## 2 Results
### Performance of SPAD Prediction Regression Models
#### 2.3.2 Wheat SPAD Values Prediction  
Table 4 shows the Pearson correlations coefficient between the selected CVIs and SPAD readings. The MLP segmented method shows a high correlation with the SPAD values for all the extracted CVIs, with the highest observed for ExG CVI (0.927) and the lowest for EGI CVI (0.412). The ExG segmentation method yielded relatively low correlations, with the highest correlation for ExG CVI (0.704) and lowest for EGI CVI (0.111), indicating poorly segmented images. Among the extracted CVIs, ExR, CIVE and DGCI demonstrated high correlations with all the segmentation methods, which is consistent with the finding of [PERSON] et al. [25]. Table 5 shows the performance of the regression models for the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Correlation Coefficient (r)**} \\ \cline{3-7}
**CVI** & **Mathematical Expression** & & \multicolumn{6}{c}{**Segmentation Method**} \\ \cline{3-7}  & & **ExG** & **ExGR** & **MLP** & **SVM** & **RF** \\ \hline Green & - & 0.420 & 0.291 & 0.554 & 0.418 & 0.459 \\ Blue & - & \(-\)0.228 & \(-\)0.424 & \(-\)0.682 & \(-\)0.562 & \(-\)0.605 \\ ExG [21] & 2 \(\times\) G \(-\) R \(-\) B & 0.742 & 0.763 & 0.893 & 0.824 & 0.838 \\ ExR [7] & 1.4 \(\times\) R \(-\) G & 0.692 & 0.822 & 0.885 & 0.745 & 0.820 \\ CIVE [22] & 0.441 \(\times\) R \(-\) & 0.670 & 0.745 & 0.924 & 0.787 & 0.822 \\  & 0.811 \(\times\) G + 0.385 \(\times\) B \(+\) 18.78 & \(-\)0.551 & \(-\)0.568 & \(-\)0.661 & \(-\)0.517 & \(-\)0.642 \\ ERI [23] & (H \(-\) 60)/(60 \(+\) (1 \(-\) S) \(+\) (1 \(-\) B)]/3 & 0.672 & 0.759 & 0.948 & 0.821 & 0.850 \\ DGCI [24] & G \(-\) R & \(-\)0.585 & \(-\)0.721 & \(-\)0.842 & \(-\)0.766 & \(-\)0.671 \\ COMI [17] & ExG \(+\) CIVE \(+\) ExGR & 0.623 & 0.748 & 0.901 & 0.792 & 0.782 \\ GBRG [21] & (G \(-\) R)/(R \(-\) G) & 0.689 & 0.824 & 0.870 & 0.844 & 0.802 \\ EGI [23] & (G \(-\) R)/(G \(-\) B) & 0.231 & 0.425 & 0.464 & 0.382 & 0.295 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation analysis of color vegetation indices (CVIs) and SPAD readings for cowpea. R, G, and B are the average red, green and blue values. The ExG, ExGR, MLP, SVM, and RF are the excess green, excess green–red, multilayer perceptron, support vector machines, and random forest segmentation methods, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Training Set** & & **Testing Set** & \\ \cline{2-6}
**Model** & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline MLP-GR & 0.942 & 2.152 & 2.120 & 0.904 & 2.682 & 2.931 \\ RF-GR & 0.881 & 3.885 & 2.581 & 0.837 & 3.958 & 3.780 \\ SVM-GR & 0.864 & 4.255 & 3.601 & 0.826 & 3.620 & 3.881 \\ ExGR-GR & 0.790 & 6.102 & 4.512 & 0.747 & 5.790 & 4.910 \\ ExG-GR & 0.772 & 6.881 & 5.155 & 0.701 & 7.151 & 6.851 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the regression models during training and testing for glasshouse data. MLP-GR, RF-GR, SVM-FR, ExGR-GR, and ExG-GR are the glasshouse-based regression models for the multilayer perceptron, random forest, support vector machine, excess green, and excess green–red segmentation methods, respectively. training and testing datasets. The MLP-FR model exhibited the best performance, achieving the highest R\({}^{2}\) score for both training (0.893) and testing (0.821) datasets, with the lowest MAE scores (2.520 and 3.233, respectively). This indicates that the MLP method segmented the images well, with minimal errors. RF-FR and SVM-FR followed with R\({}^{2}\) = 0.815 and 0.791, respectively, for training, and 0.775 and 0.747, respectively, for testing. The ExG-FR model had relatively the lowest performance, with R\({}^{2}\) = 0.642 and 0.521 for training and testing, respectively. This could be attributed to the poorly segmented images obtained using the ExG method, resulting in many misclassified pixel features for training the regression model.