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Chinese Journal of Applied Ecology ›› 2025, Vol. 36 ›› Issue (6): 1889-1897.doi: 10.13287/j.1001-9332.202506.018

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Predicting heavy metal concentration in crop grain using automated machine learning models

ZHANG Yexiang1, CHEN Fengxian2, ZHANG Yuhong1, CHEN Xijuan2*   

  1. 1School of Environmental and Safety Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China;
    2Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
  • Received:2024-10-24 Accepted:2025-04-30 Online:2025-06-18 Published:2025-12-18

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Abstract: With the acceleration of industrialization and the intensification of agricultural activities, heavy metals (HMs) pollution in crops has become an issue that can not be ignored in current agricultural production. Based on 791 data sets from 54 publications, we predicted HMs concentrations in crop grains by using automated machine learning (AutoML) models. Ten factors were used as input variables: organic fertilizer application, HMs concentration in organic fertilizer, soil HMs concentration, soil organic matter, pH, cation exchange capacity, clay content, silt content, sand content and plant types. The concentrations of chromium (Cr), cadmium (Cd), lead (Pb), arsenic (As) and mercury (Hg) in crop grains were set as output variables. We evaluated the simulation and prediction performance of six models: deep learning (DL), distributed random forest (DRF), extremely randomized trees (XRT), stacked ensemble (SE), gradient boosting machine (GBM) and generalized linear model (GLM), with which we analyzed the key factors driving heavy metal accumulation in crop grains. The results showed that the optimal prediction model differed for different HMs. The DL model provided the best prediction for Cr, Pb, As and Hg, while the GBM model achieved the highest prediction accuracy for Cd. Feature importance and SHAP analysis revealed that the application of organic fertilizer and plant type were the key factors influencing HMs accumulation in crop grains. Organic fertilizer application, soil HMs concentration, organic fertilizer HMs concentration, and sand content were positively correlated with HMs concentration in crop grains, while cation exchange capacity, pH, organic matter, and clay content were negatively correlated with heavy metal concentration in crop grains. In summary, the DL and GBM models performed better in predicting heavy metal concentrations in crop grains. The input risk of heavy metals during organic fertilizer application must be strictly controlled.

Key words: machine learning, heavy metal, grain, organic fertilizer, prediction