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

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: the former birds
were flushed, whereas the latter attempted to attack the drone. 3. The
automatic counting of birds was best done with ImageJ/Fiji microbiology
software – the average counting rate was 100 birds in 64 seconds. 4.
Machine learning using neural network algorithms proved to be an effective
and quick way of counting birds – 100 birds in 7 seconds. 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.
5. The responsible study of wildlife using a drone should only be carried
out by persons experienced in the biology and behaviour of the target
animals.