## 2 Results
### Performance of SPAD Prediction Regression Models
#### 2.3.2 Wheat SPAD Values Prediction
Figure 6 shows the linear regression between the predicted and observed SPAD values for the different segmentation methods using the wheat testing dataset. The MLP-FR model demonstrated better prediction accuracy (R\({}^{2}\) = 0.821), outperforming all the other methods, while the ExG-FR model had the lowest prediction accuracy (R\({}^{2}\) = 0.546). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CVI**} & \multirow{2}{*}{**Mathematical Expression**} & \multicolumn{6}{c}{**Correlation Coefficient(r)**} \\ \cline{3-8}  & & & \multicolumn{4}{c}{**Segmentation Method**} \\ \cline{3-8}  & & & **ExG** & **ExGR** & **MLP** & **SVM** & **RF** \\ \hline Green & - & 0.242 & 0.212 & 0.443 & 0.38 & 0.291 \\ Blue & - & 0.377 & \(-\)0.344 & \(-\)0.618 & \(-\)0.522 & \(-\)0.552 \\ ExG [21] & 2 \(\times\) G \(-\) R \(-\) B & 0.704 & 0.726 & 0.927 & 0.841 & 0.877 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Correlation analysis of color vegetation indices (CVIs) and measured SPAD readings for wheat. R, G, B, H, S, B are the average red, green, blue, hue, saturation, and brightness values respectively. MLP, RF, SVM, ExGR, and ExG are the segmentation methods using multilayer perceptron, random forest, support vector machine, excess green, and excess green–red segmentation methods, respectively. Figure 5: Scatterplots of predicted and observed SPAD values for the glasshouse plants. MLP-GR, RF-GR, SVM-GR, ExGR-GR, and ExG-GR represent the glasshouse-based regression models for the multilayer perceptron, random forest, support vector machine, excess green, and excess green–red segmentation methods, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CVI**} & \multirow{2}{*}{**Mathematical Expression**} & \multicolumn{6}{c}{**Correlation Coefficient(r)**} \\ \cline{3-7}  & & \multicolumn{6}{c}{**Segmentation Method**} \\ \cline{3-7}  & & **ExG** & **ExGR** & **MLP** & **SVM** & **RF** \\ \hline ExR [7] & \(1.4\times\,\mathrm{R}\,-\,\mathrm{G}\) & \(0.675\) & \(0.722\) & \(0.845\) & \(0.751\) & \(0.803\) \\ CIVE [22] & \(0.441\times\,\mathrm{R}\,-\,0.811\times\,\mathrm{G}\,+\,0.385\times\,\mathrm{B}\,+\, 18.78\) & \(0.601\) & \(-0.652\) & \(-0.842\) & \(-0.773\) & \(-0.782\) \\ ERI [23] & \((\mathrm{R}\,-\,\mathrm{G})\,\times\,(\mathrm{R}\,-\,\mathrm{B})\) & \(-0.407\) & \(-0.476\) & \(-0.681\) & \(-0.572\) & \(-0.644\) \\ DCGI [24] & \((\mathrm{H}\,-\,60)/[60+(1\,-\,\mathrm{S})+(1\,-\,\mathrm{B})]/3\) & \(0.623\) & \(0.690\) & \(0.881\) & \(0.791\) & \(0.747\) \\ GR & \(\mathrm{G}\,-\,\mathrm{R}\) & \(-0.551\) & \(-0.606\) & \(-0.740\) & \(-0.657\) & \(-0.671\) \\ COM1 [17] & ExG + CIVE + ExGR & \(0.532\) & \(0.682\) & \(0.813\) & \(0.722\) & \(0.78\) \\ GBRG [21] & \((\mathrm{G}\,-\,\mathrm{R})/(\mathrm{R}\,-\,\mathrm{G})\) & \(0.690\) & \(0.743\) & \(0.904\) & \(0.781\) & \(0.811\) \\ EGI [23] & \((\mathrm{G}\,-\,\mathrm{R})/(\mathrm{G}\,-\,\mathrm{B})\) & \(0.111\) & \(0.251\) & \(0.412\) & \(0.322\) & \(0.352\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 6: Scatterplots of predicted and observed SPAD values for the field plants. MLP-FR, RF-FR, SVM-FR, ExGR-FR, and ExG-FR are the field-based regression models for the multilayer perceptron, random forest, support vector machine, excess green, and excess green–red segmentation methods, respectively.