Eyes on nature: Embedded vision cameras for multidisciplinary biodiversity monitoring

Abstract Global environmental challenges require comprehensive data to manage and protect biodiversity. Currently, vision-based biodiversity monitoring efforts are mixed, incomplete, human-dependent, and passive. To tackle these issues, we present a portable, modular, low-power device with embedded vision for biodiversity monitoring. Our camera uses interchangeable lenses to resolve barely visible and remote subjects, as well as customisable algorithms for blob detection, region-of-interest classification, and object detection to identify targets. We showcase our system in six case studies from the ethology, landscape ecology, agronomy, pollination ecology, conservation biology, and phenology disciplines. Using the same devices, we discovered bats feeding on durian tree flowers, monitored flying bats and their insect prey, identified nocturnal insect pests in paddy fields, detected bees visiting rapeseed crop flowers, triggered real-time alerts for waterbirds, and tracked flower phenology over months. We measured classification accuracies between 55% and 96% in our field surveys and used them to standardise observations over highly-resolved time scales. The cameras are amenable to situations where automated vision-based monitoring is required off the grid, in natural and agricultural ecosystems, and in particular for quantifying species interactions. Embedded vision devices such as this will help addressing global biodiversity challenges and facilitate a technology-aided global food systems transformation.