Abstract

Traditional methods for determining starch content in corn kernels are labor-intensive, destructive, and inefficient. To overcome these challenges, this work developed a rapid, non-destructive approach based on near-infrared hyperspectral imaging, applied to 58 rainfed corn varieties. A spectral preprocessing scheme combining wavelet transform, multiplicative scatter correction, and standard normal variate transformation was employed to enhance spectral quality. A two-stage wavelength selection framework was established using competitive adaptive reweighted sampling and sparrow search algorithm optimization. From the selected optimal wavelengths, four predictive models, namely partial least squares regression, artificial neural network (ANN), convolutional neural networks, and gradient boosting decision tree, were established, implemented, and systematically compared. The results identify 14 key wavelengths (1020.65–1647.71 nm) strongly correlated with starch content, with clear assignments to specific chemical bonds and good physical interpretability. Among these models, the ANN exhibited the best performance. The R2, RMSE, and RPD of the test set were 0.826, 0.759%, and 2.40, respectively, indicating favorable prediction accuracy and generalization ability. These key wavelengths provide a foundation for developing portable detection instruments. This work supports corn quality grading, breeding of high-starch varieties, and rapid raw material screening, thereby enhancing the quality and efficiency of the corn industry.

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