Combined gramian angular difference field image coding and improved mobile vision transformer for determination of apple soluble solids content by Vis-NIR spectroscopy

The soluble solids content (SSC) is an important indicator for evaluating the internal quality of apples. This study proposed a novel method for detecting apple SSC. This method involved collecting the transmission spectra of apples, transforming one-dimensional spectra into two-dimensional images using the gramian angular difference field (GADF) technique, and analyzing the images using an improved mobile vision transformer (MobileViT) model. Furthermore, the Grad-CAM method was used to visualize the model's prediction effect on SSC and find the wavelength range that had a greater impact on the model results. The results indicated that the GADF-improved MobileViT model exhibited excellent performance on the test dataset, with a determination coefficient (R2) of 0.938, a root mean square error (RMSE) of 0.532, and a mean absolute error (MAE) of 0.423. As the number of wavelengths used for modeling decreased, the improvements made in this study to MobileViT continued to enhance the model's performance effectively. And when using fewer training samples, the GADF-improved MobileViT model also had better performance than the original model. In conclusion, using visible and near-infrared spectroscopy and GADF image encoding techniques for the prediction of apple SSC is feasible.