Journal Article

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

Marchowski, Dominik

2021

Overview

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. The experiment carried out on 33 species of waterbirds shows the effectiveness of the use of the drone in population censusing, 96% of 343 cases, drone counting was successful. The best automatic counting tool was microbiology software ImageJ/Fiji and Machine learning using neural network algorithms – DenoiSeg.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: Algorithms / Animal breeding / Aquatic birds / Artificial intelligence / Artificial neural networks / bird monitoring / Birds / birds’ reactions to drones / Breeding / Climate change / Drones / Ecological research / Generalized linear models / Group size / Learning algorithms / Machine learning / microbiological tools in ecological research / Microbiology / Microorganisms / Neural networks / Research methodology / River networks / Software / unmanned aerial vehicle / waterbirds / Waterfowl / Wildlife