## 4 Experimental Setting
RR uses regularization terms and penalizes large coefficients, helping to mitigate the effects of multicollinearity and prevent overfitting. The regularization term is controlled by a hyperparameter (usually denoted as \"alpha\" or \"lambda\"). LR does not include a regularization term in the objective function. It is more prone to overfitting when dealing with highly correlated features (multicollinearity) or when the number of features is close to or exceeds the number of observations. PN maintains the best algorithm for K prediction, and, again, the performances of RR and LR are very similar for predicting K. As explained, RR is a modified version of LR that adds a regularization term to address certain issues, particularly multicollinearity. If the correlation between independent variables is high, RR can provide more stable and reliable coefficient estimates compared to LR. Since the performance of RR is better in predicting K, this indicates that the dataset for the training possesses multicollinearity. Likewise, the best technique for P prediction is PN, and it is observed that the performance of PN in this nutrient prediction is the best compared to other nutrients. All the other algorithms also had better scores, which indicates that the values in the features used for training the P prediction are more homogeneous compared to the earlier models. Similarly, PN achieved the best performance in comparison with the other models. All models had lower performances in predicting N compared to predicting P. It is also observed that the performance of SW in predicting N is similar to that predicting P, when compared against RR and LR. Although LR and RR show stability and generalizability across different datasets, SW has better performance in this nutrient compared to Ca and Mg because of its simplicity drawback and tendency to assume that the relationship between variables is best represented by a combination of selected features. Figures 4-8 depict the Streamlit outputs for the single-nutrient prediction of Ca, Mg, K, P, and N, respectively, based on the best-performing model, PN. The predicted values for each nutrient are computed utilizing the PN model, taking into account spatial-temporal parameters and other relevant nutrient inputs. The diagrams illustrate that the predicted nutrient concentrations are used to recommend the amount of nutrient recovery, by comparing them against the benchmark nutrient values. \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **R\({}^{2}\) Score** & **MAE** & **RMSE** \\ \hline ModelEN\({}_{\text{N}}\) & 0.3006 & 0.4524 & 0.6326 \\ ModelPN\({}_{\text{N}}\) & **0.5862** & **0.3808** & **0.4866** \\ ModelESW\({}_{\text{N}}\) & 0.4240 & 0.4388 & 0.5741 \\ ModelRR\({}_{\text{N}}\) & 0.5508 & 0.3657 & 0.5070 \\ ModelLS\({}_{\text{N}}\) & 0.1994 & 0.4948 & 0.6768 \\ ModelLR\({}_{\text{N}}\) & 0.5532 & 0.3661 & 0.5056 \\ AVG & 0.4357 & 0.4164 & 0.5638 \\ STDEV & 0.1574 & 0.0535 & 0.0777 \\ \hline \end{tabular}
\end{table} 
Table 11: Performance of N prediction using approach 1. Figure 4: Rice Ca nutrient prediction based on other nutrients of N, P, K, and Mg.  
Figure 5: Rice Mg nutrient prediction based on other nutrients of N, P, K, and Ca. Figure 6: Rice K nutrient prediction based on other nutrients of N, P, Mg, and Ca. Figure 7: Rice P nutrient prediction based on other nutrients of N, K, Mg, and Ca. Referring to the aforementioned Streamlit interface for individual nutrients, including Ca, Mg, K, P, and N, the application provides essential values for \"predicted\", \"Best practice (Range)\", \"Best practice (Average)\", and \"Intervention.\" The predicted values for each nutrient are computed utilizing the PN model, taking into account spatial-temporal parameters and other relevant nutrient inputs. The \"Best practice Range\" and \"Best practice Average\" values specify the optimal ranges and averages of nutrient concentrations, offering valuable benchmarks for nutrient levels.