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Multiple Object Tracking in Native Bee Hives: A Case Study with Jataí in the Field

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14197))

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Abstract

Artificial intelligence approaches, such as computer vision, can help better understand the behavior of bees and management. However, the accurate detection and tracking of bee species in the field remain challenging for traditional methods. In this study, we compared YOLOv7 and YOLOv8, two state-of-the-art object detection models, aiming to detect and classify Jataí Brazilian native bees using a custom dataset. Also, we integrated two tracking algorithms (Tracking based on Euclidean distance and ByteTrack) with YOLOv8, yielding a mean average precision (mAP50) of 0.969 and mAP50–95 of 0.682. Additionally, we introduced an optical flow algorithm to monitor beehive entries and exits. We evaluated our approach by comparing it to human performance benchmarks for the same task with and without the aid of technology. Our findings highlight occlusions and outliers (anomalies) as the primary sources of errors in the system. We must consider a coupling of both systems in practical applications because ByteTrack counts bees with an average relative error of 11%, EuclidianTrack monitors incoming bees with 9% (21% if there are outliers), both monitor bees that leave, ByteTrack with 18% if there are outliers, and EuclidianTrack with 33% otherwise. In this way, it is possible to reduce errors of human origin.

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Acknowledgments

The authors would like to thank the Universidade de São Paulo (USP BeeKeep CS - https://beekeep.pcs.usp.br), the Empresa Brasileira de Pesquisa Agropecuária (Embrapa), and the Associação Brasileira de Estudo das Abelhas (A.B.E.L.H.A. - https://abelha.org.br) by the data and videos. People who allowed filmings on their properties. To the Laboratório Multiusuário de Práticas Simuladas (LaMPS - https://lamps.medicina.ufop.br) and the Laboratório de Controle e Automação Multiusuário (LABCAM) for the infrastructure and equipment provided. Google Collaboratory by the technologies that make AI research possible with scarce resources. To Carlos J. Pereira, Eduardo Carvalho, Levi W. R. Filho, and André A. Santos (Instituto Tecnológico Vale) along with Diego M. Alberto (Efí) for their support with the computational methods. This research received financial support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Financing code 001.

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Authors and Affiliations

  1. Programa de Pós-Graduação em Ciência da Computação – Universidade Federal de Ouro Preto (UFOP), Ouro Preto, MG, Brazil

    Rodolfo R. V. Leocádio, Alan Kardek Rêgo Segundo & Gustavo Pessin

  2. Instituto Tecnológico Vale – Mineração, Ouro Preto, MG, Brazil

    Alan Kardek Rêgo Segundo & Gustavo Pessin

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  1. Rodolfo R. V. Leocádio
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  2. Alan Kardek Rêgo Segundo
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  3. Gustavo Pessin
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Correspondence to Rodolfo R. V. Leocádio .

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Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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Leocádio, R.R.V., Segundo, A.K.R., Pessin, G. (2023). Multiple Object Tracking in Native Bee Hives: A Case Study with Jataí in the Field. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14197. Springer, Cham. https://doi.org/10.1007/978-3-031-45392-2_12

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  • DOI: https://doi.org/10.1007/978-3-031-45392-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45391-5

  • Online ISBN: 978-3-031-45392-2

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