IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i8p1176-d882580.html
   My bibliography  Save this article

A Dead Broiler Inspection System for Large-Scale Breeding Farms Based on Deep Learning

Author

Listed:
  • Hongyun Hao

    (College of Engineering, China Agriculture University, Beijing 100082, China)

  • Peng Fang

    (College of Engineering, Jiangxi Agriculture University, Nanchang 330045, China)

  • Enze Duan

    (Agricultural Facilities and Equipment Research Institute, Jiangsu Academy of Agriculture Sciences, Nanjing 210014, China)

  • Zhichen Yang

    (College of Engineering, China Agriculture University, Beijing 100082, China)

  • Liangju Wang

    (College of Engineering, China Agriculture University, Beijing 100082, China)

  • Hongying Wang

    (College of Engineering, China Agriculture University, Beijing 100082, China)

Abstract

Stacked cage is the main breeding method of the large-scale farm in China. In broiler farms, dead broiler inspection is a routine task in the breeding process. It refers to the manual inspection of all cages and removal of dead broilers in the broiler house by the breeders every day. However, as the total amount of broilers is huge, the inspection work is not only time-consuming but also laborious. Therefore, a dead broiler inspection system is constructed in this study to replace the manual inspection work. It mainly consists of an autonomous inspection platform and a dead broiler detection model. The automatic inspection platform performs inspections at the speed of 0.2 m/s in the broiler house aisle, and simultaneously collects images of the four-layer broilers. The images are sent to a server and processed by a dead broiler detection model, which was developed based on the YOLOv3 network. A mosaic augment, the Swish function, an spatial pyramid pooling (SPP) module, and complete intersection over union (CIoU) loss are used to improve the YOLOv3 performance. It achieves a 98.6% mean average precision (intersection of union (IoU) = 0.5) and can process images at 0.007 s per frame. The dead broiler detection model is robust to broilers of different ages and can adapt to different lighting conditions. It is deployed on the server with a human–machine interface. By observing the processing results using the human–machine interface, the breeders could directly find the cage position of dead broilers and remove them, which could reduce the workload of breeders and promote the intelligent development of poultry breeding.

Suggested Citation

  • Hongyun Hao & Peng Fang & Enze Duan & Zhichen Yang & Liangju Wang & Hongying Wang, 2022. "A Dead Broiler Inspection System for Large-Scale Breeding Farms Based on Deep Learning," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1176-:d:882580
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/8/1176/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/8/1176/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Katarzyna Olejnik & Ewa Popiela & Sebastian Opaliński, 2022. "Emerging Precision Management Methods in Poultry Sector," Agriculture, MDPI, vol. 12(5), pages 1-18, May.
    2. Helen Louton & Shana Bergmann & Andre Piller & Michael Erhard & Jenny Stracke & Birgit Spindler & Paul Schmidt & Jan Schulte-Landwehr & Angela Schwarzer, 2022. "Automatic Scoring System for Monitoring Foot Pad Dermatitis in Broilers," Agriculture, MDPI, vol. 12(2), pages 1-16, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yiting Yin & Ailin Diao & Ziyi Li & Qi Wang & Shuguang Liu, 2023. "Design of an Automatic Ground Cleaning Machine for Dedusting Rooms of Chicken Houses," Agriculture, MDPI, vol. 13(6), pages 1-22, June.
    2. Honorata Sierocka & Maciej Zajkowski & Grzegorz Hołdyński & Zbigniew Sołjan, 2023. "Characteristics of Electricity Consumption on the Example of Poultry Farming in Poland," Energies, MDPI, vol. 16(1), pages 1-17, January.
    3. Nicole Kemper, 2023. "Animal Hygiene on Farms—More Important than Ever Before," Agriculture, MDPI, vol. 13(8), pages 1-3, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1176-:d:882580. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.