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Estimation of the leaf water content (LWC) plays an important role in field irrigation management. This study aimed to estimate the LWC of winter wheat based on hyperspectral data of leaf blades, especially focusing on the effect of different variable screening methods and growth stages on the estimation model. Research data were obtained from field trials in 2022 and 2023 at the booting, heading, and grain-filling stages. Vegetation indices were constructed for each growth stage by combining the two bands. Tthe input characteristic variables based on vegetation indices were screened by two methods: (I) the input characteristic variables were directly obtained by ranking the correlation coefficients; (II) based on the method I, the vegetation index was further screened by the ReliefF algorithm to obtain a second set of input characteristic variables. The LWC estimation models were constructed using random forest (RF), long short-term memory (LSTM) network and back propagation neural network (BPNN) based on particle swarm optimization (PSO). The best method for estimating LWC was derived by comparing the accuracy of the models. The results showed that comparing the two variable screening methods, the characteristic variables further screened by ReliefF could effectively improve the accuracy of the LSTM and PSO-BPNN models, while the effect of improving the RF model is not obvious. The best model for each growth stage was established by the ReliefF screening method combined with the PSO-BPNN, at the booting stage, heading stage and grain-filling stage. The r2 of the validation set was 0.816, 0.736, and 0.806, respectively, and the RMSE was 0.546%, 0.899%, and 1.531%, respectively, and the NRMSE was 0.681%, 1.195%, and 2.185%, respectively. It was suggested that the screening method of feature variables through the ReliefF algorithm could improve its estimation accuracy in the particular model. Its combination with the PSO-BPNN model had the best application effect in the estimation of LWC in winter wheat at the growth stages.
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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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