Detail publikace

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.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

9. 2. 2024

Nakladatel

Elsevier

ISSN

1872-7107

Periodikum

COMPUTERS AND ELECTRONICS IN AGRICULTURE

Ročník

217

Číslo

únor 2024

Stát

Spojené království Velké Británie a Severního Irska

Strany od

1

Strany do

18

Strany počet

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"
}