## 3 Results and Discussion
### Comparisons of Different ML Models for Estimating Leaf Area of \(D\). mobile Leaves
In our study, it can be seen that within the 10 best models MLR5 and MLR4 secured second and seventh position, respectively, based on AR ranking methodology (Table 9). Suitability criteria of MLR to produce good prediction accuracy are dependent on the factors which include the absence of multicollinearity between the predictors (measured by VIF and T), strong linear correlation between the predictors and dependent variable, and finally imposing the cross-validation methodology to prevent the over fit of the models. The results depicted from VIF and T values of our experiment show that L, W, and their variants are linearly independent to each other, i.e., there is an absence of multicollinearity. From the feature correlation heatmap it can be inferred that L, W, and their variants are strongly linearly correlated with leaf area (LA) where r ranges from 0.76 to 0.90. The  
Figure 5: RMSE of GBR with different numbers of n_estimator. 10-fold cross-validation was also performed to prevent overfitting of the models. Although the MLR model for LA prediction in previous studies did not outperform ANN or other decision tree-based models, there are reports in other fields in agreement with our results of outperformance over ANN and other complex models. A study by [97] showed MLR outperformed random forest based on the VIF values and cross-validation techniques for predicting Soil Organic Carbon stocks. Similar results were seen in another report [98] of the outperformance of MLR over other more complex modeling approaches. The reports of better prediction performance of MLR over ANN were further inferred by [99]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Inputs**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} \\ \cline{3-8}  & & \(\mathbf{R^{2}}\) & **MAE** & **RMSE (cm2)** & **R2** & **MAE** & **RMSE (cm2)** \\ \hline LW & ANN2 & 0.96 & 0.87 & 1.27 & 0.96 & 0.86 & 1.13 \\ LW & GBR1 & 0.96 & 0.86 & 1.18 & 0.96 & 0.82 & 1.11 \\ LW & SVR1 & 0.94 & 0.87 & 1.47 & 0.96 & 0.83 & 1.21 \\ LW & MLR1 & 0.96 & 0.90 & 1.2 & 0.96 & 0.9 & 1.24 \\ LW2 & ANN6 & 0.96 & 0.90 & 1.25 & 0.96 & 0.84 & 1.18 \\ LW2 & GBR2 & 0.96 & 0.86 & 1.18 & 0.96 & 0.82 & 1.11 \\ LW2 & SVR2 & 0.94 & 0.89 & 1.52 & 0.96 & 0.84 & 1.23 \\ LW2 & MLR2 & 0.97 & 0.87 & 1.15 & 0.96 & 0.94 & 1.22 \\ LW3 & ANN10 & 0.96 & 0.90 & 1.27 & 0.97 & 0.87 & 1.18 \\ LW3 & GBR3 & 0.96 & 0.86 & 1.18 & 0.96 & 0.82 & 1.11 \\ LW3 & SVR3 & 0.94 & 0.93 & 1.58 & 0.96 & 0.86 & 1.25 \\ LW3 & MLR3 & 0.95 & 1.05 & 1.34 & 0.95 & 1.06 & 1.42 \\ L2W & ANN15 & 0.95 & 0.94 & 1.41 & 0.97 & 0.86 & 1.11 \\ L2W & GBR4 & 0.96 & 0.86 & 1.18 & 0.96 & 0.82 & 1.10 \\ L2W & SVR4 & 0.94 & 0.88 & 1.48 & 0.96 & 0.83 & 1.21 \\ L2W & MLR4 & 0.96 & 0.86 & 1.15 & 0.96 & 0.86 & 1.13 \\ L2W2 & ANN18 & 0.95 & 0.93 & 1.36 & 0.97 & 0.77 & 1.03 \\ L2W2 & GBR5 & 0.96 & 0.86 & 1.18 & 0.96 & 0.82 & 1.10 \\ L2W2 & SVRS & 0.94 & 0.89 & 1.53 & 0.96 & 0.85 & 1.24 \\ L2W2 & MLR5 & 0.97 & 0.84 & 1.16 & 0.96 & 0.85 & 1.13 \\ L2W3 & ANN22 & 0.95 & 0.97 & 1.39 & 0.97 & 0.86 & 1.19 \\ L2W3 & GBR6 & 0.96 & 0.86 & 1.18 & 0.96 & 0.82 & 1.10 \\ L2W3 & GBR6 & 0.96 & 0.86 & 1.18 & 0.96 & 0.82 & 1.10 \\ L2W3 & SVR6 & 0.94 & 0.92 & 1.57 & 0.96 & 0.86 & 1.26 \\ L2W3 & MLR6 & 0.95 & 1.04 & 1.36 & 0.95 & 1.06 & 1.36 \\ L3W & ANN26 & 0.73 & 2.77 & 3.78 & 0.88 & 1.35 & 2.06 \\ L3W & GBR7 & 0.96 & 0.86 & 1.18 & 0.96 & 0.82 & 1.10 \\ L3W3 & SVR7 & 0.94 & 0.88 & 1.49 & 0.96 & 0.85 & 1.25 \\ L3W2 & MLR7 & 0.96 & 0.89 & 1.17 & 0.95 & 0.88 & 1.22 \\ L3W2 & ANN31 & 0.88 & 1.41 & 2.22 & 0.94 & 1.01 & 1.47 \\ L3W2 & GBR8 & 0.96 & 0.86 & 1.18 & 0.96 & 0.91 & 1.10 \\ L3W2 & SVR8 & 0.94 & 0.89 & 1.53 & 0.96 & 0.85 & 1.26 \\ L3W2 & MLR8 & 0.96 & 0.88 & 1.17 & 0.96 & 0.88 & 1.2 \\ L3W3 & ANN35 & 0.91 & 1.29 & 2.34 & 0.91 & 0.97 & 1.31 \\ L3W3 & GBR9 & 0.96 & 0.86 & 1.18 & 0.96 & 0.91 & 1.10 \\ L3W3 & SVR9 & 0.94 & 0.92 & 1.56 & 0.96 & 0.86 & 1.28 \\ L3W3 & MLR9 & 0.95 & 1.11 & 1.42 & 0.94 & 1.12 & 1.46 \\ \hline \end{tabular}
\end{table}