## 4 Discussion
As the proportion of the training data increased, it was found that the model's performances improved (Figure 7) when validated over the train and entire datasets [26; 53; 58]. This observation held true for both input groups where REFs+VIs inputs significantly outperformed REFs inputs in the models (_p_ = 0.01, Table 5). When validated over the training and entire datasets, it was observed that ML models performed better than statistical models for SY estimation, irrespective of the input groups (Figure 7). The performance of statistical models like SLR and PLSR was enhanced and was comparable to ML models as the cofounding effects were already eliminated by eliminating the collinearity of input variables [63]. When compared to ML models, SLR and PSLR were the least-best performers irrespective of the input groups possibly due to their nature of \begin{table}
\begin{tabular}{l c c} \hline \hline
**Variable** & \(p\) **Value (r)** & \(p\) **Value (rRMSE)** \\ \hline Model & \textless{}0.001 & \textless{}0.001 \\ \hline Train–test split & \textless{}0.001 & \textless{}0.001 \\ \hline Dataset & \textless{}0.001 & \textless{}0.001 \\ \hline Input group & \textless{}0.001 & \textless{}0.001 \\ \hline Train–test split: Dataset & \textless{}0.001 & \textless{}0.001 \\ \hline Train–test split: Input group & 0.217 & 0.330 \\ \hline Dataset: Input group & \textless{}0.001 & 0.820 \\ \hline Train–test split: Dataset: Input group & 0.002 & 0.964 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Impact of input parameters on soybean yield estimation models’ performance. limitedly exploring non-linear complexities between the input and output variables [63]. The relatively smaller size of the data and the need for a larger size of training data for building optimal neural networks affected the performance of ANN [26; 58] which was the best performer only at a 95:5 split ratio. Overall, RF outperformed all other models due to its dependency on decision trees that can solve overfitting issues irrespective of dataset size [26]. This is completed by having numerous random subsamples of the original dataset during the training of decision trees. It is also because of this that RF can handle numerous model parameters, reduce estimate bias, and produce more accurate and reliable predictions for novel instances that are not always present in the training dataset [64]. The viability of integrating SUAS and ML techniques using different data splits for SY estimation was shown in this study. Incorporating agroclimatic conditions like soil and weather parameters as well as data collection over several growth stages and cropping seasons may be a potential avenue to improve the model's performance. It is also worth noting that the cost of implementing this technology such as initial investment in drones, sensors and data processing software, especially for small scale farmers still poses a challenge. Furthermore, handling large volumes of agricultural data, including aerial imagery and crop information, raises concerns about data privacy and security. Safeguarding sensitive information and complying with data protection regulations may present challenges in the implementation of the study. Our next goals are to incorporate these findings into satellite imaging platforms and determine the optimum stage for SCN infestation detection and SY prediction. This will consider providing open-source platforms such as web tools that are affordable and easily accessible to farmers and consider data privacy policies. Based on these estimations, farmers would be able to employ precision management strategies to enhance yield and profitability.