Fusion of spectral and image information for generalized detection of SSC in multi-variety peaches

Soluble solids content (SSC) is an important indicator for determining the commercial value of peaches. Visible/near-infrared (Vis/NIR) spectroscopy combined with chemometric methods is a primary technique for predicting peach SSC. However, the interference of fruit color with spectral signals makes it challenging to accurately detect SSC across different varieties. This study explored the feasibility of fusing spectral and image data to achieve accurate SSC prediction for multiple peach varieties. Diffuse reflectance spectra and images of three peach varieties (‘Hujing’, ‘Jinqiuhong’, and ‘Dongxue’) were collected. Multiple feature-level fusion strategies for spectral and image data were proposed. Partial least squares regression (PLSR) and support vector regression (SVR) models were developed based on the multimodal fusion data to predict the SSC of individual and multiple varieties, respectively. Their predictive performance was compared with that of models established using spectral data alone. To further improve the generalization ability of the multi-variety models, a spectrum-image fusion network (SIFNet) was proposed by extracting and leveraging high-level image features and integrating them with spectral information. The results showed that the SIFNet achieved superior performance in predicting the SSC of multi-variety peaches, with R P 2 , RMSEP, and RPD P of 0.8235, 1.0514, and 2.5208, respectively. • Mitigating color interference in peach SSC prediction via spectral-image fusion. • Multimodal fusion enhanced SSC prediction robustness for multi-variety peaches. • Deep learning models achieved optimal performance for multi-variety peaches. • Grad-CAM visualized fusion mechanism to improve model generalization capability.