  It is extremely important to correctly identify the cultivars of maize seeds
in the breeding process of maize. In this paper, the transfer learning as a
method of deep learning is adopted to establish a model by combining with the
hyperspectral imaging technology. The haploid seeds can be recognized from
large amount of diploid maize ones with great accuracy through the model.
First, the information of maize seeds on each wave band is collected using the
hyperspectral imaging technology, and then the recognition model is built on
VGG-19 network, which is pre-trained by large-scale computer vision database
(Image-Net). The correct identification rate of model utilizing seed spectral
images containing 256 wave bands (862.5-1704.2nm) reaches 96.32%, and the
correct identification rate of the model utilizing the seed spectral images
containing single-band reaches 95.75%. The experimental results show that, CNN
model which is pre-trained by visible light image database can be applied to
the near-infrared hyperspectral imaging-based identification of maize seeds,
and high accurate identification rate can be achieved. Meanwhile, when there is
small amount of data samples, it can still realize high recognition by using
transfer learning. The model not only meets the requirements of breeding
recognition, but also greatly reduce the cost occurred in sample collection.
