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A WIP-based insect atlas: An interpretable, open-set approach to insect identification and trait inference

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Title: A WIP-based insect atlas: An interpretable, open-set approach to insect identification and trait inference
Authors: Sereno, Denis, Bartumeus, Frederic, Chane, Camille, Simon, Histace, Aymeric, Jacob, Pierre, Garriga, Joan
Contributors: Interactions hôtes-vecteurs-parasites-environnement dans les maladies tropicales négligées dues aux trypanosomatides (UMR INTERTRYP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Université de Montpellier (UM), Centre d'Estudis Avançats de Blanes (CEAB), Consejo Superior de Investigaciones Cientificas España = Spanish National Research Council Spain (CSIC), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), ICREA Infection Biology Laboratory (Department of Experimental and Health Sciences), Universitat Pompeu Fabra Barcelona (UPF), SOVE
Source: 9TH Society for Vector Ecology Congress
https://hal.science/hal-05366834
9TH Society for Vector Ecology Congress, SOVE, Oct 2025, Chania, Greece
Publisher Information: CCSD
Publication Year: 2025
Collection: Université Paris Seine: ComUE (HAL)
Subject Terms: Wing Interference Patterns, Atlas, Deep Learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Subject Geographic: Chania, Greece
Description: International audience ; Insect identification is essential to taxonomy, ecology, and public-health. Traditional methodsrely on expert knowledge and intact specimens, making them time-consuming and limiting. Insectwings carry rich structural and optical information that can reveal taxonomic identity. While wingvenation is a known diagnostic trait, we propose using optical features encoded in Wing Interfer-ential Patterns (WIPs), arising from thin-film interference, as a novel and robust marker. Usinga dataset of 5,502 high-resolution images from seven Dipteran families, we trained convolutionalneural networks on a species-subset represented by at least 10 specimens, to classify species basedon WIPs, achieving over 90% accuracy. These highlight the potential of WIPs as a scalable andeffective tool for insect identification.Building on this, we introduce an open-set framework supporting classification in the context ofincomplete taxonomic knowledge. We demonstrate that the latent representations learned by ourmodels, when visualized, form a structured space in which the taxonomic hierarchy (family, genus,species) emerges naturally, like a WIP-based insect atlas. This atlas is not only interpretablebut also expandable, supporting both the discovery of unseen species and their integration withminimal retraining.Beyond taxonomy, WIPs can convey additional information related to ecological and physio-logical traits. Preliminary analyses show WIP variations correlated with sex and blood-feedingstatus, suggesting that WIPs act as multidimensional biological sensors encoding more than iden-tity alone.These contributions establish an interpretable open-set-compatible framework for insect iden-tification and trait inference. The “WIP-based insect atlas” offers an extensible platform forbiodiversity research, ecological monitoring, and vector surveillance in dynamic, data-limited en-vironments.
Document Type: conference object
Language: English
Availability: https://hal.science/hal-05366834
Accession Number: edsbas.C43733CB
Database: BASE
Description
Abstract:International audience ; Insect identification is essential to taxonomy, ecology, and public-health. Traditional methodsrely on expert knowledge and intact specimens, making them time-consuming and limiting. Insectwings carry rich structural and optical information that can reveal taxonomic identity. While wingvenation is a known diagnostic trait, we propose using optical features encoded in Wing Interfer-ential Patterns (WIPs), arising from thin-film interference, as a novel and robust marker. Usinga dataset of 5,502 high-resolution images from seven Dipteran families, we trained convolutionalneural networks on a species-subset represented by at least 10 specimens, to classify species basedon WIPs, achieving over 90% accuracy. These highlight the potential of WIPs as a scalable andeffective tool for insect identification.Building on this, we introduce an open-set framework supporting classification in the context ofincomplete taxonomic knowledge. We demonstrate that the latent representations learned by ourmodels, when visualized, form a structured space in which the taxonomic hierarchy (family, genus,species) emerges naturally, like a WIP-based insect atlas. This atlas is not only interpretablebut also expandable, supporting both the discovery of unseen species and their integration withminimal retraining.Beyond taxonomy, WIPs can convey additional information related to ecological and physio-logical traits. Preliminary analyses show WIP variations correlated with sex and blood-feedingstatus, suggesting that WIPs act as multidimensional biological sensors encoding more than iden-tity alone.These contributions establish an interpretable open-set-compatible framework for insect iden-tification and trait inference. The “WIP-based insect atlas” offers an extensible platform forbiodiversity research, ecological monitoring, and vector surveillance in dynamic, data-limited en-vironments.