Descriptor: Parasitoid Wasps and Associated Hymenoptera Dataset (DAPWH)
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arXiv
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| Format: | Preprint |
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2026
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| author | Pinheiro, Joao Manoel Herrera Herrera, Gabriela Do Nascimento Fernandes, Luciana Bueno Dos Reis Santos, Alvaro Doria Dos Godoy, Ricardo V. Almeida, Eduardo A. B. Onody, Helena Carolina Vieira, Marcelo Andrade Da Costa Penteado-Dias, Angelica Maria Becker, Marcelo |
| author_facet | Pinheiro, Joao Manoel Herrera Herrera, Gabriela Do Nascimento Fernandes, Luciana Bueno Dos Reis Santos, Alvaro Doria Dos Godoy, Ricardo V. Almeida, Eduardo A. B. Onody, Helena Carolina Vieira, Marcelo Andrade Da Costa Penteado-Dias, Angelica Maria Becker, Marcelo |
| contents | Accurate taxonomic identification is the cornerstone of biodiversity monitoring and agricultural management, particularly for the hyper-diverse superfamily Ichneumonoidea. Comprising the families Ichneumonidae and Braconidae, these parasitoid wasps are ecologically critical for regulating insect populations, yet they remain one of the most taxonomically challenging groups due to their cryptic morphology and vast number of undescribed species. To address the scarcity of robust digital resources for these key groups, we present a curated image dataset designed to advance automated identification systems. The dataset contains 3,556 high-resolution images, primarily focused on Neotropical Ichneumonidae and Braconidae, while also including supplementary families such as Andrenidae, Apidae, Bethylidae, Chrysididae, Colletidae, Halictidae, Megachilidae, Pompilidae, and Vespidae to improve model robustness. Crucially, a subset of 1,739 images is annotated in COCO format, featuring multi-class bounding boxes for the full insect body, wing venation, and scale bars. This resource provides a foundation for developing computer vision models capable of identifying these families. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20028 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Descriptor: Parasitoid Wasps and Associated Hymenoptera Dataset (DAPWH) Pinheiro, Joao Manoel Herrera Herrera, Gabriela Do Nascimento Fernandes, Luciana Bueno Dos Reis Santos, Alvaro Doria Dos Godoy, Ricardo V. Almeida, Eduardo A. B. Onody, Helena Carolina Vieira, Marcelo Andrade Da Costa Penteado-Dias, Angelica Maria Becker, Marcelo Computer Vision and Pattern Recognition Artificial Intelligence Accurate taxonomic identification is the cornerstone of biodiversity monitoring and agricultural management, particularly for the hyper-diverse superfamily Ichneumonoidea. Comprising the families Ichneumonidae and Braconidae, these parasitoid wasps are ecologically critical for regulating insect populations, yet they remain one of the most taxonomically challenging groups due to their cryptic morphology and vast number of undescribed species. To address the scarcity of robust digital resources for these key groups, we present a curated image dataset designed to advance automated identification systems. The dataset contains 3,556 high-resolution images, primarily focused on Neotropical Ichneumonidae and Braconidae, while also including supplementary families such as Andrenidae, Apidae, Bethylidae, Chrysididae, Colletidae, Halictidae, Megachilidae, Pompilidae, and Vespidae to improve model robustness. Crucially, a subset of 1,739 images is annotated in COCO format, featuring multi-class bounding boxes for the full insect body, wing venation, and scale bars. This resource provides a foundation for developing computer vision models capable of identifying these families. |
| title | Descriptor: Parasitoid Wasps and Associated Hymenoptera Dataset (DAPWH) |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2602.20028 |