IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i21p15527-d1272450.html
   My bibliography  Save this article

Real-Time Monitoring of Animals and Environment in Broiler Precision Farming—How Robust Is the Data Quality?

Author

Listed:
  • Michael Selle

    (Institute of Animal Breeding and Genetics, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany)

  • Fabian Spieß

    (Institute for Animal Nutrition, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany)

  • Christian Visscher

    (Institute for Animal Nutrition, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany)

  • Silke Rautenschlein

    (Clinic for Poultry, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany)

  • Arne Jung

    (Clinic for Poultry, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany)

  • Monika Auerbach

    (Clinic for Poultry, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany)

  • Jörg Hartung

    (Institute of Animal Breeding and Genetics, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany)

  • Christian Sürie

    (Farm for Education and Research Ruthe, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany)

  • Ottmar Distl

    (Institute of Animal Breeding and Genetics, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany)

Abstract

Increasing digitalization in animal farming, commonly addressed as Precision Livestock Farming (PLF), offers benefits in terms of productivity, sustainability, reduced labor and improved monitoring of animal welfare. However, the large amounts of collected data must be stored, processed and evaluated in a proper way. In practice, challenges of continuous and exact data collection can arise, e.g., from air pollutants like dust occluding cameras and sensors, degrading material, the ever-present commotion caused by animals, workers and machines, regularly required maintenance or weak signal transmission. In this study, we analyzed the quality of multi-source spatio-temporal data from a broiler house with 8100 birds over a period of 31 months collected by the Farmer Assistant System (FAS). This is a ceiling-suspended robot equipped with several sensors and cameras that continuously collect data while moving through the barn. The data analysis revealed numerous irregularities: missing values, outliers, repetitive measurements, systematic errors, and temporal and spatial inconsistencies. About 40–50% of all records collected with the early version of the FAS had to be sorted out. The newer version of FAS provided cleaner data, although still about 10–20% of the data had to be removed. Our study has shown that where sophisticated technological systems meet a challenging environment, a thorough and critical review of data completeness and quality is crucial to avoid misinterpretation. The pipeline developed here is designed to help developers and farmers detect failures in signal processing and localize the problem in the hardware components. Scientists, industrial developers and farmers should work more closely together to develop new PLF technologies to more easily advance digitization in agriculture.

