## 3 Results
### Soybean Yield Estimation with ML
#### 3.3.4. Impact of Dataset Sizes on Soybean Yield Modeling  
SY estimation accuracy diminished with increase in data split ratios (r\({}_{\text{test}}\): 0.83-0.79, rRMSE\({}_{\text{test}}\): 20.30-39.60%, Figures 6b and 7, Table 4) when models were validated for performance over the test dataset. On the contrary, the SY estimation accuracy enhanced with the increase in training dataset size (r\({}_{\text{train}}\): 0.95-0.97, rRMSE\({}_{\text{train}}\): 13.10-8.72%, rentire: 0.84-0.93, rRMSE\({}_{\text{entire}}\): 20.60-13.32%, Figures 6a,c and 7, and Table 4) for the models when validated over train and entire datasets. When validated over those datasets at a 95:5 split, RF with REFs+VIs as the inputs, yielded the best SY estimation performance (r\({}_{\text{train}}\) = 0.97, rRMSE\({}_{\text{train}}\) = 8.72%, rentire = 0.93, rRMSE\({}_{\text{entire}}\) = 13.32%, Figures 6a,c and 7, and Table 4). RF with REFs+VIs as the input variables also was the best performer when it was validated over the test dataset, at the 60:40 split ratio.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & \multicolumn{3}{c}{**Dataset: Train**} & \multicolumn{3}{c}{**Dataset: Test**} & \multicolumn{3}{c}{**Dataset: Entire**} \\ \hline \multirow{2}{*}{**Train:Test Ratio**} & **Input** & **Best** & **r** & **rRMSE (\%)** & **Best** & **r** & **rRMSE (\%)** & **Best** & **r** & **rRMSE (\%)** \\ \hline \multirow{2}{*}{60:40} & REFs & \multirow{2}{*}{RF} & 0.96 & 12.00 & PLSR & 0.80 & 21.40 & \multirow{2}{*}{RF} & 0.84 & 0.84 & 20.00 \\  & REFs+VIs & & 0.97 & 10.10 & RF & 0.84 & 20.00 & & 0.88 & 16.90 \\ \hline \multirow{2}{*}{65:35} & REFs & \multirow{2}{*}{RF} & 0.96 & 11.90 & PLSR & 0.81 & 22.20 & \multirow{2}{*}{RF} & 0.85 & 19.30 \\  & REFs+VIs & & 0.97 & 10.00 & SLR & 0.81 & 23.00 & & 0.90 & 16.10 \\ \hline \multirow{2}{*}{70:30} & REFs & \multirow{2}{*}{RF} & 0.96 & 11.80 & SLR & 0.80 & 21.80 & \multirow{2}{*}{RF} & 0.85 & 19.10 \\  & REFs+VIs & & 0.97 & 9.96 & SLR & 0.81 & 21.60 & & 0.90 & 15.90 \\ \hline \multirow{2}{*}{75:25} & REFs & \multirow{2}{*}{RF} & 0.96 & 11.40 & SLR & 0.79 & 21.00 & \multirow{2}{*}{RF} & 0.85 & 19.30 \\  & REFs+VIs & & 0.97 & 9.62 & SLR & 0.77 & 22.00 & & 0.90 & 15.50 \\ \hline \multirow{2}{*}{80:20} & REFs & \multirow{2}{*}{RF} & 0.96 & 11.60 & SLR & 0.76 & 22.30 & \multirow{2}{*}{RF} & 0.88 & 17.60 \\  & REFs+VIs & & 0.97 & 9.45 & SLR & 0.73 & 23.5 & & 0.91 & 15.20 \\ \hline \multirow{2}{*}{85:15} & REFs & \multirow{2}{*}{RF} & 0.96 & 11.50 & SLR & 0.61 & 23.50 & \multirow{2}{*}{RF} & 0.88 & 17.20 \\  & REFs+VIs & & 0.97 & 9.20 & SLR & 0.6 & 24.80 & & 0.91 & 14.90 \\ \hline \multirow{2}{*}{90:10} & REFs & \multirow{2}{*}{RF} & 0.96 & 11.50 & SLR & 0.65 & 27.30 & \multirow{2}{*}{RF} & 0.91 & 15.80 \\  & REFs+VIs & & 0.97 & 9.08 & SLR & 0.66 & 27.40 & & 0.93 & 13.60 \\ \hline \multirow{2}{*}{95:5} & REFs & \multirow{2}{*}{RF} & 0.96 & 11.60 & SLR & 0.71 & 38.00 & \multirow{2}{*}{RF} & 0.93 & 14.60 \\  & REFs+VIs & & 0.97 & 8.72 & ANN & 0.79 & 39.60 & & 0.93 & 13.32 \\ \hline \hline \end{tabular}  
* The input groups are designated as REFs (reflectance only) and REFs+VIs (reflectance and selected vegetation indices).  
\end{table}
Table 4: _Cont._