| dc.description.abstract | This paper proposes a seed classifier that combines deep learning and biological hierarchical classification architecture for seed identification of large number of categories. Residual neural network, combined with the family, genus, and species of the biological taxonomy, as a three-level classification process, first enter the seed image into the neural network model of the family, and after classifying the family of the seed, then The image is input into the category layer network of this class, and finally enters the third layer of the classifier to classify the category and output the final decision. We use deep learning and hierarchical architecture to classify 784 types of seeds and 560 types of seed samples containing derived species, and the recognition rates can reach 73.13% and 92.33%, respectively. Compared with the recognition rate of 12.7% and 31.33% of the network architecture (Resnet50+Siamese), the experimental results show that our method has obvious advantages, and the hierarchical architecture separates the classification process, which can determine the cause of the classification error through the hierarchy, Compensate for the uninterpretability of deep learning. | en_US |