IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i8p1562-d1210713.html
   My bibliography  Save this article

Division of Cow Production Groups Based on SOLOv2 and Improved CNN-LSTM

Author

Listed:
  • Guanying Cui

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
    These authors contributed equally to this work.)

  • Lulu Qiao

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
    These authors contributed equally to this work.)

  • Yuhua Li

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

  • Zhilong Chen

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

  • Zhenyu Liang

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

  • Chengrui Xin

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

  • Maohua Xiao

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

  • Xiuguo Zou

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
    Faculty of Applied Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada)

Abstract

Udder conformation traits interact with cow milk yield, and it is essential to study the udder characteristics at different levels of production to predict milk yield for managing cows on farms. This study aims to develop an effective method based on instance segmentation and an improved neural network to divide cow production groups according to udders of high- and low-yielding cows. Firstly, the SOLOv2 (Segmenting Objects by LOcations) method was utilized to finely segment the cow udders. Secondly, feature extraction and data processing were conducted to define several cow udder features. Finally, the improved CNN-LSTM (Convolution Neural Network-Long Short-Term Memory) neural network was adopted to classify high- and low-yielding udders. The research compared the improved CNN-LSTM model and the other five classifiers, and the results show that CNN-LSTM achieved an overall accuracy of 96.44%. The proposed method indicates that the SOLOv2 and CNN-LSTM methods combined with analysis of udder traits have the potential for assigning cows to different production groups.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1562-:d:1210713
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/8/1562/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/8/1562/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jun-gyu Kim & Sang-yeon Lee & In-bok Lee, 2023. "The Development of an LSTM Model to Predict Time Series Missing Data of Air Temperature inside Fattening Pig Houses," Agriculture, MDPI, vol. 13(4), pages 1-18, March.
    2. Lan Ma & Fangping Xie & Dawei Liu & Xiushan Wang & Zhanfeng Zhang, 2023. "An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device," Agriculture, MDPI, vol. 13(4), pages 1-15, March.
    3. Enze Duan & Hongyun Hao & Shida Zhao & Hongying Wang & Zongchun Bai, 2023. "Estimating Body Weight in Captive Rabbits Based on Improved Mask RCNN," Agriculture, MDPI, vol. 13(4), pages 1-18, March.
    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. Cheng Shen & Zhong Tang & Maohua Xiao, 2023. "“Eyes”, “Brain”, “Feet” and “Hands” of Efficient Harvesting Machinery," Agriculture, MDPI, vol. 13(10), pages 1-3, September.

    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:13:y:2023:i:8:p:1562-:d:1210713. 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.