Fluorescence hyperspectral imaging for detection of selenium content in lettuce leaves under cadmium-free and cadmium environments.

This study aimed to investigate the feasibility of detecting selenium content in lettuce leaves under complex environments (cadmium-free and cadmium environments) using fluorescence hyperspectral imaging (FHSI). Accordingly, multimodal difference-aware competitive adaptive reweighted sampling (MDCARS) was proposed to select cadmium-related features in complex environments and was integrated with a ResNet-convolutional neural network (RCNN) for the quantitative prediction of selenium content. MDCARS selected features with superior interpretability and model verification outcomes compared with common methods, thereby highlighting its advantages for complex data sources. Additionally, the RCNN performed better than the other models, and it was combined with MDCARS to achieve the optimal prediction of selenium content in lettuce leaves under complex environments, with the R, RMSEP and RPD values of 0.8975, 0.0487 mg•kg and 3.1240 respectively. Therefore, FHSI combined with MDCARS and RCNN offers a viable approach for predicting the selenium content in lettuce leaves under complex environments.