# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 3 Results
### Evaluation Results of LSTM-CNN-Attention  
This section presents the predictive performance of key soil properties, including OC, N, CaCO\({}_{3}\), and pH in H\({}_{2}\)O. To assess the efficacy of the LSTM-CNN-Attention model, ablation experiments were conducted by systematically removing individual components (LSTM, CNN, and Attention) to evaluate their contributions to the model's overall performance. The results are detailed in Table 5, with performance metrics comprising R\({}^{2}\), RMSE, MAE, and RPD. The findings indicate that the proposed LSTM-CNN-Attention model outperforms all other variations across all evaluated metrics and soil properties. In particular, the model demonstrates excellent predictive accuracy, achieving R\({}^{2}\) values above 0.9 for all soil properties, including 0.949 for OC, 0.916 for N, 0.943 for CaCO\({}_{3}\), and 0.926 for pH. Moreover, the model exhibits strong reliability, with RPD values exceeding 3.0, including the highest RPD of 5.377 for CaCO\({}_{3}\). These results highlight the model's ability to deliver both accurate and reliable predictions of soil properties. The ablation experiments underscore the necessity of each component within the proposed model. As presented in Table 5, the removal of any module resulted in a decline in predictive performance, affirming the importance of integrating LSTM, CNN, and Attention mechanisms. The results demonstrated that the CNN model achieved relatively \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Without Removing Spectral Data**} & \multicolumn{3}{c}{**Removing Spectral Data**} \\ \cline{3-10}  & & **R\({}^{2}\)** & **RMSE** & **MAE** & **RPD** & **R\({}^{2}\)** & **RMSE** & **MAE** & **RPD** \\ \hline OC & LSTM-CNN-Attention & 0.930 & 19.592 & 9.34 & 3.748 & 0.949 & 16.687 & 8.599 & 3.940 \\  & CNN & 0.897 & 22.758 & 12.517 & 3.262 & 0.913 & 21.970 & 11.020 & 3.379 \\  & PISR & 0.868 & 26.798 & 14.843 & 2.771 & 0.879 & 25.816 & 13.941 & 2.876 \\  & RF & 0.904 & 22.481 & 11.059 & 3.303 & 0.918 & 21.281 & 10.752 & 3.489 \\ \hline N & LSTM-CNN-Attention & 0.905 & 1.005 & 0.624 & 3.692 & 0.916 & 0.993 & 0.580 & 3.737 \\  & CNN & 0.825 & 1.437 & 0.928 & 2.385 & 0.829 & 1.419 & 0.777 & 2.415 \\  & PISR & 0.812 & 1.705 & 1.027 & 2.010 & 0.828 & 1.502 & 0.946 & 2.282 \\  & RF & 0.848 & 1.331 & 0.779 & 2.574 & 0.849 & 1.331 & 0.777 & 2.574 \\ \hline CaCO\({}_{3}\) & LSTM-CNN-Attention & 0.934 & 34.641 & 13.123 & 5.180 & 0.943 & 33.370 & 12.990 & 5.377 \\  & CNN & 0.886 & 46.981 & 18.842 & 2.984 & 0.889 & 46.770 & 18.420 & 2.997 \\  & PISR & 0.832 & 48.820 & 24.953 & 2.871 & 0.841 & 48.603 & 24.409 & 2.884 \\  & RF & 0.910 & 40.123 & 18.164 & 3.494 & 0.919 & 39.850 & 18.092 & 3.518 \\ \hline pH(H\({}_{2}\)O) & LSTM-CNN-Attention & 0.923 & 0.370 & 0.266 & 2.702 & 0.926 & 0.364 & 0.265 & 3.352 \\  & CNN & 0.876 & 0.495 & 0.369 & 2.385 & 0.888 & 0.448 & 0.343 & 2.985 \\  & PISR & 0.806 & 0.628 & 0.419 & 2.129 & 0.818 & 0.571 & 0.384 & 2.342 \\  & RF & 0.836 & 0.560 & 0.381 & 2.388 & 0.838 & 0.558 & 0.379 & 2.397 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of different models for soil property prediction under different conditions.