Date of Award:

8-2019

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Vladimir Kulyukin

Committee

Vladimir Kulyukin

Committee

Xiaojun Qi

Committee

Curtis Dyreson

Abstract

Over a decade ago, beekeepers noticed that the bees were dying or disappearing without any prior health disorder. Colony Collapse Disorder(CCD) has been a major threat to bee colonies around the world which affects vital human crop pollination. Possible instigators of CCD include viral and fungal diseases, decreased genetic diversity, pesticides and a variety of other factors. The interaction among any of these potential facets may be resulting in immunity loss for honey bees and the increased likelihood of collapse. It is essential to rescue honey bees and improve the health of bee colony.

Monitoring the traffic of bees helps to track the status of hive remotely. An Electronic beehive monitoring system extracts video, audio and temperature data without causing any interruption to the bee hives. This data could provide vital information on colony behavior and health. This research uses Artificial Intelligence and Computer Vision methodologies to develop and analyze technologies to monitor omnidirectional bee traffic of hives without disrupting the colony. Bee traffic means the number of bees moving in a given area in front of the hive over a given period of time. Forager traffic is the number of bees coming in and/or leaving the hive over a time. Forager traffic is a significant component in monitoring food availability and demand, colony age structure, impacts of pests and diseases, etc on hives. The goal of this research is to estimate and keep track of bee traffic by eliminating unnecessary information from video samples.

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