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Varroa Mite Detection Using Deep Learning Techniques

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Hybrid Artificial Intelligent Systems (HAIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14001))

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Abstract

The varroa mite is a major problem for beekeeping today because it threatens the survival of hives. This paper develops deep learning methods for detecting varroa in images to monitor the level of infestation of the hives in order to use treatments against varroa in time and save the bees. The ultimate goal is its implementation by beekeepers. Therefore, the deep learning models are trained on pictures taken by smartphone cameras covering the entire board where both pupae and varroas are placed. This makes the object detection task a challenge, since it becomes a small object detection problem. This paper shows the experiments that have been developed to solve this challenge, such as the use of super resolution techniques, as well as the difficulties encountered.

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Notes

  1. 1.

    For instance, https://beemapping.com, www.beescanning.com and https://apisfero.org.

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Acknowledgments

This work is supported by grants PID2020-112673RB-I00, PID2020-116641GB-I00 and PID2021-123219OB-I00 funded by MCIN/AEI/ 10.13039/501100011033, and the DGA-FSE (grant A07-17R).

Author information

Authors and Affiliations

  1. University of La Rioja, Logroño, Spain

    Jose Divasón, Francisco Javier Martinez-de-Pison & Ana Romero

  2. University of Zaragoza, Zaragoza, Spain

    Pilar Santolaria & Jesús L. Yániz

Authors
  1. Jose Divasón
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  2. Francisco Javier Martinez-de-Pison
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  3. Ana Romero
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  4. Pilar Santolaria
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  5. Jesús L. Yániz
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Corresponding author

Correspondence to Jose Divasón .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of Leon, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. University of A Coruña, Ferrol - Coruña, Spain

    José Luis Calvo Rolle

  8. University of A Coruña, Ferrol - Coruña, Spain

    Héctor Quintián

  9. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Divasón, J., Martinez-de-Pison, F.J., Romero, A., Santolaria, P., Yániz, J.L. (2023). Varroa Mite Detection Using Deep Learning Techniques. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_28

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  • DOI: https://doi.org/10.1007/978-3-031-40725-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40724-6

  • Online ISBN: 978-3-031-40725-3

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