Bee Wing ID Project Description

bee_wing.txt

[In collaboration with Lauren Ponisio.]

We are in the midst of Earth's sixth mass extinction due to
anthropogenic impacts such as climate change and habitat
destruction. Biodiversity provides essential services to people, such as
crop pollination and water filtration, which will also be lost as
species go extinct. Conservation biologists and ecologists have the
immense task of describing the processes that maintain biodiversity and
how we can harness those processes to restore species and the services
they provide.

One of the most fundamental steps in understanding how to maintain and
restore biodiversity is identifying what species are in a landscape, an
often expensive and time consuming task, particularly in understudied
groups like insects. This task will become even more difficult in the
future as the field of taxonomy continues to dwindle.

If tools were developed to quickly and cheaply identify species, this
would propel new advances in ecology that would hopefully translate to
slowing the loss of biodiversity.

We propose to build software to identify insects by their wing
venation. We will first focus on bees, an important group for their role
as crop pollinators but also particularly diverse and difficult to
identify to species (for example, a key to the genus Lasioglossum has
over 300 couplets which must be navigated to get to a species
identification). Bee wings can be distinguished based on relationships
between vein junctions, cell perimeters and areas, and veins.

While these features can be extracted through heuristic, designed
features, we will focus here on automatic discovery of underlying
patterns through the application of deep convolutional neural networks.
Previous work [http://idbee.ece.wisc.edu/]—using manually constructed
features—achieved classification rates of 70–80%, and we hope to see
significant improvements over these results.