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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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003648 |
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| Summary: | 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. |
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| ISSN: | 2772-3755 |