EXPRESS: Quantitative Prediction of Honey Peach (Prunus persica L.) Maturity Using Hyperspectral Imaging and Regression Generative Adversarial Network.

Abstract

The honey peach (Prunus persica L.) is highly valued by consumers due to its distinctive flavor and nutritional richness. However, its quality is strongly influenced by the maturity stage, which directly affects the optimal harvest time and market value. To achieve rapid, non-destructive, and quantitative maturity assessment, hyperspectral imaging (HSI) was utilized to collect spectral data from samples across the visible-near-infrared (VIS-NIR) range. A comprehensive maturity index (CMI) was subsequently established by integrating key physicochemical parameters, including fruit weight, size, color, firmness, pH, moisture content, and soluble solids content (SSC). This index provided a holistic representation of maturity status. To address the limitation of a small sample size, a regression generative adversarial network (RGAN) was introduced. This model jointly generated spectral data and their corresponding CMI values. The generated samples exhibited high consistency with real samples in terms of spectral feature distribution, as visualized by t-distributed stochastic neighbor embedding (t-SNE), and CMI value distribution. Comparative evaluation of prediction models indicated that data augmentation with 800 generated samples enabled the convolutional neural network regression (CNNR) model to achieve optimal performance, with R_p of 0.952 and RMSEP of 0.187. This approach significantly improved predictive accuracy and generalization capability. In summary, the proposed method, which integrates HSI with RGAN for CMI prediction, allows for accurate quantification of honey peach maturity. And it offers an efficient and intelligent solution for maturity grading and quality control.