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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.

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