Smart Beehive Monitoring for Remote Regions

Research output: ThesisDoctoral Thesis

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This work addresses the design and development of a beehive monitoring system for remote regions. Multiple sensor systems were developed, and deployed at various sites across Western Australia to collect a beehive dataset of 2,170 days. During these deployments, NB-IoT was thoroughly tested for its communication feasibility from remote sites. This is the first work to propose the use of machine learning for beehive weight estimation, and presents two deep learning models for beehive weight estimation. This work also proposes the use of deep learning to optimise the design of beehive monitoring systems for the task of beehive weight estimation.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
  • Cardell-Oliver, Rachel, Supervisor
  • Datta, Amitava, Supervisor
  • Keating, Adrian, Supervisor
  • Putrino, Gino, Supervisor
Award date10 Nov 2022
Publication statusUnpublished - 2022


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