## 3 Experimental Result
### Comparative Experiment  
To further assess the practical predictive capability of ReSE-AP Net, a series of benchmark models were selected for comparison including conventional machine learning algorithms (PLSR, Ridge, SVR, RF, XGBoost) and commonly used deep-learning architectures in this domain (VGG-11, TCN, ResNet-152, Transformer). All models were trained on the LUCAS 2009 dataset under identical preprocessing procedures to ensure experimental fairness. The training hyper-parameters were uniformly set as follows: 3000 epochs, a learning rate of 0.0003175, a batch size of 320, the Adam optimizer, and mean squared error (MSE) loss. The resulting performance on the independent test set is summarized in Table 4 (comparison of R\({}^{2}\)) and Table 5 (comparison of RMSE). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model** & **Clay** & **Silt** & **Sand** & **PH in CaCl\({}_{2}\)** & **PH in H\({}_{2}\)O** & **OC** & **CaCO\({}_{3}\)** & **N** & **P** & **K** & **CEC** \\ \hline PLSR & 0.763 & 0.549 & 0.649 & 0.895 & 0.886 & 0.917 & 0.903 & 0.868 & 0.277 & 0.371 & 0.724 \\ Ridge & 0.746 & 0.505 & 0.610 & 0.872 & 0.864 & 0.907 & 0.883 & 0.845 & 0.249 & 0.335 & 0.688 \\ SVR & 0.755 & 0.522 & 0.583 & 0.622 & 0.642 & 0.937 & 0.903 & 0.902 & 0.196 & 0.277 & 0.738 \\ RF & 0.524 & 0.416 & 0.453 & 0.610 & 0.622 & 0.874 & 0.656 & 0.797 & 0.050 & 0.139 & 0.529 \\ XGBoost & 0.562 & 0.423 & 0.491 & 0.684 & 0.711 & 0.893 & 0.765 & 0.810 & 0.082 & 0.161 & 0.561 \\ VGG11 & 0.855 & 0.739 & 0.785 & 0.928 & 0.920 & 0.961 & 0.959 & 0.932 & 0.364 & 0.480 & 0.808 \\ TCN & 0.834 & 0.705 & 0.767 & 0.938 & 0.928 & 0.955 & 0.954 & 0.926 & 0.280 & 0.469 & 0.769 \\ ResNet152 & 0.734 & 0.557 & 0.618 & 0.834 & 0.849 & 0.941 & 0.942 & 0.891 & 0.160 & 0.338 & 0.716 \\ Transformer & 0.850 & 0.720 & 0.770 & 0.940 & 0.930 & 0.960 & 0.960 & 0.940 & 0.410 & 0.600 & 0.830 \\
**ReSE-AP Net** & **0.865** & **0.747** & **0.806** & **0.938** & **0.928** & **0.966** & **0.964** & **0.939** & **0.415** & **0.557** & **0.817** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison table of R\({}^{2}\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model** & **Clay** & **Silt** & **Sand** & **PH in CaCl\({}_{2}\)** & **PH in H\({}_{2}\)O** & **OC** & **CaCO\({}_{3}\)** & **N** & **P** & **K** & **CEC** \\ \hline ResBlock & 0.8162 & 0.6408 & 0.7442 & 0.9210 & 0.9173 & 0.9574 & 0.9480 & 0.9101 & 0.3179 & 0.4810 & 0.7062 \\ ResNet + SE & 0.8482 & 0.7255 & 0.7912 & 0.9288 & 0.9200 & 0.9613 & 0.9512 & 0.9224 & 0.3590 & 0.5100 & 0.8019 \\ ReSE-AP Net & 0.8653 & 0.7467 & 0.8055 & 0.9384 & 0.9278 & 0.9656 & 0.9642 & 0.9393 & 0.4148 & 0.5568 & 0.8172 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison table of R\({}^{2}\) in the ablation experiments. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model** & **Clay** & **Silt** & **Sand** & **PH in CaCl\({}_{2}\)** & **PH in H\({}_{2}\)O** & **OC** & **CaCO\({}_{3}\)** & **N** & **P** & **K** & **CEC** \\ \hline PLSR & 0.763 & 0.549 & 0.649 & 0.895 & 0.886 & 0.917 & 0.903 & 0.868 & 0.277 & 0.371 & 0.724 \\ Ridge & 0.746 & 0.505 & 0.610 & 0.872 & 0.864 & 0.907 & 0.883 & 0.845 & 0.249 & 0.335 & 0.688 \\ SVR & 0.755 & 0.522 & 0.583 & 0.622 & 0.642 & 0.937 & 0.903 & 0.902 & 0.196 & 0.277 & 0.738 \\ RF & 0.524 & 0.416 & 0.453 & 0.610 & 0.622 & 0.874 & 0.656 & 0.797 & 0.050 & 0.13 As shown in Tables 4 and 5, partial least squares regression (PLSR) delivered the best overall performance among the conventional machine learning models, followed by support vector regression (SVR). Within the deep-learning cohort, the Transformer architecture achieved the highest aggregate accuracy across elemental predictions. In contrast, ReSE-AP Net outperformed all machine learning models for every element: for OC, its R\({}^{2}\) exceeded that of the best machine learning performer (SVR) by roughly 2.8% and surpassed the poorest performer (random forest, RF) by about 9.2%.