  Accurate and fast identification of seed cultivars is crucial to plant
breeding, with accelerating breeding of new products and increasing its
quality. In our study, the first attempt to design a high-accurate
identification model of maize haploid seeds from diploid ones based on optimum
waveband selection of the LSTM-CNN algorithm is realized via deep learning and
hyperspectral imaging technology, with accuracy reaching 97% in the determining
optimum waveband of 1367.6-1526.4nm. The verification of testing another
cultivar achieved an accuracy of 93% in the same waveband. The model collected
images of 256 wavebands of seeds in the spectral region of 862.9-1704.2nm. The
high-noise waveband intervals were found and deleted by the LSTM. The
optimum-data waveband intervals were determined by CNN's waveband-based
detection. The optimum sample set for network training only accounted for 1/5
of total sample data. The accuracy was significantly higher than the
full-waveband modeling or modeling of any other wavebands. Our study
demonstrates that the proposed model has outstanding effect on maize haploid
identification and it could be generalized to some extent.
