Applying Computer Vision Models to Detect in Real Time the Pollen Flow at the Input of Honeybee Hives (Apis mellifera L.)

  • Daniel de Amaral da Silva UFC
  • Isac Gabriel Abrahão Bomfim IFCE
  • Antonio Rafael Braga UFC
  • Danielo G. Gomes UFC


Pollen flow into the beehives is directly related to the strength and the health of the honeybee (Apis mellifera L.) colonies. Traditional monitoring of beehives is done through manual and invasive inspections, which are time-consuming, stress bees. On the other hand, the recent paradigm of precision beekeeping has allowed for remote and non-invasive monitoring of hives. In this study, six computer vision models were applied to videos recorded at the entrance of honeybee hives to detect incoming pollen flow. The results showed that the YOLOv7 model performed better in the FPS metric, while also achieving a metric of AP75 superior to 77%. The CenterNet model presented the best metrics for real-time applications, with an excellent predictive performance at low computational cost.


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SILVA, Daniel de Amaral da; BOMFIM, Isac Gabriel Abrahão; BRAGA, Antonio Rafael; GOMES, Danielo G.. Applying Computer Vision Models to Detect in Real Time the Pollen Flow at the Input of Honeybee Hives (Apis mellifera L.). In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 14. , 2023, João Pessoa/PB. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 21-30. ISSN 2595-6124. DOI: