Man versus machine: cost and carbon emission savings of 4G-connected Artificial Intelligence technology for classifying species in camera trap images

Abstract

Abstract Timely and accurate detection and identification of species are crucial for monitoring wildlife for conservation and management. Technological advances, including connectivity of camera traps to mobile phone networks and artificial intelligence (AI) algorithms for automated species identification, can potentially improve the timeliness and accuracy of species detection and identification. Adoption of this new technology, however, is often seen as cost-prohibitive as it has been difficult to calculate the cost savings or qualitative benefits over the life of the program. We developed a decision tool to quantify potential cost savings associated with incorporating the use of mobile phone network connectivity and AI technologies into monitoring programs. Using a feral cat eradication program as a case study, we used our decision tool to quantify technology-related savings in costs and carbon emissions, and compared the accuracy of AI species identification to that of experienced human observers. Over the life of the program, AI technology yielded cost savings of $0.27 M and when coupled with mobile phone network connectivity, AI saved $2.15 M and 115,838 kg in carbon emissions, with AI algorithms outperforming human observers in both speed and accuracy. Our case study demonstrates how advanced technologies can improve accuracy and cost-effectiveness and improve monitoring program efficiencies.