Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land

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http://hdl.handle.net/10138/318054

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Santangeli , A , Chen , Y , Kluen , E , Chirumamilla , R , Tiainen , J & Loehr , J 2020 , ' Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land ' , Scientific Reports , vol. 10 , no. 1 , 10993 . https://doi.org/10.1038/s41598-020-67898-3

Title: Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land
Author: Santangeli, Andrea; Chen, Yuxuan; Kluen, Edward; Chirumamilla, Raviteja; Tiainen, Juha; Loehr, John
Contributor: University of Helsinki, Helsinki Institute of Sustainability Science (HELSUS)
University of Helsinki, Helsinki Institute of Life Science HiLIFE
University of Helsinki, Biological stations
Date: 2020-07-14
Language: eng
Number of pages: 8
Belongs to series: Scientific Reports
ISSN: 2045-2322
URI: http://hdl.handle.net/10138/318054
Abstract: In conservation, the use of unmanned aerial vehicles (drones) carrying various sensors and the use of deep learning are increasing, but they are typically used independently of each other. Untapping their large potential requires integrating these tools. We combine drone-borne thermal imaging with artificial intelligence to locate ground-nests of birds on agricultural land. We show, for the first time, that this semi-automated system can identify nests with a high performance. However, local weather, type of arable field and height of the drone can affect performance. The results’ implications are particularly relevant to conservation practitioners working across sectors, such as biodiversity conservation and food production in farmland. Under a rapidly changing world, studies like this can help uncover the potential of technology for conservation and embrace cross-sectoral transformations from the onset; for example, by integrating nest detection within the precision agriculture system that heavily relies on drone-borne sensors.
Subject: ECOLOGY
TECHNOLOGIES
1181 Ecology, evolutionary biology
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