## 4 Further Evaluation and Discussion  
To further quantify and evaluate the model's fitting quality and overall performance, scatter plots of the observed versus predicted values were generated (Figure 7). Each plot contained numerous points and two curves: the red line denoted y = x, representing the ideal scenario in which the predicted values perfectly match the observations, whereas the blue curve corresponded to the regression fit of the model's predictions. Point density was color-coded, with deeper (reddish) hues indicating higher concentrations of samples. In every plot, the red and blue curves intersected; this intersection marks the point at which the predicted and observed values are equal. When the intersection lay within the region of highest point density, the model exhibited a superior fitting performance and robustness within the principal data distribution. The results show that for most soil-element predictions, the intersection of the curves for ReSE-AP Net fell within the densest region, confirming its strong predictive capability and generalization. Nonetheless, for the pH and sand indicators, the intersection only appeared in relatively dense regions rather  
Figure 5: Comparison chart of R\({}^{2}\). Figure 6: RMSE comparison chart. than the densest area, suggesting that the model's performance on these two attributes could be further improved. It is pertinent to contrast our ReSE-AP Net with recent related works that also leverage ASPP-like structures for hyperspectral data analysis, notably the contributions from [PERSON] et al. [34] and [PERSON] et al. [35]. [PERSON] et al. [34] ingeniously adapted the ResNet-50 architecture for weed detection by replacing its latter stages with an ASPP module. While this design proved effective for their specific task, our preliminary experiments indicated that deeper networks, such as ResNet-152, did not necessarily yield a superior performance in our soil property prediction context, suggesting that the optimal network depth is task-dependent. Furthermore, their model lacked an explicit attention mechanism, which we identified as a key component for refining spectral features. In contrast, ReSE-AP Net is architecturally optimized in two ways: first, it employs a residual network of a deliberately chosen, more moderate depth to prevent overfitting and capture salient features effectively, and second, it integrates the SE channel attention mechanism within the feature extraction backbone, enabling progressive feature refinement and noise suppression. In another relevant study, [PERSON] et al. [35] proposed RAANet for semantic segmentation, which innovatively incorporates a residual structure within the ASPP module itself and deploys a dense arrangement of attention modules both inside and outside the ASPP. While this approach is novel and effective, its primary focus is on a complex, attention-augmented ASPP, with comparatively less emphasis on the initial deep feature extraction process. This may risk underutilizing the rich information embedded in the original hyperspectral data. ReSE-AP Net adopts a fundamentally different strategy by prioritizing the front-end feature  
Figure 7: Scatter plot of the fitting of the true values and predicted values. extraction. Our model leverages a synergistic combination of residual connections and SE attention to ensure that features are comprehensively extracted and purified before they are channeled into the multi-scale analysis stage. This strategic divergence underscores the unique architectural philosophy of our approach. In summary, the novelty of ReSE-AP Net, when benchmarked against these state-of-the-art models, is threefold:  
1. Endogenous refinement through deeply embedded attention: We pioneered the concept of embedding channel attention within each fundamental building block of the feature extraction backbone.