# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 3 Results
### Construction of the Cotton Yield Estimation Model  
#### 3.3.1 Construction of the Cotton Yield Estimation Model Based on Canopy SIF  
The variation in canopy SIF with respect to the cotton yield estimation model R\({}^{2}\) is shown in Figure 5. In the four key growth periods, the effect of the cotton yield model constructed with RF is the best, and the R\({}^{2}\) is basically 0.90 in each period. The R\({}^{2}\) of the cotton yield model constructed using Ada Boost and Bagging had a significant peak at 14:00-15:00, and the range of R\({}^{2}\) was 0.70-0.80. The model of cotton yield based on BPNN had the worst effect, with R\({}^{2}\) less than 0.5. In the flowering and boll period (Figure 5c), the constructed models gave better results for all models except BPNN. In the boll period (Figure 5d), none of the models worked very well. This indicates that time variation had an effect on the SIF cotton yield model.  
Figure 4: Correlation graph of cotton canopy SIF, AGB, and LAI in different time periods. (**a**): budding period; (**b**): flowering period; (**c**): flowering and boll period; (**d**): boll period. *, significant correlation at the 0.01 level.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Budding Period** & \
\begin{tabular}{c} **Flowering** \\ **Period** \\ \end{tabular} &
\begin{tabular}{c} **Flowering and** \\ **Boll Period** \\ \end{tabular} & **Boll Period** \\ \hline Yield and AGB & 0.420 * & 0.858 ** & 0.868 ** & 0.556 ** \\ Yield and LAI & 0.494 * & 0.691 ** & 0.819 ** & 0.690 ** \\ \hline \hline \end{tabular}  
*, significant correlation at the 0.01 level; **, significant correlation at the 0.05 level.  
\end{table}
Table 3: Correlation analysis of cotton yield with AGB and LAI in different growth periods.  
According to the correlation analysis between LAI, AGB, and canopy SIF, canopy SIF with good correlation on 14:00-15:00 was selected. A cotton yield estimation model based on canopy SIF from 14:00 to 15:00 was established. Figures 6-9 show the yield estimation model of canopy SIF based on 14:00-15:00 at different growth stages of cotton. By comparing the four modeling methods, it was concluded that the cotton yield model constructed with RF had the best effect (Figure 7). The best modeling using the RF algorithm was achieved during the flowering and boll period (R\({}^{2}\) = 0.9336, RMSE = 75.16 kg\(\cdot\)ha\({}^{-1}\), RPD = 3.668, Figure 7c), followed by the flowering period (R\({}^{2}\) = 0.9256, Figure 7b), while the worst modeling was achieved during the boll period (R\({}^{2}\) = 0.8919, Figure 7d). The results of utilizing the BPNN algorithm were unsatisfactory for all growth periods, with a maximum R\({}^{2}\) value of 0.6138 (Figure 9a). The Ada Boost algorithm was used to achieve the best modeling effect in the flowering and boll period (R\({}^{2}\) = 0.8572, RMSE = 107.5 kg\(\cdot\)ha\({}^{-1}\), RPD = 2.331, Figure 6c). The Bagging algorithm was able to achieve the best modeling effect in the flowering and boll period (R\({}^{2}\) = 0.8830, RMSE = 119.7 kg\(\cdot\)ha\({}^{-1}\), RPD = 2.236, Figure 8c). In summary, the best modeling was achieved for the flowering and boll period, followed by the flowering period, in the yield estimation model based on 14:00-15:00 canopy SIF. The optimal model was RF modeling in the boll period.  
Figure 5: Variations in the yield estimation model R\({}^{2}\) values of canopy SIF for cotton in different time periods. (**a**): budding period; (**b**): flowering period; (**c**): flowering and boll period; (**d**): boll period.  
Figure 6: Yield estimation model of cotton based on canopy SIF from 14:00 to 15:00 using Ada Boost for different growth periods. (**a**): budding period; (**b**): flowering period; (**c**): flowering and boll period; (**d**): boll period. Black samples represent the results of the training set, and blue samples represent the results of the verification set.