Evaluation of crop water status using UAV-based images data with a model updating strategy
This study aims to evaluate crop water status by fusing multiple features from the unmanned aerial vehicle (UAV)-based canopy images with model updating strategy. A UAV platform carrying multispectral and thermal infrared cameras was used to collect high spatial resolution images of winter wheat and...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425001593 |
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| author | Ning Yang Zhitao Zhang Xiaofei Yang Ning Dong Qi Xu Junying Chen Shikun Sun Ningbo Cui Jifeng Ning |
| author_facet | Ning Yang Zhitao Zhang Xiaofei Yang Ning Dong Qi Xu Junying Chen Shikun Sun Ningbo Cui Jifeng Ning |
| author_sort | Ning Yang |
| collection | DOAJ |
| description | This study aims to evaluate crop water status by fusing multiple features from the unmanned aerial vehicle (UAV)-based canopy images with model updating strategy. A UAV platform carrying multispectral and thermal infrared cameras was used to collect high spatial resolution images of winter wheat and summer maize under different water treatments over two years. The plant water content (PWC) and above-ground biomass (AGB), which represent crop water status, were collected simultaneously. The vegetation indices (VIs), texture features, and canopy thermal indicators were extracted from UAV-based images to estimate PWC and AGB based on CNN-LSTM-Attention (CLA) model. The results showed that combining spectral, textural, and thermal features with the CLA model significantly improved estimation accuracy. Specifically, multi-feature fusion achieved the best performance in winter wheat, with MAE of 1.80 % and 1.23 %, and RMSE of 2.13 % and 1.57 % for PWC in 2022 and 2023, respectively. For AGB, the corresponding MAE values were 1.12 t/hm² and 1.04 t/hm², and RMSE values were 1.41 t/hm² and 1.31 t/hm². In addition, the model updating strategy successfully verified the robustness of the estimation model for winter wheat across different years, and the application of the CLA model to summer maize demonstrated its effective transferability. In summary, this method can improve the estimation accuracy of PWC and AGB, thereby achieving efficient evaluation of crop water status. |
| format | Article |
| id | doaj-art-c744b21f79f74fc68dfb35b095d84f8e |
| institution | DOAJ |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-c744b21f79f74fc68dfb35b095d84f8e2025-08-20T03:10:34ZengElsevierAgricultural Water Management1873-22832025-05-0131210944510.1016/j.agwat.2025.109445Evaluation of crop water status using UAV-based images data with a model updating strategyNing Yang0Zhitao Zhang1Xiaofei Yang2Ning Dong3Qi Xu4Junying Chen5Shikun Sun6Ningbo Cui7Jifeng Ning8College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China; Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China; Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, China; Corresponding author at: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China.College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China; Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China; Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China; Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China; Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China; Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaCollege of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, ChinaThis study aims to evaluate crop water status by fusing multiple features from the unmanned aerial vehicle (UAV)-based canopy images with model updating strategy. A UAV platform carrying multispectral and thermal infrared cameras was used to collect high spatial resolution images of winter wheat and summer maize under different water treatments over two years. The plant water content (PWC) and above-ground biomass (AGB), which represent crop water status, were collected simultaneously. The vegetation indices (VIs), texture features, and canopy thermal indicators were extracted from UAV-based images to estimate PWC and AGB based on CNN-LSTM-Attention (CLA) model. The results showed that combining spectral, textural, and thermal features with the CLA model significantly improved estimation accuracy. Specifically, multi-feature fusion achieved the best performance in winter wheat, with MAE of 1.80 % and 1.23 %, and RMSE of 2.13 % and 1.57 % for PWC in 2022 and 2023, respectively. For AGB, the corresponding MAE values were 1.12 t/hm² and 1.04 t/hm², and RMSE values were 1.41 t/hm² and 1.31 t/hm². In addition, the model updating strategy successfully verified the robustness of the estimation model for winter wheat across different years, and the application of the CLA model to summer maize demonstrated its effective transferability. In summary, this method can improve the estimation accuracy of PWC and AGB, thereby achieving efficient evaluation of crop water status.http://www.sciencedirect.com/science/article/pii/S0378377425001593Crop water statusUAVFeature fusionModel updating strategyDeep learningModel transferability |
| spellingShingle | Ning Yang Zhitao Zhang Xiaofei Yang Ning Dong Qi Xu Junying Chen Shikun Sun Ningbo Cui Jifeng Ning Evaluation of crop water status using UAV-based images data with a model updating strategy Agricultural Water Management Crop water status UAV Feature fusion Model updating strategy Deep learning Model transferability |
| title | Evaluation of crop water status using UAV-based images data with a model updating strategy |
| title_full | Evaluation of crop water status using UAV-based images data with a model updating strategy |
| title_fullStr | Evaluation of crop water status using UAV-based images data with a model updating strategy |
| title_full_unstemmed | Evaluation of crop water status using UAV-based images data with a model updating strategy |
| title_short | Evaluation of crop water status using UAV-based images data with a model updating strategy |
| title_sort | evaluation of crop water status using uav based images data with a model updating strategy |
| topic | Crop water status UAV Feature fusion Model updating strategy Deep learning Model transferability |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425001593 |
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