Publication detail

Machine learning and computer vision techniques in continuous beehive monitoring applications: A survey

BILÍK, Š. ZEMČÍK, T. KRATOCHVÍLA, L. ŘIČÁNEK, D. RICHTER, M. ZAMBANINI, S. HORÁK, K.

Original Title

Machine learning and computer vision techniques in continuous beehive monitoring applications: A survey

Type

journal article in Web of Science

Language

English

Original Abstract

The wide use and availability of machine learning and computer vision techniques allows developing relatively complex monitoring systems in multiple domains. Besides the traditional industrial segments, new applications appear not only in biology and agriculture, where they may be employed to detect infection, parasites, and weeds, but also in automated monitoring and early warning systems. This trend clearly reflects the introduction of easily accessible hardware and development kits, such as the Arduino or RaspberryPi family. In this article, more than 50 research projects focusing on automated beehive monitoring methods using computer vision procedures are referenced; most of the approaches then facilitate pollen and Varroa mite detection together with bee traffic monitoring. Such systems could also find use in monitoring and inspecting the health state of honeybee colonies, exhibiting a potential for identifying dangerous conditions before the situation becomes critical and improving periodical bee colony inspection planning to markedly reduce the costs. By extension, our article proposes an analysis of the research trends in the given application field and outlines possible development directions. The entire project has also targeted veterinary and apidology professionals and experts, who might benefit from a matter-of-fact interpretation of machine learning and its capabilities; thus, each family of techniques is preceded by a brief theoretical introduction and motivation related to the relevant base method. The article can inspire other researchers to employ machine learning techniques in specific beehive monitoring applications.

Keywords

Pollen detection, Varroasis detection, Bee traffic inspection, Bee inspection

Authors

BILÍK, Š.; ZEMČÍK, T.; KRATOCHVÍLA, L.; ŘIČÁNEK, D.; RICHTER, M.; ZAMBANINI, S.; HORÁK, K.

Released

9. 2. 2024

Publisher

Elsevier

ISBN

1872-7107

Periodical

COMPUTERS AND ELECTRONICS IN AGRICULTURE

Year of study

217

Number

únor 2024

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

18

Pages count

18

URL

https://www.sciencedirect.com/science/article/pii/S0168169923009481?dgcid=author#bib1

BibTex

@article{BUT186807,
  author="Šimon {Bilík} and Tomáš {Zemčík} and Lukáš {Kratochvíla} and Dominik {Řičánek} and Miloslav {Richter} and Sebastian {Zambanini} and Karel {Horák}",
  title="Machine learning and computer vision techniques in continuous beehive monitoring applications: A survey",
  journal="COMPUTERS AND ELECTRONICS IN AGRICULTURE",
  year="2024",
  volume="217",
  number="únor 2024",
  pages="1--18",
  doi="10.1016/j.compag.2023.108560",
  issn="1872-7107",
  url="https://www.sciencedirect.com/science/article/pii/S0168169923009481?dgcid=author#bib1"
}