# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 5 Conclusions  
In this paper, we proposed an LSTM-CNN-Attention model for predicting soil properties from hyperspectral data, comparing its performance with those of both traditional machine learning models and advanced deep learning frameworks. This model integrates the temporal learning capability of LSTM, the feature extraction power of CNN, and the feature enhancement ability of attention mechanisms, achieving superior accuracy and robustness in predicting key soil properties such as OC, N, CaCO\({}_{3}\), and pH(H\({}_{2}\)O). The proposed framework outperforms existing methods, including S-AlexNet by [PERSON] et al. [25], with consistently higher R\({}^{2}\) and RPD metrics, demonstrating its effectiveness in modeling the complex nonlinear relationships within spectral data. A key contribution of this study lies in the integration of the attention mechanism, which enables the model to selectively emphasize relevant spectral features while mitigating the impact of noise and redundant data. This design enhances predictive performance across multiple soil properties, with R\({}^{2}\) values consistently exceeding 0.9 and RPD values surpassing 3. Comparisons with previous studies further underscore the limitations of models that rely on dimensionality reduction techniques, such as PCA-LSTM, or that lack attention mechanisms, highlighting the superiority of the proposed approach. While the results validate the potential of the LSTM-CNN-Attention model, challenges remain in enhancing computational efficiency and scalability. Future work will focus on optimizing the model's architecture and exploring methods to accelerate training. Additionally, the application of this model will be extended to field-collected soil data, accounting for environmental factors such as weather, light intensity, and moisture, which can introduce variability in soil properties. Future efforts will focus on refining the model, optimizing its structure, and broadening its applicability to sustainable agricultural practices, with the ultimate goal of facilitating soil health monitoring and efficient resource management. To enhance the model's adaptability for practical field applications, particular attention will be given to addressing the noise and variability commonly encountered in field-collected data. Advanced preprocessing techniques, domain adaptation strategies, and robust feature selection methods will be investigated to ensure that the model retains its predictive accuracy in real-world scenarios. These enhancements will further strengthen the model's utility for soil health assessment and support informed decision making across diverse agricultural environments. Conceptualization, Y.L. ; methodology, Y.L. ; software, Y.L. ; validation, S.H. ; formal analysis, X.Z. ; investigation, L.S. ; resources, L.S. ; data curation, Y.X. ; writing--original draft preparation, Y.L. ; writing--review and editing, S.H. ; visualization, Y.X. ; supervision, L.S. ; project administration, X.Z. ; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Hunan Province (grant number 2023J)J30304) and the Scientific Research Program of the Hunan Province Department of Education (grant number 23A0197). **Institutional Review Board Statement:** Not applicable. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{4}{c}{**LSTM-CNN-Attention**} & \multicolumn{4}{c}{**[PERSON] et al. (2023) [25]**} \\ \cline{2-9}  & \(\mathbf{R}^{2}\) & **RMSE** & **MAE** & **RPD** & \(\mathbf{R}^{2}\) & **RMSE** & **MAE** & **RPD** \\ \hline OC & 0.949 & 16.687 & 8.599 & 3.940 & 0.94 & 17.04 & 9.02 & 4.02 \\ N & 0.916 & 0.993 & 0.580 & 3.737 & 0.89 & 1.21 & 0.70 & 3.02 \\ CaCO\({}_{3}\) & 0.943 & 33.370 & 12.909 & 5.377 & 0.93 & 34.19 & 13.52 & 3.89 \\ pH (H\({}_{2}\)O) & 0.926 & 0.364 & 0.265 & 3.352 & 0.87 & 0.48 & 0.37 & 2.16 \\ \hline \hline \end{tabular}
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