## 3 Results
### Model Construction for Monitoring the Grain-Filling Process of Winter Wheat  
Based on the principle of the strong correlation and the above analysis results, the VIs with a better performance were selected to construct linear, polynomial and logistic growth models. Additionally, for three different winter wheat varieties, the VI-based monitoring modeling of the grain-filling process was carried out, i.e., the prediction models were built using data from three varieties, individually and combined (overall). The VI independent variables corresponding to different function models were screened out via the best R\({}^{2}\) performance of the model development and the model validation (Table 3). In the monitoring model of the overall data, the best performing VIs were mainly the NDRE and PSRI. When modeling different varieties, it could be found that the best VI for the three indicators of Xumai 44 and Xumai 43 was the NDRE, and the best VIs of Xumai 33 were the NDRE, NLI, NDVI and PSRI. In addition, during the screening process of functional models, it was found that the logistic growth model did not perform the best on all varieties and all indicators. The model of the overall data showed that the prediction accuracy of the logistic growth model was better than that of the linear and polynomial models in both dry and fresh thousand-grain weights, and was slightly worse than the polynomial model in terms of water content. In the separate modeling of the three varieties, it could be found that in terms of the monitoring of dry and fresh thousand-grain weight, all varieties performed the best on the logistic growth model. In the monitoring model of water content, except for Xumai 33, which performed slightly better on the logistic growth model, all other varieties performed the best on the polynomial model. Overall correlations were strong despite the decrease in accuracy of the monitoring models (R\({}^{2}\), RMSE) when compared to the individual variety models (Figure 5). The VIs in the model showed a positive correlation trend with the indicators of the winter wheat grain-filling process on the whole, and the fitting effect performed well, indicating the potential to use a single model to estimate wheat maturity independently of the varieties. Figure 4: Pearson correlation (r) between vegetation indices (VIs) and three indicators ((**a**–**c**) represent dry thousand-grain weight, fresh thousand-grain weight and water content, respectively) of wheat grain-filling process by days after sowing and overall. A–E represent five sampling periods. Figure 5: Overall models to monitor dry thousand-grain weight, fresh thousand-grain weight and water content using PSRI (**a,b**) and NDRE (**c**). A–E represent five sampling periods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variety**} & \multirow{2}{*}{**Indictors**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**VI**} & \multicolumn{2}{c}{**Number of Samples**} & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{4-11}  & & & & **Model** & **Model** & **Model** & **Model** & **Model Validation** & **Model Validation** &  
In the single-cultivar model, the VIs of Xumai 44 and Xumai 43 remained relatively consistent in terms of dry and fresh thousand-grain weight and water content, and were significantly different from Xumai 33 (Figure 6). Under the same thousand-grain weight monitoring and the same VIs, although the three varieties generally showed the same negative correlation trend, the relative growth rate of Xumai 33 was significantly different from that of Xumai 44 and Xumai 43. Moreover, in the monitoring model construction of fresh thousand-grain weight, the best VIs of Xumai 33 were different from that of Xumai 44 and Xumai 43, which was the PSRI. The construction of monitoring models with different VIs also created growth rates in different directions. The monitoring models of the three varieties all maintained a roughly positive correlation in terms of water content.