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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
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    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. 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.
    3. 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.
    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.
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