Peer review

The identification for haploid seeds is an important process in maize haploid breeding. Thanks to the diffuse transmission (DT) technology of near-infrared (NIR) spectroscopy, maize haploid seeds can be selected automatically using NIR spectrum features. However, the NIR spectra of maize seeds contain a large number of redundant features and noise that will degrade the identification performance. We resolved this problem by designing a low dimension and uniform space of seed spectrum features to improve the collected spectra. The zero-phase component analysis (ZCA) method was utilized to uniform the feature space and the partial least squares regression (PLSR) was employed to design the low dimension space. Then, by using the classifier of back propagation neural network (BPNN), a high qualitative identification method was developed for selecting maize haploid seeds. The study results demonstrate that the average accuracy of the proposed method is outstanding (96.16%) with a minor standard deviation (SD) compared with other methods. Therefore, our proposed method is potentially useful for automatically identifying maize haploid seeds.

Text in English