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Identification and Analysis of Emergency Behavior of Cage-Reared Laying Ducks Based on YoloV5

Author

Listed:
  • Yue Gu

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)

  • Shucai Wang

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)

  • Yu Yan

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)

  • Shijie Tang

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)

  • Shida Zhao

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)

Abstract

The behavior of cage-reared ducks is an important index to judge the health status of laying ducks. For the automatic recognition task of cage-reared duck behavior based on machine vision, by comparing the detection performance of YoloV4 (you only look once), YoloV5, and Faster-RCNN, this work selected the YoloV5 target detection network with the best performance to identify the three behaviors related to avoidance after a cage-reared duck emergency. The recognition average precision was 98.2% (neck extension), 98.5% (trample), and 98.6% (spreading wings), respectively, and the detection speed was 20.7 FPS. Based on this model, in this work, 10 duck cages were randomly selected, and each duck cage recorded video for 3 min when there were breeders walking in the duck house and no one was walking for more than 20 min. By identifying the generation time and frequency of neck extension out of the cage, trample, and wing spread, it was concluded that the neck extension, trampling, and wing spread behaviors of laying ducks increase significantly when they feel panic and fear. The research provides an efficient, intelligent monitoring method for the behavior analysis of cage-rearing of ducks and provides a basis for the health status judgment and behavior analysis of unmonitored laying ducks in the future.

Suggested Citation

  • Yue Gu & Shucai Wang & Yu Yan & Shijie Tang & Shida Zhao, 2022. "Identification and Analysis of Emergency Behavior of Cage-Reared Laying Ducks Based on YoloV5," Agriculture, MDPI, vol. 12(4), pages 1-16, March.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:4:p:485-:d:783004
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    References listed on IDEAS

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    1. Abozar Nasirahmadi & Ulrike Wilczek & Oliver Hensel, 2021. "Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models," Agriculture, MDPI, vol. 11(11), pages 1-13, November.
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    Cited by:

    1. Shilin Li & Shujuan Zhang & Jianxin Xue & Haixia Sun & Rui Ren, 2022. "A Fast Neural Network Based on Attention Mechanisms for Detecting Field Flat Jujube," Agriculture, MDPI, vol. 12(5), pages 1-19, May.

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