## 3 Results
### Confusion Matrix and Spatial Analysis  
To conduct a comprehensive error analysis as suggested, we further dissected the model's performance by examining the confusion matrix, per-grade error distribution, and the spatial patterns of prediction errors.  
A detailed breakdown of classification performance is presented in the confusion matrix (Figure 6, center-bottom). The matrix reveals that the model performs well, with high values along the diagonal, indicating a large number of correct classifications for all Cultivated Land Quality (CLQ) grades. The primary source of error stems from confusion between adjacent grades. For instance, for CLQ-3, seven parcels were misclassified as CLQ-2 and sixty-six were misclassified as CLQ-4. Similarly, for CLQ-4, 12 parcels were misclassified as CLQ-3 and 34 as CLQ-5. This pattern is logical, as adjacent grades possess more similar soil and environmental characteristics, making them harder to differentiate. Importantly, severe misclassifications across non-adjacent grades were rare, underscoring the model's reliability.  
The per-grade error analysis (Figure 6, bottom-right) further clarifies this trend. The boxplot shows the distribution of prediction errors (Predicted Grade--Actual Grade) for each CLQ level. It is observed that the median error for all grades is close to zero, but the variance of the error (interquartile range) tends to increase with the CLQ grade. This suggests that while the model is generally unbiased for each class, its predictions for higher-quality land (e.g., CLQ-5 and CLQ-6) are subject to greater uncertainty and larger potential errors compared to lower-quality land.  
The geographic distribution of prediction errors reveals distinct spatial patterns (Figure 6, top panel). The Error Magnitude Classification map (top-right) highlights that parcels with medium (\(0.5<|\,\mathrm{error}\,|\,\leq 1.0\), \(n=383\)) and very high (\(|\,\mathrm{error}\,|>1.5\), \(n=77\)) absolute errors are not randomly distributed. Instead, they exhibit significant spatial clustering, particularly in the northern and central-eastern parts of the study area. This spatial aggregation of errors suggests that the model's performance is influenced by local factors not fully captured by the input variables, which is likely attributable to intra-field heterogeneity. These regions might be characterized by more complex cropping patterns, varied irrigation, or sudden changes in soil type within single parcels, which can confound the spectral signals used for prediction.  
To statistically validate this visual observation, we performed a spatial autocorrelation analysis on the prediction errors. The calculation yielded a Global Moran's I statistic of 0.0270 (\(p<0.01\)). This positive and statistically significant value confirms a moderate spatial autocorrelation in the prediction errors. This result provides quantitative evidence that the model's errors are spatially dependent--that is, overestimated and underestimated parcels  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Models** & **Epochs** & **Accuracy** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline ResNet-LSTM & 300 & 68.60 & 51.07 & 71.47 & 46.18 & 79.07 \\ Transformer Regressor & 300 & 70.18 & 39.90 & 63.17 & 41.08 & 83.65 \\ CNN-RNN hybrid models & 300 & 69.01 & 70.77 & 84.13 & 41.20 & 72.56 \\ BO-Stacking-TabNet & 300 & 72.41 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative experimental results for deep learning approaches.  
tend to cluster together--reinforcing the hypothesis that localized spatial processes and field heterogeneity affect the model's accuracy.