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CORIGAN: Assessing multiple species and interactions within images

Tresson Paul, Tixier Philippe, Puech William, Bagny-Beilhe Leïla, Roudine Sacha, Pagès Christine, Carval Dominique. 2019. CORIGAN: Assessing multiple species and interactions within images. Methods in Ecology and Evolution, 10 (11) : pp. 1888-1893.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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Url - jeu de données : https://doi.org/10.5061/dryad.t03b7b8

Quartile : Q1, Sujet : ECOLOGY

Abstract : Images are resourceful data for ecologists and can provide a more complete information than other methods to study biodiversity and the interactions between species. Automated image analysis however often relies on extensive datasets, not implementable by small research teams. We are here proposing an object detection method that allows the analysis of high‐resolution images containing many animals interacting in a small dataset. We developed an image analysis pipeline named 'CORIGAN' to extract the characteristics of animal communities. CORIGAN is based on the YOLOv3 model as the core of object detection. To illustrate potential applications, we use images collected during a sentinel prey experiment. Our pipeline can be used to detect, count and study the physical interactions between various animals. On our example dataset, the model reaches 86.6% precision and 88.9% recall at the species level or even at the caste level for ants. The training set required fewer than 10 hr of labelling. Based on the pipeline output, it was possible to build the trophic and non‐trophic interactions network describing the studied community. CORIGAN relies on generic properties of the detected animals and can be used for a wide range of studies and supports. Here, we study invertebrates on high‐resolution images, but the same processing can be transferred for the study of larger animals on satellite or aircraft images.

Mots-clés Agrovoc : Biologie du sol, Imagerie, camera trapping [EN], Formicidae, Araneae

Mots-clés libres : Animal detection, Convolutional neural network, Image processing, Interaction study, Trophic network, Sentinel prey

Classification Agris : L20 - Animal ecology
U30 - Research methods
P34 - Soil biology

Champ stratégique Cirad : CTS 7 (2019-) - Hors champs stratégiques

Auteurs et affiliations

  • Tresson Paul, CIRAD-PERSYST-UPR GECO (FRA)
  • Tixier Philippe, CIRAD-PERSYST-UPR GECO (FRA) ORCID: 0000-0001-5147-9777
  • Puech William, Université de Montpellier (FRA)
  • Bagny-Beilhe Leïla, CIRAD-BIOS-UPR Bioagresseurs (CMR)
  • Roudine Sacha, Université de Montpellier (FRA)
  • Pagès Christine, CIRAD-BIOS-UPR Bioagresseurs (FRA)
  • Carval Dominique, CIRAD-PERSYST-UPR GECO (FRA) - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/593406/)

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