## 4 Discussion  
The near-infrared (i.e., NIR) spectra of seeds impart crucial insights into the constitution of internal substances. Nevertheless, hurdles such as low absorption intensity and severe peak overlapping in the spectra, along with variations instigated by sample morphology, environmental factors, and spectrometer conditions, can inject noise into the spectral acquisition process. Furthermore, the modeling dataset frequently incorporates a vast number of variables relative to the number of samples, engendering redundancy and subpar modeling performance. Consequently, preprocessing methods and feature variable selection techniques were deployed to scrutinize the key elements influencing NIR quantitative models of seed germination rates from a chemometrics standpoint. By mitigating noise and non-linear variables and curtailing the impact of interfering variables on the modeling process, the complexity of the models was diminished, culminating in enhanced prediction accuracy and stability [19; 20].  
The integration of the SiPLS and RF algorithm is based on a synergistic principle. The RF algorithm initiates a random selection for the variable set, which can lead to uncertainty in variable information. During the variable selection process, the RF algorithm may incorporate variables devoid of information or those that present interference information, leading to suboptimal detection performance.  
SiPLS, on the other hand, offers an initial filtering process for the characteristic wavelength interval, generating variables that carry sensitive information related to the germination rate. This process curtails the influence of other uninformative or weakly associated variables on the modeling performance.  
As such, we initially employed the SiPLS feature interval of the full spectrum for preliminary screening, followed by using these pre-screened variables as the initial variable set for the RF algorithm. The goal is to select feature variables of higher importance. Consequently, the initial variable set consists of information wavelengths with strong pertinence, which effectively enhances detection accuracy and operational efficiency. This results in a significant improvement in the model's detection performance.  
Figure 14: Variable selection results using different algorithms.  
Compared to SiPLS alone, the SIPLS-RF screening process reduces the number of feature variables to only 12.66%. This reduction results in a decrease of the Root Mean Square Error for the SVR modeling performance test set of 19.05%, while the determination coefficient increases by 8.03%. This highlights the beneficial impact of this integrative approach on the model's performance.  
In this investigation, an SVR model predicated on SiPLS-RF preprocessing was constructed to forecast the average germination rate of rapeseed plant seed batches. The model evinced commendable performance in predicting germination rates for seeds hailing from different varieties and artificially aged seed batches. However, it is noteworthy that discrepancies may exist between seeds aged artificially and those aged naturally, and ensuing research could incorporate samples from disparate years to bolster the practicality and stability of NIR analysis and prediction models.