Predicting the Zinc Content in Rice from Farmland Using Machine Learning Models: Insights from Universal Geochemical Parameters

Zinc (Zn) is an essential nutrient for the human body and is prone to deficiency. Supplementing Zn through zinc-enriched cereals is of great significance in addressing the widespread issue of zinc deficiency. However, there is no simple linear correlation between the soil zinc content and rice grain zinc content, which poses challenges for zoning zinc-enriched rice cultivation based on the soil Zn content. Therefore, accurately predicting the zinc content in rice grains is of great importance. To verify the robustness of the prediction model and expand its applicability, this study established a prediction model using 371 sets of previously collected and tested rice grain and root zone soil samples from the Pearl River Delta and Heyuan regions in Guangdong. The model was validated using the data from 65 sets of rice and root zone soil samples collected and analyzed in Zijin and Dongyuan counties, Heyuan, in 2023. The results show that zinc absorption by rice grains is controlled by multiple factors, primarily related to the soil S, P, CaO, Mn, TFe2O3, TOC, and SiO2/Al2O3 ratio. Both the artificial neural network model and random forest model demonstrated a good predictive performance across large regions. However, in the Heyuan region, the random forest model outperformed the artificial neural network model, with an R2 of 0.79 and an RMSE of 0.05 when the predicted data were compared against the measured BAFZn of the rice. This suggests that predicting the zinc content in rice grains based on the soil macro-elements (including oxides) and TOC is feasible, and, within certain regional boundaries, the prediction model is robust and widely applicable. This study provides valuable insights into the rational development of zinc-enriched rice in the Heyuan region and offers a useful reference for establishing prediction models of the beneficial element content in rice grains in areas with limited data.