Selenium-rich foods play a crucial role in human health and hold significant economic value for agricultural products. However, many regions in China are experiencing selenium deficiency, which has led to an increased demand for Se-rich agricultural products. This study focused on Nanzhang County, a key area within the “Organic Valley” of Hubei Province, China. We employed fuzzy weights-of-evidence, backpropagation neural network, and support vector regression models to predict optimal planting zones for Selenium-rich crops. A comparative analysis indicated that the backpropagation neural network model provided the highest prediction accuracy (R² = 0.77), identifying Selenium-rich crop zones covering 68.87% of the aera, where Selenium-rich crops made up 86.67% of all samples. Notably, the backpropagation neural network yielded excellent performance for rice and rapeseed, with R² values of 0.95 and 0.99, respectively. The findings also indicate that the Selenium content in crops is closely linked to Selenium levels in the soil and is significantly influenced by synergistic and antagonistic interactions with other elements. This study provides scientific support for the cultivation of selenium-rich crops. It plays a positive role in promoting the development of the local selenium-rich industry and the sustainable utilization of soil selenium resources.

The northwestern Hubei region, primarily encompassing Shiyan City and Yunxi County in Hubei Province, constitutes a crucial component of the South Qinling Tectonic Belt. The Neoproterozoic Wudang Group in the study area exhibits Cu element enrichment, with ore deposit formation closely associated with stratigraphic and structural features. This study evaluates copper mineral resource distribution and metallogenic potential in northwestern Hubei by employing factor analysis, concentration-area fractal modeling, and the fuzzy weights-of-evidence method based on stream sediment data, aiming to construct a metallogenic potential model. Factor analysis was applied to process 2002 stream sediment samples of 32 elements to identify principal factors related to copper mineralization. Inverse distance interpolation was used to generate element distribution maps of principal factors, which were integrated with geological and structural data to establish a model using the fuzzy weights of evidence method. Prediction results indicate that most known copper deposits are located within posterior favourability ranges of 0.0027–0.272, constrained by stratigraphic and fault controls. The central northwestern Hubei region is identified as a priority target for future copper exploration. This research provides methodological references for conducting mineral resource potential assessments in north-western Hubei using innovative evaluation approaches.