Automated Farmland Biodiversity Monitoring Using Camera Traps, Mobile Networks, and AI-Driven Species Identification
CHRISTINA seeks to demonstrate automated animal biodiversity data within agricultural landscapes, which serve as habitats for a diverse array of species. In light of recent legislative initiatives such as Biodiversity Net Gain and the Global Biodiversity Framework, it is imperative to capture and quantify the biodiversity supported by farmland habitats, so that this data can inform farm management practices and facilitate the implementation of effective conservation measures. Traditional methods for biodiversity assessment is prone to expertise, time and resource constraints, leading to insufficient data collection.
To address this issue, we propose the utilization of commercially available, network-enabled camera traps for passive monitoring of wildlife over extended periods. By employing AI-driven image recognition, we will automate the compilation of an inventory of species detected in the captured images. These images will be transmitted via mobile network to a cloud-based platform for species identification, with results displayed on an accessible dashboard for farmers, landowners, and ecologists to evaluate and use.
*The project is named “CHRISTINA” in honor of Cristina Mittermeier, whose contributions to conservation photography inspire our technological approach to documenting and safeguarding biodiversity on farmlands.
Further reading:
Bedson, C.P., Thomas, L., Wheeler, P.M., Reid, N., Harris, W.E., Lloyd, H., Mallon, D. and Preziosi, R., 2021. Estimating density of mountain hares using distance sampling: A comparison of daylight visual surveys, night‐time thermal imaging and camera traps. Wildlife Biology, 2021(3), pp.wlb-00802.
Fergus, P., Chalmers, C., Longmore, S., Wich, S., Warmenhove, C., Swart, J., Ngongwane, T., Burger, A., Ledgard, J. and Meijaard, E., 2023. Empowering wildlife guardians: an equitable digital stewardship and reward system for biodiversity conservation using deep learning and 3/4G camera traps. Remote Sensing, 15(11), p.2730.
Green, S.E., Rees, J.P., Stephens, P.A., Hill, R.A. and Giordano, A.J., 2020. Innovations in camera trapping technology and approaches: The integration of citizen science and artificial intelligence. Animals, 10(1), p.132.
Leave a Reply