## 5 Conclusions  
This study first elucidated the significance of soil-element prediction and its relevance to sustainable agriculture. The publicly available LUCAS 2009 soil dataset was then introduced, outliers were removed, and a suite of descriptive statistics--size, mean, std, median, mode, kurtosis, and IQR--were computed and interpreted to demonstrate the scientific soundness of the data partitioning strategy. After data cleansing, piecewise pooling averaging (PPA) was applied to reduce the dimensionality of the spectral inputs. Building on these preparations, a multi-scale attention residual network based on spatial pyramid pooling (ReSE-AP Net) was proposed and employed for visible-near-infrared (Vis-NIR) hyperspectral inversion of multiple soil elements on the LUCAS 2009 dataset. The model extracts initial features via a front-end convolutional layer, propagates salient information through residual blocks augmented with SE channel attention, and enhances predictive accuracy and robustness by leveraging multi-scale feature extraction and fusion within the ASPP module. Experimental results showed that ReSE-AP Net outperformed all mainstream traditional machine learning models and equaled or surpassed widely used deep learning architectures, with particularly strong performance in terms of RMSE; its success on a publicly available dataset further attests to its generalization capability. Despite the demonstrated robustness and high performance of the proposed ReSE-AP Net, we acknowledge several limitations that warrant discussion and outline clear avenues for future research. 1. The reliance on the LUCAS 2009 dataset, while ensuring high data quality and standardization, introduced a potential bias. The dataset is geographically confined to Europe and is predominantly composed of agricultural soils. Consequently, the model's generalizability to other geographical regions, diverse land-use types (e.g., forests, wetlands), or less standardized, private datasets remains an open question requiring empirical validation. Future work will therefore focus on acquiring and testing the model on such heterogeneous datasets to rigorously assess its real-world applicability. 2. A nuanced analysis of the performance metrics revealed a noteworthy finding. Although ReSE-AP Net surpassed all baseline models in terms of RMSE across all soil properties, its performance on the R\({}^{2}\) metric for pH, N, and K was merely on par with the Transformer architecture. We hypothesize two complementary reasons for this observation. One pertains to the inherent inductive biases of the models: the Transformer's self-attention mechanism may be more adept at capturing the global, long-range spectral dependencies upon which the prediction of these particular elements relies, whereas our CNN-based model excels at leveraging local features. The other reason, suggested by the superior RMSE of our model, is that ReSE-AP Net achieves exceptional accuracy on the majority of samples within the central data distribution but may be less effective than the Transformer at fitting the extreme values that heavily influence the R\({}^{2}\) score. This indicates a clear opportunity for refinement. To address this, our immediate future work will concentrate on enhancing the model architecture. A primary strategy will be to introduce an adaptive weighting mechanism within the ASPP module. This mechanism will be designed to dynamically assign weights during the fusion of multi-scale convolutional features, thereby amplifying salient feature information while suppressing irrelevant noise. In principle, such a modification should augment the feature fusion capability of the ASPP module, leading to an overall improvement in the model's predictive power, especially in capturing the full variance of the data. This promising direction is currently under active investigation.