Research library

Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A Survey

Keywords: Pollen detection Varroasis detection Bee traffic inspection Bee inspection Machine learning Computer vision Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic.

Download PDF

Publication details

Authors
Simon BilikⰠTomas ZemcikⰠLukas KratochvilaⰠ整⁡氮
Organizations
🇨🇿 Brno University of Technology
Year
2023
Type
Journal

Relevancy to Gratheon

This paper is relevant to Gratheon because it informs camera-based hive-scanner and computer-vision models, sensor hardware, telemetry pipelines, and monitoring dashboards, product strategy and competitive research mapping. Its methods and findings can be translated into product requirements for reliable field deployments: what should be sensed, how signals should be interpreted, and which uncertainty or validation limits need to be surfaced to beekeepers. For Gratheon, the work is most useful as an evidence-backed design reference for connecting local hive observations with actionable recommendations in the web app while keeping hardware practical for remote apiaries.