# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 5 Conclusions  
In this study, we analyzed the biometric parameters affecting the correlation between cotton canopy SIF and yield, clarified the optimal time and growth period for yield estimation by cotton canopy SIF, and established and screened the optimal model for yield estimation by using cotton canopy SIF parameters. Our conclusions based on this research are as follows:  
1. The effects of LAI and AGB on cotton canopy SIF and cotton yield were similar. Throughout the reproductive period of cotton, the LAI, AGB, and canopy SIF all gradually increased from the budding period to the flowering and boll period and began to decline in the boll period. The correlation coefficients of LAI and AGB with cotton yield and canopy SIF both increased from the budding period, reached a maximum during the flowering and boll period, and decreased in the boll period, all of which were significantly positively correlated. The trend of the R\({}^{2}\) of the cotton yield model based on SIF and SIFy closely follows the pattern of LAI and AGB;
2. At different monitoring time periods, the R\({}^{2}\) of the cotton yield estimation model based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, and the optimal observation time for the cotton canopy SIF to estimate the yield was 14:00-15:00. The R\({}^{2}\) of the cotton yield model based on SIF and SIFy increased with the course of fertility from the budding period to the flowering and boll period and decreased in the boll period, and the optimal growth period for the estimation model was the flowering and boll period;
3. Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm for canopy SIFy parameters at 14:00-15:00 during the flowering and boll period (R\({}^{2}\) = 0.9612, RMSE = 66.27 kg\(\cdot\)ha\({}^{-1}\), RPD = 4.264) was followed by the model utilizing the Bagging algorithm (R\({}^{2}\) = 0.8898) and the Ada Boost algorithm (R\({}^{2}\) = 0.8796). Through verification, the applicability of the model is proved.  
Conceptualization, H.W. and Y.D.; methodology, H.W. and Y.D.; software, S.Q.; validation, M.Y.; formal analysis, Q.Y.; resources, Z.Z.; data curation, M.Y. and F.X.; writing--original draft preparation, H.W. and Y.D.; writing--review and editing, L.M., Q.Y. and Y.M.; visualization, H.W.; supervision, Y.D.; project administration, Z.Z. and Z.G.; funding acquisition, Z.Z and Z.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, China (Grant No. 42061058); \"Tianshan Talents\" Training Program, China, Key Scientific and Technological Research Program of XPCC, China (Grant No. 2023 AA008); The Corps \"Strong Green\" Scientific and Technological Innovation Backbone Personnel Project, China (Grant No. 2022 CB002-001); and Eight Division Shihezi City Key Areas of Innovation Team Plan, China (Grant No. 2023 TD01).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.