingle | Kaijun Jin Jihong Zhang Ningning Liu Miao Li Zhanli Ma Zhenhua Wang Jinzhu Zhang Feihu Yin Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton Agricultural Water Management Cotton Water state identification Computer vision Improved MobileVit algorithm Deep learning |
| title | Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton |
| title_full | Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton |
| title_fullStr | Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton |
| title_full_unstemmed | Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton |
| title_short | Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton |
| title_sort | improved mobilevit deep learning algorithm based on thermal images to identify the water state in cotton |
| topic | Cotton Water state identification Computer vision Improved MobileVit algorithm Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425000794 |
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