Suggested Citation

  • Michael Selle & Fabian Spieß & Christian Visscher & Silke Rautenschlein & Arne Jung & Monika Auerbach & Jörg Hartung & Christian Sürie & Ottmar Distl, 2023. "Real-Time Monitoring of Animals and Environment in Broiler Precision Farming—How Robust Is the Data Quality?," Sustainability, MDPI, vol. 15(21), pages 1-14, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15527-:d:1272450
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/21/15527/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/21/15527/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jonathan McFadden & Francesca Casalini & Terry Griffin & Jesús Antón, 2022. "The digitalisation of agriculture: A literature review and emerging policy issues," OECD Food, Agriculture and Fisheries Papers 176, OECD Publishing.
    2. Keith H Coble & Ashok K Mishra & Shannon Ferrell & Terry Griffin, 2018. "Big Data in Agriculture: A Challenge for the Future," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 40(1), pages 79-96.
    3. Fabian Spieß & Bernd Reckels & Amr Abd-El Wahab & Marwa Fawzy Elmetwaly Ahmed & Christian Sürie & Monika Auerbach & Silke Rautenschlein & Ottmar Distl & Joerg Hartung & Christian Visscher, 2022. "The Influence of Different Types of Environmental Enrichment on the Performance and Welfare of Broiler Chickens and the Possibilities of Real-Time Monitoring via a Farmer-Assistant System," Sustainability, MDPI, vol. 14(9), pages 1-17, May.
    4. Steve MacFeely, 2019. "The Big (data) Bang: Opportunities and Challenges for Compiling SDG Indicators," Global Policy, London School of Economics and Political Science, vol. 10(S1), pages 121-133, January.
    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. Li, Lei & Lin, Jiabao & Ouyang, Ye & Luo, Xin (Robert), 2022. "Evaluating the impact of big data analytics usage on the decision-making quality of organizations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. Xiaoli Zhao & Pavel Castka & Cory Searcy, 2020. "ISO Standards: A Platform for Achieving Sustainable Development Goal 2," Sustainability, MDPI, vol. 12(22), pages 1-19, November.
    3. Jan Anton van Zanten & Rob van Tulder, 2020. "Beyond COVID-19: Applying “SDG logics” for resilient transformations," Journal of International Business Policy, Palgrave Macmillan, vol. 3(4), pages 451-464, December.
    4. Mohammad Amiri-Zarandi & Rozita A. Dara & Emily Duncan & Evan D. G. Fraser, 2022. "Big Data Privacy in Smart Farming: A Review," Sustainability, MDPI, vol. 14(15), pages 1-18, July.
    5. MacFeely Steve, 2020. "Measuring the Sustainable Development Goal Indicators: An Unprecedented Statistical Challenge," Journal of Official Statistics, Sciendo, vol. 36(2), pages 361-378, June.
    6. Komarek, Adam M. & De Pinto, Alessandro & Smith, Vincent H., 2020. "A review of types of risks in agriculture: What we know and what we need to know," Agricultural Systems, Elsevier, vol. 178(C).
    7. MacFeely Steve, 2020. "Measuring the Sustainable Development Goal Indicators: An Unprecedented Statistical Challenge," Journal of Official Statistics, Sciendo, vol. 36(2), pages 361-378, June.
    8. Robert Finger, 2023. "Digital innovations for sustainable and resilient agricultural systems," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(4), pages 1277-1309.
    9. Schroeder, Ted C. & Tonsor, Glynn T. & Coffey, Brian K., 2019. "Commodity futures with thinly traded cash markets: The case of live cattle," Journal of Commodity Markets, Elsevier, vol. 15(C), pages 1-1.
    10. Rim Lassoued & Diego M. Macall & Stuart J. Smyth & Peter W. B. Phillips & Hayley Hesseln, 2021. "Expert Insights on the Impacts of, and Potential for, Agricultural Big Data," Sustainability, MDPI, vol. 13(5), pages 1-18, February.
    11. Keith H. Coble, 2020. "Relevant and/or Elegant Economics," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 392-399, March.
    12. Cara Stitzlein & Simon Fielke & François Waldner & Todd Sanderson, 2021. "Reputational Risk Associated with Big Data Research and Development: An Interdisciplinary Perspective," Sustainability, MDPI, vol. 13(16), pages 1-13, August.
    13. Oksana Hrynevych & Miguel Blanco Canto & Mercedes Jiménez García, 2022. "Tendencies of Precision Agriculture in Ukraine: Disruptive Smart Farming Tools as Cooperation Drivers," Agriculture, MDPI, vol. 12(5), pages 1-15, May.
    14. Katerina Zdravkova, 2023. "Personalized Education for Sustainable Development," Sustainability, MDPI, vol. 15(8), pages 1-13, April.
    15. Rabhi, Loubna & Jabir, Brahim & Falih, Noureddine & Afraites, Lekbir & Bouikhalene, Belaid, 2023. "A Connected farm Metamodeling Using Advanced Information Technologies for an Agriculture 4.0," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 15(2), June.
    16. Lorenzo Donadio & Rossano Schifanella & Claudia R Binder & Emanuele Massaro, 2021. "Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    17. Emily Duncan & Alesandros Glaros & Dennis Z. Ross & Eric Nost, 2021. "New but for whom? Discourses of innovation in precision agriculture," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 38(4), pages 1181-1199, December.
    18. Ainhoa González & Shane Mc Guinness & Enda Murphy & Grainne Kelliher & Lyn Hagin-Meade, 2023. "Priorities, Scale and Insights: Opportunities and Challenges for Community Involvement in SDG Implementation and Monitoring," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    19. Håvard Hegre & Kristina Petrova & Nina von Uexkull, 2020. "Synergies and Trade-Offs in Reaching the Sustainable Development Goals," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    20. Wang, Tong & Jin, Hailong & Sieverding, Heidi & Kumar, Sandeep & Miao, Yuxin & Rao, Xudong & Obembe, Oladipo & Mirzakhani Nafchi, Ali & Redfearn, Daren & Cheye, Stephen, 2023. "Understanding farmer views of precision agriculture profitability in the U.S. Midwest," Ecological Economics, Elsevier, vol. 213(C).

    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:jsusta:v:15:y:2023:i:21:p:15527-:d:1272450. 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.