Objective  In view of the problem that the existed rapid and non-destructive maize seed germination rate testing methods are easily affected by the color of seed skin, the photoacoustic spectroscopy deep scanning technology was proposed to improve the detection accuracy of maize seed germination rate.

Method  Six maize cultivar seeds with three different colors were selected and treated using artificial aging method to obtain eight kinds of maize seeds with different aging time. The photoacoustic spectrum information with seven different depths was obtained by modulating the spectral frequency. The best scanning frequency and characteristic spectrum were determined by principal component analysis method. Different modeling approaches including partial least squares regression, back propagation neural network, generalized regression neural network and support vector regression were applied for comparing the prediction accuracy to optimize maize seed germination rate model.

Result  The best scanning frequency of photoacoustic spectrum was 500 Hz. The prediction model accuracy of support vector regression was the highest, and the correlation coefficients were all over 0.980 0. The prediction correlation coefficients of germination rates of six maize cultivar seeds were 0.983 8, 0.984 7, 0.983 6, 0.987 8, 0.983 3 and 0.994 7 respectively, while that of the mixed six cultivar maize seeds reached 0.942 1.

Conclusion  Through expanding the spectrum, sound and depth information, the photoacoustic spectrum depth scanning technology has a good generalization ability, and is suitable for high-precision germination rate detection of maize with different colors.