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Research on an Identification and Grasping Device for Dead Yellow-Feather Broilers in Flat Houses Based on Deep Learning

Author

Listed:
  • Chengrui Xin

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Hengtai Li

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)

  • Yuhua Li

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)

  • Meihui Wang

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)

  • Weihan Lin

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)

  • Shuchen Wang

    (School of Electrical and Control Engineering, Xuzhou University of Technology, Xuzhou 221018, China)

  • Wentian Zhang

    (Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Maohua Xiao

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Xiuguo Zou

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)

Abstract

The existence of dead broilers in flat broiler houses poses significant challenges to large-scale and welfare-oriented broiler breeding. To ensure the timely identification and removal of dead broilers, a mobile device based on visual technology for grasping them was meticulously designed in this study. Among the multiple recognition models explored, the YOLOv6 model was selected due to its exceptional performance, attaining an impressive 86.1% accuracy in identification. This model, when integrated with a specially designed robotic arm, forms a potent combination for effectively handling the task of grasping dead broilers. Extensive experiments were conducted to validate the efficacy of the device. The results reveal that the device achieved an average grasping rate of dead broilers of 81.3%. These findings indicate that the proposed device holds great potential for practical field deployment, offering a reliable solution for the prompt identification and grasping of dead broilers, thereby enhancing the overall management and welfare of broiler populations.

Suggested Citation

  • Chengrui Xin & Hengtai Li & Yuhua Li & Meihui Wang & Weihan Lin & Shuchen Wang & Wentian Zhang & Maohua Xiao & Xiuguo Zou, 2024. "Research on an Identification and Grasping Device for Dead Yellow-Feather Broilers in Flat Houses Based on Deep Learning," Agriculture, MDPI, vol. 14(9), pages 1-19, September.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1614-:d:1478395
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    References listed on IDEAS

    as
    1. Guanying Cui & Lulu Qiao & Yuhua Li & Zhilong Chen & Zhenyu Liang & Chengrui Xin & Maohua Xiao & Xiuguo Zou, 2023. "Division of Cow Production Groups Based on SOLOv2 and Improved CNN-LSTM," Agriculture, MDPI, vol. 13(8), pages 1-21, August.
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