Research on an Improved Non-Destructive Detection Method for the Soluble Solids Content in Bunch-Harvested Grapes Based on Deep Learning and Hyperspectral Imaging
Source abstract
Abstract
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 Rm2 and RMSEM of the SSC prediction model were 0.8705 and 0.5696 Brix%, respectively, while the Rp2 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spectroscopy and Chemometric Analyses · Advanced Chemical Sensor Technologies · Spectroscopy Techniques in Biomedical and Chemical Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510640, China
College of Engineering, South China Agricultural University, Guangzhou 510642, China
Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Funding
Laboratory of Lingnan Modern Agriculture Project · The Project of Collaborative Innovation Center of Guangdong Academy of Agricultural Sciences · 2020 Provincial Agricultural Science and Technology Innovation and Extension System Construction Project · President’s Foundation of Guangdong Academy of Agricultural Sciences
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