SERS combined with the SAE-CNN model for estimating apple rootstocks under heavy metal copper stress

Abstract

Copper-containing chemicals are commonly used in orchard management, and their use over a long time causes excessive copper content in orchard soil. Surface-enhanced Raman spectroscopy (SERS) technique combined with deep learning (DL) methods were used to characterize the response of apple rootstocks under heavy metal Cu stress. The hydroponic method prepared Apple rootstocks under five concentration gradients of heavy metal Cu stress as the experimental samples. The Raman spectral data were collected and subjected to spectral preprocessing. A stacked auto-encoder convolutional neural network (SAE-CNN) model was used to develop a rapid discriminative model for root, stem, and leaf stress levels of heavy metal Cu on apple rootstocks. The SAE-CNN models all outperformed the traditional models, with performance metrics above 99%. The results showed that the proposed SAE-CNN model could rapidly discriminate heavy metal Cu in apple rootstocks.