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Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.
Roy, D B; Alison, J; August, T A; Bélisle, M; Bjerge, K; Bowden, J J; Bunsen, M J; Cunha, F; Geissmann, Q; Goldmann, K; Gomez-Segura, A; Jain, A; Huijbers, C; Larrivée, M; Lawson, J L; Mann, H M; Mazerolle, M J; McFarland, K P; Pasi, L; Peters, S; Pinoy, N; Rolnick, D; Skinner, G L; Strickson, O T; Svenning, A; Teagle, S; Høye, T T.
Afiliación
  • Roy DB; UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK.
  • Alison J; Centre for Ecology and Conservation, University of Exeter, Penryn TR10 9EZ, UK.
  • August TA; Department of Ecoscience and Arctic Research Centre, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark.
  • Bélisle M; UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK.
  • Bjerge K; Centre d'étude de la forêt (CEF) et Département de biologie, Université de Sherbrooke, 2500 Boulevard de l'Université, Sherbrooke, Québec, Canada J1K 2R1.
  • Bowden JJ; Department of Electrical and Computer Engineering, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark.
  • Bunsen MJ; Natural Resources Canada, Canadian Forest Service - Atlantic Forestry Centre, 26 University Drive, PO Box 960, Corner Brook, Newfoundland, Canada A2H 6J3.
  • Cunha F; Mila - Québec AI Institute, Montréal, Québec, Canada H3A 0E9.
  • Geissmann Q; Mila - Québec AI Institute, Montréal, Québec, Canada H3A 0E9.
  • Goldmann K; Federal University of Amazonas, Manaus, 69080-900, Brazil.
  • Gomez-Segura A; Center For Quantitative Genetics and Genomics, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark.
  • Jain A; The Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK.
  • Huijbers C; UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK.
  • Larrivée M; Mila - Québec AI Institute, Montréal, Québec, Canada H3A 0E9.
  • Lawson JL; Naturalis Biodiversity Centre, Darwinweg 2, 2333 CR Leiden, The Netherlands.
  • Mann HM; Insectarium de Montreal, 4581 Sherbrooke Rue E, Montreal, Québec, Canada H1X 2B2.
  • Mazerolle MJ; UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK.
  • McFarland KP; Department of Ecoscience and Arctic Research Centre, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark.
  • Pasi L; Centre d'étude de la forêt, Département des sciences du bois et de la forêt, Faculté de foresterie, de géographie et de géomatique, Université Laval, Québec, Canada G1V 0A6.
  • Peters S; Vermont Centre for Ecostudies, 20 Palmer Court, White River Junction, VT 05001, USA.
  • Pinoy N; Mila - Québec AI Institute, Montréal, Québec, Canada H3A 0E9.
  • Rolnick D; Ecole Polytechnique, Federale de Lausanne, Station 21, 1015 Lausanne, Switzerland.
  • Skinner GL; Faunabit, Strijkviertel 26 achter, 3454 Pm De Meern, The Netherlands.
  • Strickson OT; Department of Ecoscience and Arctic Research Centre, Aarhus University, C.F Møllers Alle 3, Aarhus, Denmark.
  • Svenning A; Mila - Québec AI Institute, Montréal, Québec, Canada H3A 0E9.
  • Teagle S; School of Computer Science, McGill University, Montreal, Canada H3A 0E99.
  • Høye TT; UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK.
Philos Trans R Soc Lond B Biol Sci ; 379(1904): 20230108, 2024 Jun 24.
Article en En | MEDLINE | ID: mdl-38705190
ABSTRACT
Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Insectos Límite: Animals Idioma: En Revista: Philos Trans R Soc Lond B Biol Sci Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Insectos Límite: Animals Idioma: En Revista: Philos Trans R Soc Lond B Biol Sci Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido