Construction and application of a drought classification model for tea plantations based on multi-source remote sensing
In the backdrop of global climate change, drought is identified as a major natural hazard, posing a severe threat to tea production. Traditional methods for assessing drought stress in tea plants rely on manual investigation. However, this approach is time-consuming and labor-intensive. While unmann...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003648 |
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| author | Yang Xu Yilin Mao He Li Xiaojiang Li Litao Sun Kai Fan Zhipeng Li Shuting Gong Zhaotang Ding Yu Wang |
| author_facet | Yang Xu Yilin Mao He Li Xiaojiang Li Litao Sun Kai Fan Zhipeng Li Shuting Gong Zhaotang Ding Yu Wang |
| author_sort | Yang Xu |
| collection | DOAJ |
| description | In the backdrop of global climate change, drought is identified as a major natural hazard, posing a severe threat to tea production. Traditional methods for assessing drought stress in tea plants rely on manual investigation. However, this approach is time-consuming and labor-intensive. While unmanned aerial vehicle (UAV) remote sensing offers efficient alternatives, existing studies predominantly rely on single-sensor data (e.g., multispectral (MS) or thermal infrared (TIR)), overlooking the potential of multi-source fusion—especially for tea plantations. To address this gap, we propose RSDCM (Remote Sensing-based Drought Classification Model), an improved Genetic Algorithm-Backpropagation (GA-BP) combined with MS + TIR framework that optimizes initial weights and thresholds via GA's global search (hidden layer=1, neurons in the hidden layers=5, 50 generations, population size=5, NonUnifMutation operators) to escape local minima and accelerate convergence. A UAV platform equipped with MS, RGB, and TIR sensors collected multi-source data from drought-stressed tea plantations in Eastern China. The RSDCM model was benchmarked against single BP and three classical machine learning models (SVM, RF, ELM).The study found that: (1) Multi-source data fusion outperformed single-source data, with MS + TIR achieving optimal performance (Accuracy: 0.983, Precision: 0.967-1.000, Recall: 0.967-1.000, F1-score: 0.967-1.000)—surpassing MS (Accuracy: 0.950, Precision: 0.894-1.000, Recall: 0.917-0.983, F1-score: 0.924-0.983), TIR (Accuracy: 0.925, Precision: 0.862-0.982, Recall: 0.867-0.983, F1-score: 0.889-0.967), and RGB (Accuracy: 0.904, Precision: 0.824-0.950, Recall: 0.783-0.950, F1-score: 0.847-0.950) alone. (2) The RSDCM model (accuracy: 0.983) performed better than the other four models, with high generalizability across all drought levels (F1-scores: 0.967–1.000 for severe/moderate/light/normal classes). (3) The RSDCM model could accurately classify drought stress levels in tea plantations.Thus, RSDCM provides a novel, robust solution for UAV-based drought assessment in tea plantations, combining multi-sensor fusion and deep learning. |
| format | Article |
| id | doaj-art-e8a065e2ffea483c97b641a0c3e9ead1 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-e8a065e2ffea483c97b641a0c3e9ead12025-08-20T03:32:49ZengElsevierSmart Agricultural Technology2772-37552025-12-011210113210.1016/j.atech.2025.101132Construction and application of a drought classification model for tea plantations based on multi-source remote sensingYang Xu0Yilin Mao1He Li2Xiaojiang Li3Litao Sun4Kai Fan5Zhipeng Li6Shuting Gong7Zhaotang Ding8Yu Wang9College of Horticulture, Qingdao Agricultural University, Qingdao 266109, PR ChinaCollege of Horticulture, Qingdao Agricultural University, Qingdao 266109, PR ChinaCollege of Horticulture, Qingdao Agricultural University, Qingdao 266109, PR ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, PR ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, PR ChinaCollege of Horticulture, Qingdao Agricultural University, Qingdao 266109, PR ChinaCollege of Horticulture, Qingdao Agricultural University, Qingdao 266109, PR ChinaCollege of Horticulture, Qingdao Agricultural University, Qingdao 266109, PR ChinaCollege of Horticulture, Qingdao Agricultural University, Qingdao 266109, PR China; Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, PR China; Corresponding authors.College of Horticulture, Qingdao Agricultural University, Qingdao 266109, PR China; Corresponding authors.In the backdrop of global climate change, drought is identified as a major natural hazard, posing a severe threat to tea production. Traditional methods for assessing drought stress in tea plants rely on manual investigation. However, this approach is time-consuming and labor-intensive. While unmanned aerial vehicle (UAV) remote sensing offers efficient alternatives, existing studies predominantly rely on single-sensor data (e.g., multispectral (MS) or thermal infrared (TIR)), overlooking the potential of multi-source fusion—especially for tea plantations. To address this gap, we propose RSDCM (Remote Sensing-based Drought Classification Model), an improved Genetic Algorithm-Backpropagation (GA-BP) combined with MS + TIR framework that optimizes initial weights and thresholds via GA's global search (hidden layer=1, neurons in the hidden layers=5, 50 generations, population size=5, NonUnifMutation operators) to escape local minima and accelerate convergence. A UAV platform equipped with MS, RGB, and TIR sensors collected multi-source data from drought-stressed tea plantations in Eastern China. The RSDCM model was benchmarked against single BP and three classical machine learning models (SVM, RF, ELM).The study found that: (1) Multi-source data fusion outperformed single-source data, with MS + TIR achieving optimal performance (Accuracy: 0.983, Precision: 0.967-1.000, Recall: 0.967-1.000, F1-score: 0.967-1.000)—surpassing MS (Accuracy: 0.950, Precision: 0.894-1.000, Recall: 0.917-0.983, F1-score: 0.924-0.983), TIR (Accuracy: 0.925, Precision: 0.862-0.982, Recall: 0.867-0.983, F1-score: 0.889-0.967), and RGB (Accuracy: 0.904, Precision: 0.824-0.950, Recall: 0.783-0.950, F1-score: 0.847-0.950) alone. (2) The RSDCM model (accuracy: 0.983) performed better than the other four models, with high generalizability across all drought levels (F1-scores: 0.967–1.000 for severe/moderate/light/normal classes). (3) The RSDCM model could accurately classify drought stress levels in tea plantations.Thus, RSDCM provides a novel, robust solution for UAV-based drought assessment in tea plantations, combining multi-sensor fusion and deep learning.http://www.sciencedirect.com/science/article/pii/S2772375525003648Tea plantDrought stressUAVMulti-source remote sensingData fusionDeep learning |
| spellingShingle | Yang Xu Yilin Mao He Li Xiaojiang Li Litao Sun Kai Fan Zhipeng Li Shuting Gong Zhaotang Ding Yu Wang Construction and application of a drought classification model for tea plantations based on multi-source remote sensing Smart Agricultural Technology Tea plant Drought stress UAV Multi-source remote sensing Data fusion Deep learning |
| title | Construction and application of a drought classification model for tea plantations based on multi-source remote sensing |
| title_full | Construction and application of a drought classification model for tea plantations based on multi-source remote sensing |
| title_fullStr | Construction and application of a drought classification model for tea plantations based on multi-source remote sensing |
| title_full_unstemmed | Construction and application of a drought classification model for tea plantations based on multi-source remote sensing |
| title_short | Construction and application of a drought classification model for tea plantations based on multi-source remote sensing |
| title_sort | construction and application of a drought classification model for tea plantations based on multi source remote sensing |
| topic | Tea plant Drought stress UAV Multi-source remote sensing Data fusion Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525003648 |
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