Using machine learning to predict selenium and cadmium contents in rice grains from black shale-distributed farmland area

Abstract

Cadmium (Cd) and selenium (Se) are widely enriched in soil at black shale outcropping areas, with Cd levels exceeding the standard (2.0 mg/kg in 5.5 < pH ≤ 6.5) commonly. The prevention of Cd hazards and the safe development of Se-rich land resources are key issues that need to be urgently addressed. To ensure safe utilization of Se-rich land in the CdSe coexisting areas, 158 rice samples, their corresponding rhizosphere soils, and 8069 topsoil samples were collected and tested in the paddy fields of Ankang City, Shaanxi Province, where black shales are widely exposed. The results showed that 43 % of the topsoil samples were Se-rich soil (Se > 0.4 mg/kg) wherein 79 % and 3 % of Cd concentrations exceeded the screening value and control value, respectively, according to the GB15618-2018 standard. Meanwhile, 63 % of the rice samples were Se rich (Se > 0.04 mg/kg) and the Cd content exceeded the prescribed limit (0.2 mg/kg) in Se-rich rice by 26 %. There was no significant positive correlation between the Se and Cd contents in the rice grains and the Se and Cd contents in the corresponding rhizosphere soil. The factors influencing Se and Cd uptake in rice were SiO2, CaO, P, S, pH, and TFe2O3. Accordingly, an artificial neural network (ANN) and multiple linear regression model (MLR) were used to predict Cd and Se bioaccumulation in rice grains. The stability and accuracy of the ANN model were better than those of the MLR model. Based on survey data and the prediction results of the ANN model, a safe planting zoning of Se-rich rice was proposed, which provided a reference for the scientific planning of land resources.