Full
Outline
Wheat above ground biomass (AGB) serves as a crucial indicator for assessing its growth status. Currently, unmanned aerial vehicles (UAVs) equipped with various optical sensors have been extensively utilized for monitoring wheat AGB. However, in the late stages of wheat growth, high vegetation coverage hinders optical sensors from capturing information within the canopy, thereby diminishing the accuracy of AGB monitoring. To mitigate this issue, this study incorporated point cloud characteristic parameters (PCs), which are related to canopy height, as representative indicators of canopy structure for estimating AGB using spectral indices, including color indices (CIs) and vegetation indices (VIs). The accuracy of AGB estimation models was then compared before and after the inclusion of PCs. The results revealed variations in the effectiveness of AGB estimation using different characteristic parameters during the jointing, heading, and filling stages of wheat growth. In the training set, the effect of CIs was slightly lower than that of VIs, while in the validation set, it was marginally higher. Although PCs did not exhibit the most remarkable performance across the three key periods, their stability was notably strong, and certain canopy structural characteristics demonstrated potential for AGB prediction. When individual features were used for AGB prediction, the r2 values ranged from 0.47 to 0.75. In contrast, integrating the three features of CIs, VIs, and PCs substantially enhanced the accuracy of the AGB estimation model. Specifically, during the three growth stages, the model's r2 values were 0.79, 0.81, and 0.77, and the root mean square error (RMSE) was 0.42, 0.74, and 0.80 t·hm-2, respectively. Among different types of remote sensing features, support vector machine regression (SVM) provided superior estimation results compared to Gaussian process regression (GPR). Therefore, by integrating the three features (CIs, VIs, and PCs) and employing the SVM algorithm, an effective estimation of wheat AGB can be achi.
162
26
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
