Date of Award

6-14-2023

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2023.

Department

Computer Science and Engineering

First Advisor

Behnam Dezfouli

Abstract

Traditional beehive monitoring systems suffer from many challenges. These monitoring devices are expensive to set up, difficult to implement, and lack cross compatibility with each other. Preexisting beehive monitoring solutions face all of these shortcomings. Our beehive monitoring platform aims to overcome these issues by using inexpensive, off-theshelf, open-source hardware paired with a computer vision machine learning model to accurately monitor the ingress and egress of bees into and out of the hive. This data is presented to the beekeeper in a simple GUI which allows them to track hive activity over time, and by extension, the overall health of the hive. All of the code in this project is open-source while still maintaining a professional look. This enables users to customize it to their needs. However, even if the user has no prior coding experience the proposed solution is easy to setup and run. The final product should alleviate many challenges that other beehive monitoring systems face and should hopefully create a disruption in the beehive monitoring market that would inspire other companies to utilize more cost efficient hardware and open-source software.

Download

Share

COinS