## 4 Results and Discussion
### Prediction of Optimal Planting Zones for Se-Rich Wheat, Rice, Rapeseed, and Tea  
The above prediction of Se-rich crop planting zones across multiple crops does not provide specific planting recommendations for individual Se-rich crops. Therefore, this section focuses on predicting optimal planting zones for four widely cultivated crops in the study area: wheat, rice, rapeseed, and tea. These crops are grown in drylands, paddy fields, tea gardens, and forested areas, with varying land-use practices such as crop rotation and land-use function changes. As such, the areas covered by drylands, paddy fields, tea gardens, and forested regions were selected as the prediction zones for Se-rich crops. In the previous prediction of Se-rich crop planting zones, the BP-NN model demonstrated the best performance, with Se-rich crop samples accounting for 86.67% of the predicted Se-rich planting zones. This percentage was higher than that of the FWofE and SVR models, highlighting the BP-NN's ability to effectively handle small-sample datasets and nonlinear relationships. Consequently, the BP-NN model was adopted to predict Se-rich planting zones for wheat, rice, rapeseed, and tea separately. Correlation analysis was conducted for Se content and corresponding root soil indicators for each crop, and based on the results (Figure 8), root soil Se, As, Pb, Mg, Hg, and geological strata were selected as input features for wheat; Hg, Mn, Si, pH, and geological strata for rice; Cd, Mo, Se, Ni, and geological strata for rapeseed; and P, S, Si, Ca, Cu, Fe, and geological strata for tea. The hyperparameters of the BP-NN model were also determined through an empirical approach based on the error curves of the training and testing sets, with the aim of avoiding overfitting, and the complete hyperparameters are provided in Table 14.  
Using the previously constructed BP-NN model, Se content was predicted for wheat, rice, rapeseed, and tea, and distribution maps for each crop's Se content were generated. The prediction results showed Se content ranges of 0.031-0.096 mg/kg for wheat, 0.025-0.071 mg/kg for rice, 0.016-0.286 mg/kg for rapeseed, and 0.033-0.127 mg/kg for tea. The prediction results were graded into Se-low planting zones, second-class Se-rich planting  
Figure 8: Heat map of correlation between Se of the four kinds of crops (wheat, rice, rapeseed, and tea) and root soil elements. zones and first-class Se-rich planting zones using the Jenks Natural Breaks (Figure 9) [13]. The threshold for identifying Se-rich crops was defined as the boundary between Se-low and second-class Se-rich planting zones, as determined by this classification. Accordingly, Se-rich thresholds for wheat, rice, rapeseed, and tea were set at 0.047 mg/kg, 0.040 mg/kg, 0.068 mg/kg, and 0.071 mg/kg, respectively. The areas of Se-rich planting zones were 241.48 km\({}^{2}\) (68.30%) for wheat, 213.35 km\({}^{2}\) (60.35%) for rice, 245.59 km\({}^{2}\) (69.46%) for rapeseed, and 179.47 km\({}^{2}\) (50.76%) for tea. The BP-NN model's evaluation metrics were calculated for each crop to assess the interpretability of the predicted Se-rich planting zones. For wheat, the MAE was 0.0096, the RMSE was 0.0129, and R\({}^{2}\) was 0.64; for rice, the MAE was 0.0035, the RMSE was 0.0037, and R\({}^{2}\) was 0.95; for rapeseed, the MAE was 0.0055, the RMSE was 0.0067, and R\({}^{2}\) was 0.99; and for tea, the MAE was 0.0110, the RMSE was 0.0147, and R\({}^{2}\) was 0.71. Figure 9: Graded map of the prediction results of wheat **(a)**, rice **(b)**, rapeseed **(c)**, and tea **(d)**. These metrics indicate that the prediction errors for rice and rapeseed were minimal, while the interpretability of the results (based on R\({}^{2}\)) was very high for these two crops. The prediction performance for tea was acceptable, whereas the prediction for wheat was less accurate.