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Drones, automatic counting tools, and artificial neural networks in wildlife population censusing.

ABSTRACT: 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.

SUBMITTER: Marchowski D

PROVIDER: S-EPMC8601926 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Drones, automatic counting tools, and artificial neural networks in wildlife population censusing.

Marchowski Dominik D

Ecology and evolution 20211103 22

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 learnin ...[more]

PMID: 34824822

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