Rapid detection of maize seed germination using near-infrared spectroscopy combined with Gaussian process regression

Abstract

The germination rate of maize seeds is a critical indicator for ensuring high-quality sowing and suitability for food processing. To address the limitations of traditional germination tests, a rapid and non-destructive evaluation method based on near-infrared (NIR) spectroscopy combined with Gaussian Process Regression (GPR) was developed. Various spectral data preprocessing techniques were applied, and a hybrid kernel function integrating Gaussian and Linear kernels was constructed. Particle Swarm Optimization (PSO) was used to optimize the kernel parameters. The PSO-GPR model achieved excellent performance, with determination coefficients (R 2 ) of 1.000 and 0.9899 for the training and validation sets, respectively. The root mean square errors (RMSE) were 0.0059 and 0.0033, and the residual predictive deviation (RPD) reached 9.3, outperforming PLSR and SVM models. This study provides a novel strategy for the non-destructive evaluation of crop seed quality and contributes to developing smart agricultural practices.

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