Analysis, camera, and image files for: ecoEye, embedded vision camera for biodiversity monitoring

We need comprehensive information to manage and protect biodiversity in
the face of global environmental challenges, and artificial intelligence
is needed to generate that information from vast amounts of biodiversity
data. Currently, vision-based monitoring methods are heterogenous; they
poorly cover spatial and temporal dimensions, overly depend on humans, and
are not reactive enough for adaptive management. To mitigate these issues,
we present a portable, modular, affordable, and low-power device with
embedded vision for biodiversity monitoring of a wide range of terrestrial
taxa. Our camera uses interchangeable lenses to resolve barely visible and
remote targets, as well as customisable algorithms for blob detection,
region-of-interest classification, and object detection to identify them.
We showcase our system in six use cases from the ethology, landscape
ecology, agronomy, pollination ecology, conservation biology, and
phenology disciplines. Using the same devices with different automated
setups, 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 waterfowl, and tracked flower phenology over months.
We measured classification accuracies (i.e., F1-scores) between 55% and
95% in our field surveys and used them to standardise observations over
highly-resolved time scales. Our 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
agricultural systems transformation.