Predicting soil available cadmium by machine learning based on soil properties.

Cadmium (Cd) accumulation in edible plant tissues poses a serious threat to human health through the food chain. Assessing the availability of soil Cd is crucial for evaluating associated environmental risks. However, existing experimental methods and traditional models are time-consuming and inefficient. In this study, we developed machine learning models to predict soil available Cd based on soil properties, using a dataset comprising 585 data points covering 585 soils. Traditional machine learning models exhibited prediction values beyond the theoretical range, urging the need for alternative approaches. To address this, different models were tested, and the post-constraint eXtreme Gradient Boosting (XGBoost) model was found to possess the best predictive performance (R

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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.