Maturity critically impacts rational harvesting of Xanthoceras sorbifolia Bunge, influencing yield, oil quality, and postharvest handling. This study pioneered visible/near-infrared hyperspectral imaging (HSI) for non-destructive maturity assessment of Xanthoceras sorbifolia Bunge using a proposed maturity characterization factor (MCF) generated from nine key indicators. All 628 hyperspectral images across four maturity stages of Xanthoceras sorbifolia Bunge were collected, and 19 maturity-related physicochemical parameters were measured. Band variability was analyzed using principal component analysis (PCA), and maturity discrimination models were developed by partial least squares discriminant analysis (PLS-DA), genetic algorithm-support vector machine (GA-SVM), residual network (ResNet), and convolutional neural network-Transformer (CNN-Transformer), incorporating spectral preprocessing. For simplified models, feature wavelengths were extracted using principal component (PC) loading, successive projection algorithm (SPA), and stability competitive adaptive reweighted sampling (sCARS). Developed partial least squares regression (PLSR) showed that model based on second-order derivative spectra, SPA, and PLS-DA (FD2-SPA-PLS-DA) performed well (Accuracy = 98.73%, Kappa = 0.9830). Performance was then significantly improved by combining spectral, color, and texture features (Accuracy = 99.36%, Kappa = 0.9915). For MCF prediction, model based on first-order derivative spectra, SPA, and PLSR (FD1-SPA-PLSR) yielded the optimal results with R2 P = 0.7534, root mean square error of prediction (RMSEP) = 0.1853, and relative percent deviation (RPD) = 2.0138. Finally, an MCF distribution map generated using the preferred simplified model successfully visualized maturity heterogeneity in Xanthoceras sorbifolia Bunge. Our work demonstrates the first application of HSI for rapid, non-destructive Xanthoceras sorbifolia Bunge maturity assessment, establishing it as an effective tool for further field application. • The validity of HSI for assessing maturity in Xanthoceras sorbifolia Bunge was confirmed. • Compared to sCARS and PC loadings, SPA selected variables were the optimal. • Multi-feature fusion was used to effectively improve model discrimination rates. • Maturity characterization factor (MCF) obtained through factor analysis. • The spatial distribution of MCF content in Xanthoceras sorbifolia Bunge was visualized.
Zhou et al. (Fri,) studied this question.
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