Full
Outline
In order to realize the accurate estimation of winter wheat yield, this study used an unmanned aerial vehicle (UAV) to collect multi-temporal canopy multispectral data from 266 wheat varieties (lines), extracted multiple vegetation indices, and established a single-fertility and multi-fertility combined single-stage and multi-stage based on machine learning algorithms, such as multiple linear regression (PLSR), support vector regression (SVR), random forest (RF), and back-propagation neural network (BP), to set up. wheat grain yield prediction model. At the same time, the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the models. It was found that the yield prediction with a single growth stage was best at mid-filling stage, and the optimal model was the RF model with the prediction accuracy of R2=0.655 and RMSE=0.550 kg·m-2 and the optimal model for the yield prediction model with a combination of multi-growth stages was the RF model based on multi-spectral data from the five development stages (flowering, mid-filling, late-filling, dough and full-maturity), with the prediction accuracy of R2=0.834, RMSE=0.381 kg·m-2. Meanwhile, the accuracy of the winter wheat yield prediction model was further improved after adding textural features to the modeling features. Based on a single stage, the optimal prediction stage for the vegetation index combined with textural features was the combination of dough and full-maturity stages, and the optimal model was the SVR model, with the prediction accuracy of R2=0.924 and RMSE=0.253 kg·m-2. Therefore, unmanned canopy spectra can be used to predict winter wheat yield based on vegetation indices plus textural features, with the best prediction accuracy based on SVR models with multiple growth stages (dough and full-maturity).
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| 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 |
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