Cerrado Butterfly Dataset (CBD): An Image Dataset for Butterfly Identification in the Brazilian Cerrado
Resumo
Este trabalho apresenta o Cerrado Butterfly Dataset (CBD), um dataset de imagens de borboletas do bioma Cerrado voltado à identificação automática de espécies. As imagens foram coletadas sistematicamente nas plataformas iNaturalist e GBIF e organizadas em subconjuntos de treino e teste. O conjunto de treino foi ampliado com técnicas de data augmentation para aumentar a diversidade das amostras e melhorar a robustez de modelos de aprendizado profundo. Além disso, foi utilizado um pipeline de segmentação baseado nos modelos GroundingDINO e Segment Anything Model (SAM) para a geração automática de máscaras de segmentação, facilitando aplicações em reconhecimento de espécies, detecção de objetos e segmentação de imagens. O dataset final contém 26 espécies de borboletas e 1.550 imagens por espécie.
Palavras-chave:
Butterfly identification, Image dataset, Brazilian Cerrado
Referências
Barkmann, F., Lindner, A., Würflinger, R., Höttinger, H., and Rüdisser, J. (2025). Machine learning training data: over 500,000 images of butterflies and moths (lepidoptera) with species labels. Scientific Data, 12:1369.
EMBRAPA (2019). Borboletas e mariposas - agência de informação embrapa. [link]. Accessed: 2019-03-13.
EMBRAPA (2021). Borboletas e mariposas. Acesso em: 2026-03-25.
He, W., Han, K., Nie, Y., Wang, C., and Wang, Y. (2023). Species196: A one-million semi-supervised dataset for fine-grained species recognition. arXiv preprint arXiv:2309.14183.
Io, C. (1994). Monographia Rhopalocerorum Sinensium. Henan Scientific and Technological Publishing House.
Kirillov, A., Mintun, E., Ravi, N., et al. (2023). Segment anything. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Klink, C. A. and Machado, R. B. (2005). Conservation of the brazilian cerrado. Conservation Biology, 19(3):707–713.
Liu, S., Zeng, Z., Ren, T., et al. (2024). Grounding dino: Marrying dino with grounded pre-training for open-set object detection. Proceedings of the European Conference on Computer Vision (ECCV).
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. B., and Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403(6772):853–858.
Python Pillow Contributors (2025). Pillow: The friendly pil fork. [link]. Accessed: 2026-07-03.
Ribeiro, J. F. and Walter, B. M. T. (2008). As principais fitofisionomias do bioma cerrado. In Sano, S. M., Almeida, S. P., and Ribeiro, J. F., editors, Cerrado: Ecologia e Flora, volume 2, pages 151–212. EMBRAPA Cerrados, Brasília.
van Swaay, C. and Warren, M. (2008). The european butterfly indicator for grassland species 1990–2007. Report VS2008.010, De Vlinderstichting/Dutch Butterfly Conservation.
Wang, J., Markert, K., and Everingham, M. (2009a). The automatic identification of butterfly species. In Proceedings of the Fourth Indian Conference on Computer Vision, Graphics and Image Processing.
Wang, J., Markert, K., and Everingham, M. (2009b). Learning models for object recognition from natural language descriptions. In British Machine Vision Conference (BMVC), volume 1, page 2.
Xie, J., Cao, J., Ma, L., Zhen, W., Chen, Z., Li, X., Li, H., and Xu, S. (2019). A dataset of butterfly ecological images for automatic species identification. China Scientific Data, 4(3):21–86101.
Xie, N., Wang, H., et al. (2018). Butterfly species identification using deep learning and knowledge graph. arXiv preprint arXiv:1803.06626.
EMBRAPA (2019). Borboletas e mariposas - agência de informação embrapa. [link]. Accessed: 2019-03-13.
EMBRAPA (2021). Borboletas e mariposas. Acesso em: 2026-03-25.
He, W., Han, K., Nie, Y., Wang, C., and Wang, Y. (2023). Species196: A one-million semi-supervised dataset for fine-grained species recognition. arXiv preprint arXiv:2309.14183.
Io, C. (1994). Monographia Rhopalocerorum Sinensium. Henan Scientific and Technological Publishing House.
Kirillov, A., Mintun, E., Ravi, N., et al. (2023). Segment anything. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Klink, C. A. and Machado, R. B. (2005). Conservation of the brazilian cerrado. Conservation Biology, 19(3):707–713.
Liu, S., Zeng, Z., Ren, T., et al. (2024). Grounding dino: Marrying dino with grounded pre-training for open-set object detection. Proceedings of the European Conference on Computer Vision (ECCV).
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. B., and Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403(6772):853–858.
Python Pillow Contributors (2025). Pillow: The friendly pil fork. [link]. Accessed: 2026-07-03.
Ribeiro, J. F. and Walter, B. M. T. (2008). As principais fitofisionomias do bioma cerrado. In Sano, S. M., Almeida, S. P., and Ribeiro, J. F., editors, Cerrado: Ecologia e Flora, volume 2, pages 151–212. EMBRAPA Cerrados, Brasília.
van Swaay, C. and Warren, M. (2008). The european butterfly indicator for grassland species 1990–2007. Report VS2008.010, De Vlinderstichting/Dutch Butterfly Conservation.
Wang, J., Markert, K., and Everingham, M. (2009a). The automatic identification of butterfly species. In Proceedings of the Fourth Indian Conference on Computer Vision, Graphics and Image Processing.
Wang, J., Markert, K., and Everingham, M. (2009b). Learning models for object recognition from natural language descriptions. In British Machine Vision Conference (BMVC), volume 1, page 2.
Xie, J., Cao, J., Ma, L., Zhen, W., Chen, Z., Li, X., Li, H., and Xu, S. (2019). A dataset of butterfly ecological images for automatic species identification. China Scientific Data, 4(3):21–86101.
Xie, N., Wang, H., et al. (2018). Butterfly species identification using deep learning and knowledge graph. arXiv preprint arXiv:1803.06626.
Publicado
08/09/2026
Como Citar
FERREIRA NUNES, Ana Amélia; PEIXOTO DE MEIRELES, Sincler; SANTOS DA SILVA, Samira.
Cerrado Butterfly Dataset (CBD): An Image Dataset for Butterfly Identification in the Brazilian Cerrado. In: DATASET SHOWCASE WORKSHOP (DSW), 8. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 138-149.
DOI: https://doi.org/10.5753/dsw.2026.249623.
