Date of Award
December 2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Earth System Science & Policy
First Advisor
Haochi Zheng
Abstract
Honey bees are essential for honey production and pollination services, but their colonies have been declining despite increasing demand. This decline may be due to various factors, including habitat loss, altered land use, climate shifts, and pesticide exposure. In order to assess the quantity and quality of habitats for honey bees and determine their availability and suitability for supporting bee colonies, it is crucial to identify the spatial location of registered apiary sites being used. However, such data is not available, which makes it a challenge for researchers in the field to further advance honey bee-related investigations. In my study, I developed a fast and effective way to detect apiaries with remote images using deep learning networks, such as You Only Look Once (YOLO) and Faster Region-based Convolutional Neural Network (Faster R-CNN). The approach fully utilized the publicly available, web-enabled imagery for detecting apiary sites across a large region. The result of my study can expand data capacity and provide valuable information to resource managers for better managing honey bees and other pollinator habitats.
Recommended Citation
Li, Ning, "Detection Of Apiary Sites From Remote Imagery" (2024). Theses and Dissertations. 6543.
https://commons.und.edu/theses/6543