Predicting cadmium enrichment in crops/vegetables and identifying the effects of soil factors based on transfer learning methods

Cadmium (Cd) is present in soils and can easily migrate into plants due to its various forms. This mobility allows it to be absorbed by plant roots and accumulate in edible parts, entering the food chain and posing health risks. In some regions, insufficient sampling and research, or the limited cultivation of specific vegetables and crops, make it challenging to gather adequate data for modeling. A total of 353 pairs of soil and crop/vegetable samples were collected across three regions using a unified measurement method. These samples were utilized to build predictive models to study the relationship between soil factors and cadmium (Cd) absorption in six different crops/vegetables, followed by a unified comparison. This study compares regression and probability models and determines the best feature combination, which can retain enough information to accurately predict and prevent over-fitting caused by too many features. The best feature combination is used to apply transfer learning to cadmium enrichment in crops/vegetables. The results show that the best accuracy of the random forest probability model in the rice dataset is 0.89. The best feature combination of prediction results was found by feature optimization. This feature combination has a very good effect on the prediction of cadmium in corn / vegetables by transfer learning. The accuracy of corn, rape and radish is 0.93,0.89 and 0.81, respectively. In the case of good prediction effect of transfer learning, available Cd is the most critical function, and available Cd is positively correlated with Cd in plants. It suggests that available heavy metal significantly influence predictions in crops/vegetables. In areas with less sampling and research, selecting relevant features and using transfer learning methods is more appropriate for constructing predictive models.