Using machine learning to predict selenium content in crops: Implications for soil health and agricultural land...

Selenium (Se) is an indispensable trace element to human health, yet its biological tolerance threshold is relatively narrow. The potential application of machine learning methods to indirectly predict the Se content in crops across regional areas, thereby validating the reasonableness of soil health thresholds, remains to be explored. This study analyzed the factors influencing Se absorption in crops from longevity regions and employed machine learning models to predict the bioconcentration factor of Se, thereby obtaining selenium content in these crops and ultimately estimated the Se threshold for healthy soils. The results indicated that the Artificial Neural Network (ANN) model demonstrated the best predictive performance for the bioaccumulation factor (BAF) of Se in crops. The maximum permissible concentration of Se in rice was 0.17 mg/kg, while the minimum was 0.03 mg/kg; for maize, the maximum permissible concentration was 0.25 mg/kg, and the minimum was 0.04 mg/kg. Approximately 68 % of the arable land in the study area was suitable for cultivating Se-rich crops, providing important insights for the optimization of crop cultivation.