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

• URL: http://arxiv.org/abs/2208.00085v3
• Date: Thu, 14 Sep 2023 14:45:30 GMT
• Title: Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A survey
• Authors: Simon Bilik, Tomas Zemcik, Lukas Kratochvila, Dominik Ricanek, Milos Richter, Sebastian Zambanini, Karel Horak
• Abstract summary: We survey 50 existing papers focusing on the methods of automated beehive monitoring methods using the computer vision techniques. This paper is aimed at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce them to its possibilities. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wide use and availability of the machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new application appears also in biology and agriculture, where we could speak about the detection of infections, parasites and weeds, but also about automated monitoring and early warning systems. This is also connected with the introduction of the easily accessible hardware and development kits such as Arduino, or RaspberryPi family. In this paper, we survey 50 existing papers focusing on the methods of automated beehive monitoring methods using the computer vision techniques, particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Later, we also include analysis of the research trends in this application field and we outline the possible direction of the new explorations. Our paper is aimed also at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce them to its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring.

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