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A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

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  • Na Liu

    (College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China
    National Center of Technology Innovation for Dairy-Breeding and Production Research Subcenter, Hohhot 010018, China
    Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Integrated Research Platform of Smart Animal Husbandry at Universities of Inner Mongolia, Inner Mongolia Herbivorous Livestock Feed Engineering Technology Research Center, Hohhot 010018, China)

  • Jingwei Qi

    (College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China
    National Center of Technology Innovation for Dairy-Breeding and Production Research Subcenter, Hohhot 010018, China
    Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Integrated Research Platform of Smart Animal Husbandry at Universities of Inner Mongolia, Inner Mongolia Herbivorous Livestock Feed Engineering Technology Research Center, Hohhot 010018, China)

  • Xiaoping An

    (College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China
    National Center of Technology Innovation for Dairy-Breeding and Production Research Subcenter, Hohhot 010018, China
    Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Integrated Research Platform of Smart Animal Husbandry at Universities of Inner Mongolia, Inner Mongolia Herbivorous Livestock Feed Engineering Technology Research Center, Hohhot 010018, China)

  • Yuan Wang

    (College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China
    National Center of Technology Innovation for Dairy-Breeding and Production Research Subcenter, Hohhot 010018, China
    Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Integrated Research Platform of Smart Animal Husbandry at Universities of Inner Mongolia, Inner Mongolia Herbivorous Livestock Feed Engineering Technology Research Center, Hohhot 010018, China)

Abstract

Milk production plays an essential role in the global economy. With the development of herds and farming systems, the collection of fine-scale data to enhance efficiency and decision-making on dairy farms still faces challenges. The behavior of animals reflects their physical state and health level. In recent years, the rapid development of the Internet of Things (IoT), artificial intelligence (AI), and computer vision (CV) has made great progress in the research of precision dairy farming. Combining data from image, sound, and movement sensors with algorithms, these methods are conducive to monitoring the behavior, health, and management practices of dairy cows. In this review, we summarize the latest research on contact sensors, vision analysis, and machine-learning technologies applicable to dairy cattle, and we focus on the individual recognition, behavior, and health monitoring of dairy cattle and precise feeding. The utilization of state-of-the-art technologies allows for monitoring behavior in near real-time conditions, detecting cow mastitis in a timely manner, and assessing body conditions and feed intake accurately, which enables the promotion of the health and management level of dairy cows. Although there are limitations in implementing machine vision algorithms in commercial settings, technologies exist today and continue to be developed in order to be hopefully used in future commercial pasture management, which ultimately results in better value for producers.

Suggested Citation

  • Na Liu & Jingwei Qi & Xiaoping An & Yuan Wang, 2023. "A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows," Agriculture, MDPI, vol. 13(10), pages 1-21, September.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:10:p:1858-:d:1245660
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    References listed on IDEAS

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    1. Zhen Wang & Shuai Wang & Chunguang Wang & Yong Zhang & Zheying Zong & Haichao Wang & Lide Su & Yingjie Du, 2023. "A Non-Contact Cow Estrus Monitoring Method Based on the Thermal Infrared Images of Cows," Agriculture, MDPI, vol. 13(2), pages 1-19, February.
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    4. Luyu Ding & Yang Lv & Ruixiang Jiang & Wenjie Zhao & Qifeng Li & Baozhu Yang & Ligen Yu & Weihong Ma & Ronghua Gao & Qinyang Yu, 2022. "Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer," Agriculture, MDPI, vol. 12(7), pages 1-18, June.
    5. Rong Wang & Zongzhi Gao & Qifeng Li & Chunjiang Zhao & Ronghua Gao & Hongming Zhang & Shuqin Li & Lu Feng, 2022. "Detection Method of Cow Estrus Behavior in Natural Scenes Based on Improved YOLOv5," Agriculture, MDPI, vol. 12(9), pages 1-19, August.
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    1. Caroline Mendonça Nogueira Paiva & Derick David Quintino & Thacyo Bruno Custódio de Morais & Elisa Guimarães Cozadi & Jaqueline Severino da Costa & Paulo Henrique Montagnana Vicente Leme & José Robert, 2024. "Pathways to Rural Sustainability: Opportunities and Challenges in the Creation of an Agrotechnological District in Ingaí City, Brazil," Agriculture, MDPI, vol. 14(12), pages 1-21, November.
    2. Fredrik Regler & Heinz Bernhardt, 2024. "Standardized Decision-Making for the Selection of Calf and Heifer Rearing Using a Digital Evaluation System," Agriculture, MDPI, vol. 14(2), pages 1-15, February.

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