Date of Award:

8-2019

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Advisor/Chair:

Vladimir Kulyukin

Co-Advisor/Chair:

Nicholas Flann

Third Advisor:

Xiaojun Qi

Abstract

Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified.

A normal hive inspection can be very disruptive for the bee colony, as the hive needs to be disassembled to visually assess hive health from the inside by collecting larvae and egg data. This work uses Machine Learning and Computer Vision methodologies to develop techniques to monitor hive health without disrupting the bee colony residing in the hive. Bee traffic refers to the number of bees moving in a given area in front of the hive over a given period of time. Bee traffic is related to forager traffic. Forager traffic is the number of bees moving out of the beehive. Forager traffic is a crucial factor in determining and monitoring food availability, food demand, colony age structure, the impact of pesticides, etc. on beehives. This work focuses on estimating bee traffic levels in a given hive and associate this information with data collected through manual beehive inspections.

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