Bee Traffic Estimation with YOLO and Optical Flow

Honey bees are vital to our food chain as they efficiently pollinate many crops and fruits. With the collapse of an alarming number of honey beehives in recent years, there has been a substantial need for regular monitoring of their health status and population growth. Traditionally, beekeepers have...

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Bibliographic Details
Main Authors: Dickson, R. Thompson, Parry, R. Mitchell, Campell, Christopher, Tashakkori, Rahman
Format: Conference Proceeding
Language:English
Subjects:
Estimation
Informatics
Machine learning
Manuals
Optical Flow
Precision Apiculture
Sociology
Statistics
Traffic Estimation
YOLO
Online Access:Request full text
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Summary:Honey bees are vital to our food chain as they efficiently pollinate many crops and fruits. With the collapse of an alarming number of honey beehives in recent years, there has been a substantial need for regular monitoring of their health status and population growth. Traditionally, beekeepers have manually examined their hives to assess health and growth rates. Traffic in and out of hives provides significant information about the hive's health status and population size. The Appalachian Multi-purpose Apiary Informatics System (AppMAIS) project obtains video recordings at the entrance of 28 hives in the Western region of North Carolina, USA and 3 hives in Belgium. This has allowed us to automate the estimation of traffic at the entrance of the hives using Image Processing and Machine Learning tools. This paper provides details on the YOLO-based and Optical Flow-based approaches we have utilized to estimate the traffic in and out of these hives. With an error of only 17 bees in our estimation models, our early results have been promising. Automated traffic monitoring can significantly reduce the number of required manual hive inspections.
ISSN:1558-058X
DOI:10.1109/SoutheastCon52093.2024.10500030