Research on an Improved Non-Destructive Detection Method for the Soluble Solids Content in Bunch-Harvested Grapes Based on Deep Learning and Hyperspectral Imaging.

Featured Application: Non-destructive detection and quality grading of the internal quality of grapes after harvest. The soluble solids content (SSC) is one of the important evaluation indicators for the internal quality of fresh grapes. However, the current non-destructive detection method based on hyperspectral imaging (HSI) relies on manual operation and is relatively cumbersome, making it difficult to achieve automatic detection in batches. Therefore, in this study, we aimed to conduct research on an improved non-destructive detection method for the SSC of bunch-harvested grapes. This study took the Shine-Muscat grape as the research object. Using Mask R-CNN to establish a grape image segmentation model based on deep learning (DL) applied to near-infrared hyperspectral images (400~1000 nm), 35 characteristic wavelengths were selected using Monte Carlo Uninformative Variable Elimination (MCUVE) to establish a prediction model for SSC. Based on the two abovementioned models, the improved non-destructive detection method for the SSC of bunch-harvested grapes was validated. The comprehensive evaluation index F₁ of the image segmentation model was 95.34%. The R_m² and RMSEM of the SSC prediction model were 0.8705 and 0.5696 Brix%, respectively, while the R_p² and RMSEP were 0.8755 and 0.9177 Brix%, respectively. The non-destructive detection speed of the improved method was 16.6 times that of the existing method. These results prove that the improved non-destructive detection method for the SSC of bunch-harvested grapes based on DL and HSI is feasible and efficient. [ABSTRACT FROM AUTHOR]