Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation Using Deep Learning and 3/4G Camera Traps

Abstract

The biodiversity of our planet is under threat, with approximately one million species expected to become extinct within decades. The reason: negative human actions, which include hunting, overfishing, pollution, and the conversion of land for urbanisation and agricultural purposes. Despite significant investment from charities and governments for activities that benefit nature, global wildlife populations continue to decline. Local wildlife guardians have historically played a critical role in global conservation efforts and have shown their ability to achieve sustainability at various levels. In 2021, COP26 recognised their contributions and pledged USD 1.7 billion per year; however this is a fraction of the global biodiversity budget available (between USD 124 billion and USD 143 billion annually) given they protect 80% of the planets biodiversity. This paper proposes a radical new solution based on “Interspecies Money”, where animals own their own money. Creating a digital twin for each species allows animals to dispense funds to their guardians for the services they provide. For example, a rhinoceros may release a payment to its guardian each time it is detected in a camera trap as long as it remains alive and well. To test the efficacy of this approach, 27 camera traps were deployed over a 400 km2 area in Welgevonden Game Reserve in Limpopo Province in South Africa. The motion-triggered camera traps were operational for ten months and, using deep learning, we managed to capture images of 12 distinct animal species. For each species, a makeshift bank account was set up and credited with GBP 100. Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount—mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian. The trial demonstrated that it is possible to achieve high animal detection accuracy across the 12 species with a sensitivity of 96.38%, specificity of 99.62%, precision of 87.14%, F1 score of 90.33%, and an accuracy of 99.31%. The successful detections facilitated the transfer of GBP 185.20 between animals and their associated guardians.

Keywords

• conservation
• biodiversity
• deep learning
• camera traps
• guardians
• interspecies money