Literature DB >> 29111567

A computer vision for animal ecology.

Ben G Weinstein1.   

Abstract

A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis.
© 2017 The Author. Journal of Animal Ecology © 2017 British Ecological Society.

Keywords:  automation; camera traps; ecological monitoring; images; unmanned aerial vehicles

Mesh:

Year:  2017        PMID: 29111567     DOI: 10.1111/1365-2656.12780

Source DB:  PubMed          Journal:  J Anim Ecol        ISSN: 0021-8790            Impact factor:   5.091


  29 in total

1.  Deep learning and computer vision will transform entomology.

Authors:  Toke T Høye; Johanna Ärje; Kim Bjerge; Oskar L P Hansen; Alexandros Iosifidis; Florian Leese; Hjalte M R Mann; Kristian Meissner; Claus Melvad; Jenni Raitoharju
Journal:  Proc Natl Acad Sci U S A       Date:  2021-01-12       Impact factor: 11.205

Review 2.  Challenges and solutions for studying collective animal behaviour in the wild.

Authors:  Lacey F Hughey; Andrew M Hein; Ariana Strandburg-Peshkin; Frants H Jensen
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2018-05-19       Impact factor: 6.237

3.  PelagiCam: a novel underwater imaging system with computer vision for semi-automated monitoring of mobile marine fauna at offshore structures.

Authors:  Emma V Sheehan; Danielle Bridger; Sarah J Nancollas; Simon J Pittman
Journal:  Environ Monit Assess       Date:  2019-12-05       Impact factor: 2.513

4.  Assessing Anatomical Changes in Male Reproductive Organs in Response to Larval Crowding Using Micro-computed Tomography Imaging.

Authors:  Juliano Morimoto; Renan Barcellos; Todd A Schoborg; Liebert Parreiras Nogueira; Marcos Vinicius Colaço
Journal:  Neotrop Entomol       Date:  2022-07-05       Impact factor: 1.650

5.  Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks.

Authors:  Zhaojun Wang; Jiangning Wang; Congtian Lin; Yan Han; Zhaosheng Wang; Liqiang Ji
Journal:  Animals (Basel)       Date:  2021-04-27       Impact factor: 2.752

6.  Confronting Deep-Learning and Biodiversity Challenges for Automatic Video-Monitoring of Marine Ecosystems.

Authors:  Sébastien Villon; Corina Iovan; Morgan Mangeas; Laurent Vigliola
Journal:  Sensors (Basel)       Date:  2022-01-10       Impact factor: 3.576

7.  Isolation and no-entry marine reserves mitigate anthropogenic impacts on grey reef shark behavior.

Authors:  Jean-Baptiste Juhel; Laurent Vigliola; Laurent Wantiez; Tom B Letessier; Jessica J Meeuwig; David Mouillot
Journal:  Sci Rep       Date:  2019-02-27       Impact factor: 4.379

8.  Automatic detection of fish and tracking of movement for ecology.

Authors:  Sebastian Lopez-Marcano; Eric L Jinks; Christina A Buelow; Christopher J Brown; Dadong Wang; Branislav Kusy; Ellen M Ditria; Rod M Connolly
Journal:  Ecol Evol       Date:  2021-05-18       Impact factor: 2.912

9.  Citizen science and online data: Opportunities and challenges for snake ecology and action against snakebite.

Authors:  Andrew M Durso; Rafael Ruiz de Castañeda; Camille Montalcini; M Rosa Mondardini; Jose L Fernandez-Marques; François Grey; Martin M Müller; Peter Uetz; Benjamin M Marshall; Russell J Gray; Christopher E Smith; Donald Becker; Michael Pingleton; Jose Louies; Arthur D Abegg; Jeannot Akuboy; Gabriel Alcoba; Jennifer C Daltry; Omar M Entiauspe-Neto; Paul Freed; Marco Antonio de Freitas; Xavier Glaudas; Song Huang; Tianqi Huang; Yatin Kalki; Yosuke Kojima; Anne Laudisoit; Kul Prasad Limbu; José G Martínez-Fonseca; Konrad Mebert; Mark-Oliver Rödel; Sara Ruane; Manuel Ruedi; Andreas Schmitz; Sarah A Tatum; Frank Tillack; Avinash Visvanathan; Wolfgang Wüster; Isabelle Bolon
Journal:  Toxicon X       Date:  2021-06-22

10.  Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees.

Authors:  Nik Fadzly; Wan Fatma Zuharah; Jenny Wong Jenn Ney
Journal:  Plant Signal Behav       Date:  2021-06-20
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