## 1 Introduction  
Seeds, being the foundational elements for crop growth and development, bear significant influence on agricultural production. The optimization of crop yield and quality depends on a variety of techniques and measures, among which, the provision of high-quality seeds holds notable significance, contributing to an improvement range of 30-40% [1, 2, 3]. The rapeseed plant (_Brassica rapa_ or _Brassica campstris_ L.), appreciated for its unique flavor and nutrient-dense profile, is a valued leafy vegetable and ranks among the world's top four oilseed crops, showcasing an oil content of 37% to 46%. Consequently, the rapeseed plant commands a distinguished status within the realm of agricultural production [4; 5; 6]. Traditional methods for testing germination rates rely heavily on germination tests conducted under meticulously controlled conditions--temperature, humidity, and light, to name a few--as stipulated by seed germination techniques. However, these tests come with their own set of challenges, including stringent experimental conditions, lengthy durations (spanning 7-15 days), potential seed damage, and non-reusability [7]. These shortcomings underscore the urgency for a rapid, non-destructive, and precise method for detecting germination rates, aimed at minimizing costs while enhancing detection speed and accuracy. Near-infrared spectroscopy (NIR) technology, capable of providing characteristic information about substances, enables efficient and economical sample analysis without causing sample destruction, thereby presenting itself as a viable tool for detecting germination rates in seed batches [8; 9]. In the realm of crop seed germination rate detection (i.e., vitality), [PERSON] et al. (2016) [10] employed infrared spectroscopy to evaluate the vitality of melon seeds. They subjected seeds to artificial aging, collected near-infrared spectral data, and established discriminant models for differing vitality levels using Partial Least Squares Discriminant Analysis (PLS-DA). The research yielded an impressive 94.6% accuracy rate in identifying the vitality of melon seeds through the combined use of SR variable selection and PLS-DA. In another study, [PERSON] et al. (2019). Ref. [11] used PLS-DA to analyze data derived from NIR-HSI of soybean seeds, achieving classification accuracies exceeding 95%. Meanwhile, [PERSON] et al. (2017). Ref. [12] developed a prediction model for wheat seed germination rate using Si-cPLS, with the average correlation coefficients of PLS, cPLS, and Si-cPLS models achieving 0.935, 0.949, and 0.967, respectively, thus underscoring the efficacy of multiple-model consensus in bolstering prediction performance. [PERSON] et al. (2022). Ref. [13] gathered near-infrared spectra of 100 Medicago sativa seed samples from different provinces and formulated a support vector machine prediction model. The application of first-order derivatives for spectral preprocessing with a penalty factor c = 2.896 and a kernel function g = 0.5 led to an impressive 96.67% accuracy rate in the detection set. Overall, the advancement in current research indicates the potential applicability of NIR for the rapid and non-destructive detection of seed germination rates. However, the majority of these studies primarily focus on vitality classification, germination capacity identification, and seed germination rate prediction in large-grain crops, leaving the research on rapid non-destructive detection of germination rates in multi-variety, small-grain seeds such as the rapeseed plant somewhat underexplored. The diversity of rapeseed varieties encompasses a variety of seed attributes such as color and shape, which can differ significantly. These variances in physical characteristics can influence the quality of the collected spectral sample, thereby escalating the complexity associated with accurately predicting the germination rate across differing seed types.