Monitoring Cadmium Content in the Leaves of Field Pepper and Eggplant in a Karst Area Using Hyperspectral Remote Sensing Data

Abstract

The ability to quickly and non-destructively monitor the cadmium (Cd) content in agricultural crops is the basic premise of effective prevention and control of Cd contamination in agricultural products. Hyperspectral technology provides a solution for this issue. The potential capability for the spectral prediction of the Cd content in the leaves of pepper and eggplant in the field was explored, and a spectral prediction model of the Cd content in these leaves was established. In this study, based on the indoor spectrum, the sensitive wavebands for predicting the Cd content in leaves were determined preliminarily by correlation analysis. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were used to establish spectral prediction models, and the final sensitive wavebands were determined by the size of the model index. The results show that the SVMR model exhibited higher prediction accuracy than the PLSR model. The RPDp (relative percent different of prediction set) values of the best SVMR prediction models for the pepper leaves and the eggplant leaves were 1.82 and 1.49, respectively. The values of Rp2 (coefficient of determination of prediction set), which can quantitatively estimate the Cd content in leaves, were 0.897 (p < 0.01) and 0.726 (p < 0.01), respectively. This study demonstrated that the leaf spectra of pepper and eggplant in the field can be used to predict the Cd content in leaves, providing a reference for monitoring the Cd content in the fruits of pepper and eggplant in the future.