Diagnosis of Cadmium Stress in Cabbage Leaves Based on Adaxial Surface Hyperspectral Data and the UVE-CARS-LightGBM

Cadmium (Cd) contamination in vegetables poses a serious threat to food safety, making the development of an efficient, nondestructive detection method crucial. This study integrates hyperspectral technology, feature selection, and machine learning to propose a nondestructive method for assessing Cd contamination levels in cabbage leaves. Hyperspectral data were collected from the adaxial and abaxial surfaces of cabbage leaves under eight different Cd stress conditions. After preprocessing the data and selecting feature wavelengths, machine learning models were constructed. Results indicate that spectral data from the adaxial surface of cabbage leaves outperformed data from the abaxial surface in distinguishing Cd concentration levels. After applying feature selection methods, all performance metrics of the models significantly improved across both the calibration and test datasets. Among these, the Uninformative Variable Elimination (UVE)-Competitive Adaptive Reweighted Sampling (CARS) combination method demonstrated superior filtering performance compared to UVE and CARS alone, successfully reducing the number of original bands from 137 to 25 while preserving key spectral information related to Cd contamination. Among all model combinations, the Light Gradient Boosting Machine (LightGBM) model constructed using adaxial leaf spectral data and UVE-CARS-filtered features demonstrated the highest discrimination performance. Its test set achieved an accuracy of 0.992, a precision of 0.990, a recall of 0.992, and an F1 score of 0.992. This study confirms that the combined approach of leaf adaxial surface spectroscopy and UVE-CARS-LightGBM can effectively distinguish cabbage leaves under different Cd stress concentrations, providing a new pathway for efficient nondestructive detection of heavy metal contamination in vegetables.