Drones, automatic counting tools and artificial neural networks in
wildlife population censusing
Abstract
1. The use of a drone to count the flock sizes of 33 species of
waterbirds during the breeding and non-breeding periods was
investigated. 2. In 96% of 343 cases, drone counting was successful.
18.8% of non-breeding birds and 3.6% of breeding birds exhibited
adverse reactions: in the former, the birds were flushed, whereas the
latter attempted to attack the drone. 3. The automatic counting birds
was best done with the microbiology software - ImageJ / Fiji: the
average bird counting rate was 100 birds in 82 seconds. 4. Machine
learning using neural network algorithms proved to be an effective and
fast way of counting birds – 100 birds in 23 seconds. However, as the
preparation of images and machine learning time are time-consuming, this
method is recommended only for large data sets and large bird
assemblages. 5. The responsible study of wildlife using a drone should
only be carried out by persons experienced in the biology and behaviour
of the animals concerned.