## 5 Conclusions  
In this study, an attempt was made to identify the optimum window for the early prediction of carrot yield. The specific conclusions of the study include the following:  
* The correlation between chlorophyll (SPAD values) content and carrot yield (Y\({}_{\text{A}}\)) was found to be significant (R2 = 0.78; \(p\) = 0.0001) at a crop age of 75 DAP. However, a weak correlation was reported at 30 DAP (R2 = 0.058; \(p\) = 0.493) and 105 DAP (R2 = 0.42; \(p\) = 0.416).
* To monitor the development of carrot crops and to assess the optimal window for the early prediction of carrot yield, five scenarios/models adopting the RF algorithm, a machine-learning tool, were performed. Of the studied five scenarios, the algorithm  
Figure 9: Standards of coefficients between carrot yield and simulated LCC layers for yield prediction in RF algorithm.  
with the maximum predictors (individual S2 bands + VIs + SPAD values) performed better (R\({}^{2}\) = 0.82; RMSE = 7.8 t ha\({}^{-1}\)) compared to other scenarios, whereas the model with an input of VIs and SPAD values returned a moderate correlation (R\({}^{2}\)\(\leq\) 0.67; RMSE = 10.2 t ha\({}^{-1}\)). A poor performance (R\({}^{2}\)\(\geq\) 0.46; RMSE = 26.2 t ha\({}^{-1}\)) was noted with Scenario 1, i.e., only individual S2 bands as inputs for the RF model.
* Furthermore, Model e confirmed that the incorporation of LCC in ML modelling improves the model performance and reduces the bias from 10.4 t ha\({}^{-1}\) to 52 t ha\({}^{-1}\). Hence, the current study aimed to monitor the growth and assess and model the carrot pre-harvest yield using machine-learning techniques. The optimal period for the early management of carrot crops was found to be between 60-75 DAP and was formulated as part of the self-sustainability of vegetable crops and their water footprint. This study assists in exploring the possibilities of non-destructive methods, such as SPAD measurements and free-of-cost Sentinel-2 data in monitoring the growth and assessment of carrot yield employing ML techniques.  
Conceptualization, R.M. and K.A.A.-G.; methodology, R.M., K.A.A.-G., and E.T.; software, R.M., and M.K.E.; validation, E.T., and K.A.A.-G.; formal analysis, R.M. and H.F.E.; investigation, E.T.; resources, R.M.; data curation, M.K.E., H.F.E., A.A.A., and R.M.; writing--original draft preparation, R.M.; writing--review and editing, R.M., K.A.A.-G., and E.T.; visualization, K.A.A.-G.; supervision, R.M.; project administration, K.A.A.-G.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Plan for Science, Technology and Innovation (MAARIFAH/GRANTS program), King Abdul Aziz City for Science and Technology, Riyadh, Saudi Arabia, through the project number 2-17-04-001-0016, and the Article processing charges (APC) was also funded by the project number 2-17-04-001-0016.  
Not applicable.  
The Sentinel images used in this study are openly available at [[https://dataspace.copernicus.eu/browser/](https://dataspace.copernicus.eu/browser/)]([https://dataspace.copernicus.eu/browser/](https://dataspace.copernicus.eu/browser/)) (accessed on 9 February 2024). Other data used to support the findings of this study are available from the corresponding author upon request.  
The authors are grateful to the National Plan for Science Technology and Innovation (MAARIFAH/GRANTS program), King Abdul Aziz City for Science and Technology, Riyadh, Saudi Arabia for funding this study. The unstinted co-operation and support extended by the Farm manager and the supporting staff in carrying out the field research work at the Tawdeehiya Farms are gratefully acknowledged.  
The authors declare no conflicts of interest.