A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves

In order to effectively realize the spectral detection of heavy metal content, a deep learning method which consisted of stacked auto-encoders (SAE) and partial least squares support vector machine regression (LSSVR) is proposed to obtain depth features and establish cadmium (Cd) detection model. The Vis-NIR hyperspectral images of 1120 lettuce leaf samples were obtained and the whole region of lettuce leaf sample spectral data was collected and preprocessed with different spectral pre-treatment methods. Successive projections algorithm (SPA), partial least squares regression (PLSR) and SAE were used to acquire the optimum wavelength, respectively. Besides, the characteristic wavelengths were used to build partial least squares support vector machine regression (LSSVR) models. Furthermore, the best prediction performance for detecting Cd content in lettuce leaves was obtained by Savitzky-Golay combined with first derivative (SG-1st) pre-processing method, with Rp2 of 0.9487, RMSEP of 0.01049 ​mg/kg and RPD of 3.330 using SAE-LSSVR method. The results of this study indicated that deep learning method coupled with hyperspectral imaging technique has great potential for detecting heavy metal content in lettuce leaves. •Vis-NIR hyperspectral imaging was used to detect Cd content in lettuce leaves.•SAE-LSSVR is proposed to establish depth feature regression model.•Deep learning has a great potential for the identification of Cd content.