## 4 Discussion  
### Carrot Crop Growth Assessment  
The results are in agreement with the literature, with regard to the capability of the selected S2 bands alone (Band 2, Band 3, Band 4, and Band 8A) to extract useful information about the carrot plant (health, and growth monitoring) status and crop yield prediction (R\({}^{2}\)\(\geq\) 0.62, %RMSE \(\leq\) 24) [21]. Moreover, the use of the SIPI index with S2 bands improved the performance of ML prediction models. Using the average value of each band or index reported in [21], the ML algorithms can be used to predict the carrot yields at 70 DAS with an error of 27%. They also reported that the GNDVI and RDVI are the best VIs for the early prediction of yield. Band 8A was found to have a better predictive ability in the early prediction of yield with the R\({}^{2}\) of 0.68, and it conformed with the study by [PERSON] et al. [22]. RDVI relies on the reflectance information provided in the red and NIR region of the spectrum, retrieving in one of the absorption peaks of chlorophyll and b. Nevertheless, [PERSON] et al. [22] identified other photosynthetic pigments in carrot plants whose absorption peaks are located in the blue region of the spectrum. The carrot crop was monitored throughout the growth period with the help of chlorophyll content (i.e., SPAD readings) across the DAS. In general, higher SPAD readings indicate a higher relative chlorophyll content and higher tissue nitrogen content, while lower SPAD readings indicate a lower relative chlorophyll content and lower tissue nitrogen content. As per Figure 4a, the SPAD readings reached their peak at 60 DAP and followed a similar trend at 75 DAP. Thereafter, the SPAD values dropped. The progressive increment in SPAD readings is due to the healthy condition of the crop and agricultural practices that are implemented. Furthermore, factors such as moisture, salt stress, etc. alter the vital function of the plants, resulting in a decrease in the SPAD readings [50]. Nitrogen maps were created using the simulated LCC of 75 DAP, as most of the studied VIs were found to be significant with  
Figure 7: Predicted carrot yield maps generated using a random forest (RF) algorithm. (**a**) Sentinel-2 bands, (**b**) vegetation indices, (**c**) Sentinel-2 bands + SPAD values, (**d**) vegetation indices + SPAD values, and (**e**) Sentinel-2 bands + vegetation indices + SPAD values, as predictors in model development. The SPAD values used in the model were with the Pearson correlation coefficient (R\({}^{2}\)) \(>\) 70 with yield (Y\({}_{\Lambda}\)).  
SPAD readings, still suitable for the farmer to obtain location-specific information of crop N status in order to apply a side dressing. The crop development at 90 DAP does not express large variation effects with VIs alone, whereas the model combined with simulated LCC expressed significant correlation. Therefore, it is recommended that we co-combine the SPAD meter readings with progressing with the development of the crop, due to the effect of the crop development and increased vigor, which is an indicator of plant health and nutrient status. SPAD meter readings can be used to monitor the nutrient status of the corn crop and adjust fertilizer application rates accordingly.