# Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
## 4 Discussion  
It is possible to observe the symmetrical trend of the data through a graphical analysis (Figure 5). This result corroborates with the smaller CV (Table 3) determined by the yield variable, thus indicating the potential of the variable to be estimated. However, a slight trend to the left of the graph is still noted, indicating inherent characteristics of a positive asymmetric curve. In regard to the indices and bands used to create the spectral estimation and prediction models, the correlation analysis showed that the vegetation indices and bands do not fully explain the yield variation because there are many factors responsible for the final yield, but these indices can be useful as indicators of corn yield. Based on the RMSE%, MAPE% and R2 values obtained, spectral model performance reflects the possibility of remotely \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics Analyzed** & \multicolumn{2}{c}{**Study Area**} \\ \hline Observed & & Dear \\ Average & 9137.66 & 8448.98 \\ Standard Deviation & 924.62 & 917.76 \\ RMSE & 12.31 & \\ MAPE & 8.38 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics of observed and estimated surfaces indicating root mean square error percentage (RMSE) and mean absolute percentage error (MAPE). Figure 9: Difference between observed and estimated values. estimating and predicting corn yield, despite the high spectral variability in the canopy caused by biotic and abiotic factors in the production environment. Thus, the lowest values of MAPE% and RMSE% in the study area were found during R2, which indicates that R2 may be the ideal period for estimating yield in this phenological stage, where the plant is in the process of pollination. The stigma-styles were visible as they tried to capture the pollen grains [41]. The difference of 4 days indicates that the stigma-styles of each plant were already pollinated or in an advanced stage because this pollination period usually occurs between 2 and 3 days [42]. In a study [8], the authors tried to estimate the yield of the corn crop through multispectral images using the RNA Bayesian algorithm, where they determined that the best deterministic coefficient results for the generated estimation models are found 2 months before the harvest period, which may indicate a tendency for the estimation of yield in corn culture to present its apex before the harvest. Overall, the results indicate that the algorithms with the highest incidence in the phenological stages were SVM, NN, and RT, which demonstrated a better performance in the phenological stages of VE and R3 for SVM; V8, V11, R1 and R2 for NN; V5, R4, and R6 for RT. The SVM algorithm is widely used in maize crops and demonstrates the ability to model different agronomic parameters, such as disease detection [43,44] and transpiration [45,46]. On the other hand, NN is used more frequently for yield estimation in maize crops, both through images obtained from remotely piloted acircrafts [47] and from orbital sensors [48]. To estimate yield through MODIS and GLASS satellite images, [PERSON] et al. [48] used NN, which was based on a back-propagation algorithm. In the aforementioned study, the authors estimated yield with an MAPE value of less than 10%, and an RMSE at 700 kg/ha in most cases. Based on the results in VE and V5, it is possible to observe the presence of visible bands, and the vegetation indices derived from them. The behavior of electromagnetic radiation in the visible range is mainly determined by chlorophyll. The mesophyll pigments tend to absorb wavelengths in the blue and red range [49]. Thus, the radiation from the green channel is reflected in a greater incidence, which may justify the presence of the green band and vegetation indices such as GNDVI and CI g in the models of the phenological stages of VE and V5. Based on the results in VE and V5, it is possible to observe the presence of visible bands, and vegetation 346 indices derived from them.