Non-Destructive Assessment of Rice Seed Vigor and Extraction of Characteristic Spectra Based on Near-Infrared Spectroscopy

Rice seed vigor is one of the critical factors determining rice yield and quality. Identifying substances related to seed vigor and rapidly assessing seed vigor by non-destructive methods are of great significance for increasing rice production. This study employed near-infrared diffuse reflectance spectroscopy (NIR-DRS) and transmission spectroscopy (NIR-TS) to evaluate the vigor of naturally aged rice seeds. The NIR-DRS failed to establish a reliable relationship between spectral data and seed vigor, proving ineffective in distinguishing seed vigor. After enhancing the spectral differences between viable and non-viable seeds, the NIR-TS successfully identified high-vigor and non-viable seeds, with a partial least squares discriminant analysis (PLS-DA) model achieving accuracy and germination rates of 84.52% and 88.57% on the test set, respectively. Furthermore, three algorithms, including interval partial least squares (iPLS), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS), were applied to extract characteristic spectral wavelengths associated with seed vigor. Among these, the CARS algorithm performed the best, identifying 38 characteristic wavelengths. Wavelength analysis indicated that rice seed vigor is primarily influenced by molecules such as starch, protein, moisture, and lipids. Using the characteristic wavelengths selected by the CARS algorithm, a PLS-DA prediction model for rice seed vigor was constructed, achieving high accuracy and germination rates of 90.47% and 95.38% on the test set, respectively. This study demonstrates that NIR-TS outperforms NIR-DRS in assessing rice seed vigor. Moreover, wavelength selection techniques can effectively identify characteristic spectral features related to seed vigor and significantly enhance the prediction accuracy of the model.