Big Bee: Hair Recognition and Quantification
Abstract
The Cheadle Center for Biodiversity and Ecological Restoration (CCBER) is working with the UCSB Data Science capstone team to continue the research and understanding of bee ecology through image and trait digitalization.
In efforts to help researchers involved in the Big-Bee project, we want to automate the process it takes to handle and process the digital images of bees. Given images of bees with a QR/Datamatrix code, we sought to develop scripts that can rename the image files based on the decoded box and extract EXIF metadata. In the latter half of our project, we shift our focus toward the hairy characteristics of bees. Given data of high definition lateral bee images, we want to create models that can quantify how hairy a bee is.
Results
Two scripts, the QR Code Scanner & MetaData Extractors, are published on GitHub and are currently used by two academic institutions. More trial testing is required to discover bugs and further improvements for the scripts. Using density as our metric, our U-net and transfer learning method yields an accuracy of 97.68% for bee masking and 87.2% for hair masking. Using entropy as our metric, we were able to compare the average entropy values for different bees binned in the following manner: low, middle, and high hairiness. The box-plot distribution of the bins and t-tests show that there was a positive correlation between bee hairiness and average entropy value. Furthermore, we discover that entropy analysis works best for specimens that have low reflectivity and minimal skin texture.
QR Scanner: https://github.com/booleank/Bee-ScannerMetadata
Extractor: https://github.com/harperklauke/Metadata-Extractor