Real‐time alerts from AI‐enabled camera traps using the Iridium satellite network: a case‐study in Gabon, Central Africa

Journal article

Efforts to preserve, protect and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity.

We modified an off-the-shelf camera trap (Bushnell™) and customised existing open-source hardware to create a ‘smart’ camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an ‘alert’ containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. All reference materials required to build the system are provided in open-source repositories.

Results show the system can operate for a minimum of 3 months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 min, though some outliers showed delays of 5-days or more when the system was incorrectly positioned and unable to connect to the Iridium network.

We anticipate significant developments in this field and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases including real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas.

Authors: Whytock, RC; Suijten, T; van Deursen, T; Świeżewski, J; Mermiaghe, H

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Methods in Ecology and Evolution
• Volume: 14
• Issue: 3
• Pages: 867-874
• Publication date: 2023-01-03
• Acceptance date: 2022-11-13
• DOI: 10.1111/2041-210x.14036
• EISSN: 2041-210X
• Language: English
• Keywords: networking and information technology R&D (NITRD); bioengineering; machine learning and artificial intelligence