DL-MAML：An Innovative Model for Automatically Identifying Butterfly Species

Zhao Gewei; Xu Shengquan; Xie Juanying

doi:10.7544/issn1000-1239.202220860

Journal of Computer Research and Development > 2024 > 61(3): 674-684. > DOI: 10.7544/issn1000-1239.202220860 CSTR: 32373.14.issn1000-1239.202220860

Zhao Gewei, Xu Shengquan, Xie Juanying. DL-MAML：An Innovative Model for Automatically Identifying Butterfly Species[J]. Journal of Computer Research and Development, 2024, 61(3): 674-684. DOI: 10.7544/issn1000-1239.202220860

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DL-MAML：An Innovative Model for Automatically Identifying Butterfly Species

1.
School of Computer Science, Shaanxi Normal University, Xi’an 710119
2.
College of Life Sciences, Shaanxi Normal University, Xi’an 710119

Funds: This work was supported by the National Natural Science Foundation of China (62076159, 12031010, 61673251), the Fundamental Research Funds for the Central Universities (GK202105003), and the National Animal Collection Resource Center.

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Author Bio:
Zhao Gewei: born in 1996. Master. His main research interests include machine learning, deep learning, and object detection

Xu Shengquan: born in 1967. PhD, professor, PhD supervisor. His main research interests include entomological taxonomy, insect systematics, and bioinformatics

Xie Juanying: born in 1971. PhD, professor, PhD supervisor. Senior member of CCF. Her main research interests include machine learning, data mining, and biomedical big data analyses
Received Date: October 08, 2022
Revised Date: April 27, 2023
Available Online: November 29, 2023

Graphical Abstract

Abstract

Abstract

There are tens of thousands of butterfly species. Each butterfly species is closely related to a specific type of plants. It is significant to study butterfly species automatic identification. However, it is very challenging to study butterfly species recognition via the images taken in the field environments. One reason is that there are small number of butterfly species in the existing datasets compared with the reported species in the world. The other reason is that the number of samples (images) of each butterfly species is limited in the datasets. These situations make it challengeable to train a general system for butterfly species identification via machine learning algorithms. In addition, butterfly wings are always folded in the images taken in the field environments, which make it challengeable to learn butterfly classification features, which further make it difficult to study butterfly species recognition using machine learning techniques. Therefore, meta-learning is introduced to address the challenges, and DL-MAML (deep learning advanced model-agnostic meta-learning) algorithm is proposed for identifying the butterfly species using available images taken in the field environment. DL-MAML introduces a L2 regularization term to the objective function and the parameter updating of the meta-learning MAML while introducing ResNet34 deep learning model to extract butterfly features and plus two more convolution layers to the MAML model to avoid over-fitting. The extensive experiments including ablation experiments and comparison with available meta-learning models demonstrate that the initial model parameters obtained by DL-MAML algorithm are effective to identify new species butterflies. DL-MAML is effective in identifying butterfly species using images taken in the field environments, which make it possible to construct the general and complete butterfly species identification system using available butterfly images from limited categories.
- butterfly classification,
- deep learning,
- feature extraction,
- meta learning,
- MAML algorithm

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References (29)

References

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