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Seed vigor is an important indicator for evaluating the physiological quality of seeds and directly affects germination rate, seedling emergence uniformity, and field establishment capacity. However, conventional vigor evaluation methods are often destructive, labor-intensive, and time-consuming. In this study, a nondestructive method for soybean seed vigor classification was developed by combining hyperspectral imaging with machine learning. A total of 6000 soybean seeds from four cultivars were subjected to artificial aging treatment to obtain samples with different vigor levels, and seed vigor classes were determined based on germination percentage, germination energy, and germination index. After spectral calibration, the effective spectral ranges of 401.0–1000.9 nm and 1003.7–2450.79 nm were retained for analysis. Four preprocessing methods, namely multiplicative scatter correction (MSC), standard normal variate transformation (SNV), Savitzky–Golay smoothing (SG), and Savitzky–Golay second-derivative preprocessing (D2), were applied to reduce noise and improve spectral quality. This was followed by dimensionality reduction using principal component analysis (PCA) and minimum noise fraction (MNF), and classification using random forest (RF), extreme gradient boosting (XGBoost), and support vector classifier (SVC) models. The best overall features were extracted using the method of SNV combined with PCA. On the independent test set, the highest classification performance was obtained, with accuracy, recall, and F1-score values of 93.33%, 93.33%, and 93.37%, respectively, using the SVC model. These results indicate that hyperspectral imaging combined with machine learning can provide rapid and nondestructive technical support for soybean seed vigor evaluation.
Jing et al. (Wed,) studied this question.