Drones, automatic counting tools, and artificial neural networks in wildlife population censusing.
bird monitoring
birds’ reactions to drones
machine learning
microbiological tools in ecological research
unmanned aerial vehicle
waterbirds
Journal
Ecology and evolution
ISSN: 2045-7758
Titre abrégé: Ecol Evol
Pays: England
ID NLM: 101566408
Informations de publication
Date de publication:
Nov 2021
Nov 2021
Historique:
received:
03
05
2021
revised:
04
10
2021
accepted:
19
10
2021
entrez:
26
11
2021
pubmed:
27
11
2021
medline:
27
11
2021
Statut:
epublish
Résumé
The use of a drone to count the flock sizes of 33 species of waterbirds during the breeding and non-breeding periods was investigated.In 96% of 343 cases, drone counting was successful. 18.8% of non-breeding birds and 3.6% of breeding birds exhibited adverse reactions: the former birds were flushed, whereas the latter attempted to attack the drone.The automatic counting of birds was best done with ImageJ/Fiji microbiology software - the average counting rate was 100 birds in 64 s.Machine learning using neural network algorithms proved to be an effective and quick way of counting birds - 100 birds in 7 s. However, the preparation of images and machine learning time is time-consuming, so this method is recommended only for large data sets and large bird assemblages.The responsible study of wildlife using a drone should only be carried out by persons experienced in the biology and behavior of the target animals.
Identifiants
pubmed: 34824822
doi: 10.1002/ece3.8302
pii: ECE38302
pmc: PMC8601926
doi:
Banques de données
Dryad
['10.5061/dryad.j6q573nfp']
Types de publication
Journal Article
Langues
eng
Pagination
16214-16227Informations de copyright
© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Déclaration de conflit d'intérêts
None declared.
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