Cheadle Center for Biodiversity and Ecological Restoration

Bees are important pollinators responsible for creating and maintaining the ecosystems that many animals and humans rely on. With many of its species on the decline, the Cheadle Center for Biodiversity and Ecological Restoration is leading the Big-Bee Project Network (http://big-bee.net) in an effort to digitize bee traits for further research in bee ecology. Since researchers lack data on anatomical traits that may make bees either vulnerable or resilient to human-induced environmental changes, it is important to find new methods for efficiently collecting these data.

One bee trait identified to prioritize was a bee’s hairiness because they’re important for pollination, sensing nearby danger, and staying warm in cooler temperatures and cooler in hotter climates. Through the use of deep learning and computer vision, a new pipeline was created to measure the surface density of hair from bee images. By optimizing two pre-trained U-Nets on lateral-view images of bees, the model became capable of pixel-based classification of where the bee is in a photo and where the hair is. These models were trained and evaluated on hand-crafted datasets of 550 images, which were remasked overtime for better performance.

While the main challenges came from differentiating texture of hair and texture of eyes/wings, another deep learning model succeeded at removing this noise before being fed into the hair detection pipeline. This multi-step pipeline improves previous findings by further preprocessing input images and is able to target the important signals of the image (hair). It also opens up the opportunity of using traditional edge detection methods for optimizing this problem.

This research is sponsored by the McNair/Edison STEM Summer Research Program and the National Science Foundation (#2102006).