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Systems to Monitor the Individual Feeding and Drinking Behaviors of Growing Pigs Based on Machine Vision

Author

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  • Yanrong Zhuang

    (College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Kang Zhou

    (College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Zhenyu Zhou

    (College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Hengyi Ji

    (College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Guanghui Teng

    (College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
    Beijing Engineering Research Center on Animal Healthy Environment, Beijing 100083, China)

Abstract

Feeding and drinking behaviors are important in pig breeding. Although many methods have been developed to monitor them, most are too expensive for pig research, and some vision-based methods have not been integrated into equipment or systems. In this study, two systems were designed to monitor pigs’ feeding and drinking behaviors, which could reduce the impact of the image background. Moreover, three convolutional neural network (CNN) algorithms, VGG19, Xception, and MobileNetV2, were used to build recognition models for feeding and drinking behaviors. The models trained by MobileNetV2 had the best performance, with the recall rate higher than 97% in recognizing pigs, and low mean square error (RMSE) and mean absolute error (MAE) in estimating feeding (RMSE = 0.58 s, MAE = 0.21 s) and drinking durations (RMSE = 0.60 s, MAE = 0.12 s). In addition, the two best models trained by MobileNetV2 were combined with the LabVIEW software development platform, and a new software to monitor the feeding and drinking behaviors of pigs was built that can automatically recognize pigs and estimate their feeding and drinking durations. The system designed in this study can be applied to behavioral recognition in pig production.

Suggested Citation

  • Yanrong Zhuang & Kang Zhou & Zhenyu Zhou & Hengyi Ji & Guanghui Teng, 2022. "Systems to Monitor the Individual Feeding and Drinking Behaviors of Growing Pigs Based on Machine Vision," Agriculture, MDPI, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:103-:d:1019655
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    More about this item

    Keywords

    feeding behavior; drinking behavior; CNN; MobileNetV2; LabVIEW;
    All these keywords.

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