A narrative review on the use of camera traps and machine learning in wildlife research

Bijl, Hanna and Heltai, Miklós (2022) A narrative review on the use of camera traps and machine learning in wildlife research. COLUMELLA: JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL SCIENCES, 9 (2). pp. 47-69. ISSN 2064-7816 (print), 2064-9479 (online)

Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB) | Preview


Camera trapping has become an important tool in wildlife research in the past few decades. However, one of its main limiting factors is the processing of data, which is labour-intensive and time-consuming. Consequently, to aid this process, the use of machine learning has increased. A summary is provided on the use of both camera traps and machine learning and the main challenges that come with it by performing a general literature review. Remote cameras can be used in a variety of field applications, including investigating species distribution, disease transmission and vaccination, population estimation, nest predation, animal activity patterns, wildlife crossings, and diet analysis. Camera trapping has many benefits, including being less invasive, allowing for consistent monitoring and simultaneous observation (especially of secretive or aggressive animals even in dangerous or remote areas), providing photo/video evidence, reducing observer bias, and being cost effective. The main issues are that they are subject to their environment, dependent on human placements, can disrupt animal behaviour, need maintenance and repair, have limitations on photographic data, and are sensitive to theft and vandalism. When it comes to machine learning, the main aim is to identify species in camera (trap) images, although emerging technologies can provide individual recognition as well. The downsides include the large amount of annotated data, computer power, and programming and machine learning expertise needed. Nonetheless, camera trapping and machine learning can greatly assist ecologists and conservationists in wildlife research, even more so as technology further develops.

Item Type: Article
Uncontrolled Keywords: camera trapping, wildlife research, conservation, machine learning, artificial intelligence
Subjects: G Geography. Anthropology. Recreation / földrajz, antropológia, kikapcsolódás > GE Environmental Sciences / környezettudomány
Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
S Agriculture / mezőgazdaság > SK Hunting sports / vadgazdálkodás, vadászat
Depositing User: MTMT SWORD
Date Deposited: 13 Feb 2023 07:13
Last Modified: 13 Feb 2023 07:13

Actions (login required)

Edit Item Edit Item