IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i10p1858-d1245660.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/10/1858/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/10/1858/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Eastwood, C.R. & Chapman, D.F. & Paine, M.S., 2012. "Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia," Agricultural Systems, Elsevier, vol. 108(C), pages 10-18.
    5. Chiara Evangelista & Loredana Basiricò & Umberto Bernabucci, 2021. "An Overview on the Use of Near Infrared Spectroscopy (NIRS) on Farms for the Management of Dairy Cows," Agriculture, MDPI, vol. 11(4), pages 1-21, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    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. Bonamigo, Andrei & Ferenhof, Helio Aisenberg & Forcellini, Fernando Antonio, 2017. "Dairy Ecosystem Barriers Exposed - A Case Study In A Family Production Unit At Western Santa Catarina, Brazil," Organizações Rurais e Agroindustriais/Rural and Agro-Industrial Organizations, Universidade Federal de Lavras, Departamento de Administracao e Economia, vol. 19(01), January.
    2. Sahoo, Dukhabandhu & Mohanty, Pritisudha & Mishra, Surbhi & Behera, Manash & Mohapatra, Souryabrata, 2024. "Does climate-smart agriculture technology improve the subjective well-being of farmers? Evidence from micro-level data," MPRA Paper 123955, University Library of Munich, Germany.
    3. Eastwood, C.R. & Turner, F.J. & Romera, A.J., 2022. "Farmer-centred design: An affordances-based framework for identifying processes that facilitate farmers as co-designers in addressing complex agricultural challenges," Agricultural Systems, Elsevier, vol. 195(C).
    4. Selvaggi, R. & Pappalardo, G. & Zarbà, C. & Lusk, J.L., 2024. "Driving factors behind precision livestock farming tools adoption: The case of the pedometer on dairy farms," Agricultural Systems, Elsevier, vol. 220(C).
    5. Alejandra Engler & Roberto Jara-Rojas & Carlos Bopp, 2016. "Efficient use of Water Resources in Vineyards: A Recursive joint Estimation for the Adoption of Irrigation Technology and Scheduling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5369-5383, November.
    6. Aurélie Cardona & Cristiana Carusi & Michael Mayerfeld Bell, 2021. "Engaged Intermediaries to Bridge the Gap between Scientists, Educational Practitioners and Farmers to Develop Sustainable Agri-Food Innovation Systems: A US Case Study," Sustainability, MDPI, vol. 13(21), pages 1-13, October.
    7. Andrea Arzeni & Francesca Giarè & Mara Lai & Maria Valentina Lasorella & Rossella Ugati & Anna Vagnozzi, 2023. "Interactive Approach for Innovation: The Experience of the Italian EIP AGRI Operational Groups," Sustainability, MDPI, vol. 15(19), pages 1-24, September.
    8. Radko Loučka & Václav Jambor & Jan Nedělník & Jaroslav Lang & Petr Homolka & Filip Jančík & Veronika Koukolová & Petra Kubelková & Yvona Tyrolová & Alena Výborná, 2022. "Differences between chemical analysis and portable near-infrared reflectance spectrometry in maize hybrids," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 67(5), pages 176-184.
    9. Snow, Stephen & Fielke, Simon & Fleming, Aysha & Jakku, Emma & Malakar, Yuwan & Turner, Charles & Hunter, Tammy & Tijs, Sigrid & Bonnett, Graham, 2024. "Climate services for agriculture: Steering towards inclusive innovation in Australian climate services design and delivery," Agricultural Systems, Elsevier, vol. 217(C).
    10. Aldo Dal Prà & Riccardo Bozzi & Silvia Parrini & Alessandra Immovilli & Roberto Davolio & Fabrizio Ruozzi & Maria Chiara Fabbri, 2023. "Discriminant analysis as a tool to classify farm hay in dairy farms," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-14, November.
    11. Balaine, Lorraine & Dillon, Emma J. & Läpple, Doris & Lynch, John, 2020. "Can technology help achieve sustainable intensification? Evidence from milk recording on Irish dairy farms," Land Use Policy, Elsevier, vol. 92(C).
    12. Khanna, Abhishek & Kaur, Sanmeet, 2023. "An empirical analysis on adoption of precision agricultural techniques among farmers of Punjab for efficient land administration," Land Use Policy, Elsevier, vol. 126(C).
    13. Karly Ann Burch & Dawn Nafus & Katharine Legun & Laurens Klerkx, 2023. "Intellectual property meets transdisciplinary co-design: prioritizing responsiveness in the production of new AgTech through located response-ability," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 40(2), pages 455-474, June.
    14. Andrea Colantoni & Nicola Lacetera & Loredana Basiricò & Massimo Malacarne & Andrea Summer & Umberto Bernabucci, 2022. "Innovative Technologies for the Feeding of Dairy Cattle to Ensure Animal Welfare and Production Quality—INNOVALAT," Agriculture, MDPI, vol. 12(5), pages 1-4, April.
    15. Yusei Kawagoe & Ikuo Kobayashi & Thi Thi Zin, 2023. "Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
    16. Peng, Wei & Beggio, Giovanni & Pivato, Alberto & Zhang, Hua & Lü, Fan & He, Pinjing, 2022. "Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    17. Ingram, Julie & Maye, Damian & Bailye, Clive & Barnes, Andrew & Bear, Christopher & Bell, Matthew & Cutress, David & Davies, Lynfa & de Boon, Auvikki & Dinnie, Liz & Gairdner, Julian & Hafferty, Caitl, 2022. "What are the priority research questions for digital agriculture?," Land Use Policy, Elsevier, vol. 114(C).
    18. Sadovska, V., 2018. "Sustainable value creation in the agricultural sector. A literature review," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276984, International Association of Agricultural Economists.
    19. Joly, Frédéric & Nozière, Pierre & Jacquiet, Philippe & Prache, Sophie & Dumont, Bertrand, 2023. "Metabolic assessment of biological mechanisms underlying agroecological systems: The example of parasite dilution and forage niche sharing in mixed-grazing," Agricultural Systems, Elsevier, vol. 210(C).
    20. Yang, Huan & Klerkx, Laurens & Leeuwis, Cees, 2014. "Functions and limitations of farmer cooperatives as innovation intermediaries: Findings from China," Agricultural Systems, Elsevier, vol. 127(C), pages 115-125.

    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:10:p:1858-:d:1245660. 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.