Praktische Bewertung sensorbasierter Technologien in Schweinemastbetrieben

Autor/innen

  • Matthias Filipiak
  • Georg-Friedrich Thimm
  • Philipp Hölscher
  • Joanna Stachowicz

DOI:

https://doi.org/10.15150/ae.2026.3360

Schlagworte:

Präzisionstierhaltung, Sensortechnik, Sensorausfälle, Sensorauswertung, Tierwohl

Abstract

In dieser Studie haben wir die Zuverlässigkeit von handelsüblichen, modifizierten handelsüblichen und nicht-handelsüblichen Systemen über drei Mastzyklen hinweg auf vier Schweinemastbetrieben bewertet. Auf der Grundlage von Ausfallraten, erforderlichen Eingriffen und entstandenen Datenlücken ergab die Clusteranalyse eine Low-Effort-Gruppe, die beide modifizierten Systeme, das nicht-handelsübliche und ein handelsübliches System umfasste, und eine High-Effort-Gruppe, die ausschließlich aus handelsüblichen Systemen bestand. Die meisten Ausfälle wurden durch Netzwerkprobleme verursacht, gefolgt von vereinzelten Schäden aufgrund von Schmutzansammlungen oder Korrosion. Die Regressionsanalyse der monatlichen Ausfallraten deutet jedoch darauf hin, dass die Ausfälle überwiegend zufällig und akut auftraten, was auf Zuverlässigkeitsprobleme auf struktureller Ebene in der High-Effort-Gruppe hindeutet. Für den praktischen Einsatz in landwirtschaftlichen Betrieben empfehlen wir einfache, aber robuste Systeme, die sich durch eine geringe Anzahl an Komponenten, wenige Verarbeitungsschritte, kurze Datenwege und eine unkomplizierte Installation auszeichnen.

Literaturhinweise

Banhazi, T.; Anhazi, A.; Tikasz, I.,Palotay, S; Mallimger, K.; Neubauer, T.; Corpaci, L.; Marchaim, U.; Kopler, I.; Opalinski, S.; Olejnik, K.; Kokin, E.; Gunnarsson, S.; Bjerre, T.; Soerensen, C. (2024): Facilitating PLF Technology Adoption in the Pig and Poultry Industries. Studies in Agricultural Economics 126(1), https://doi.org/10.7896/j.2725

Banhazi, T.; Lehr, H.; Black, J.; Crabtree, H.; Schofield, C.; Tscharke, M.; Berckmans, D. (2012): Precision Livestock Farming: An international review of scientific and commercial aspects. International Journal of Agricultural and Biological Engineering 5(3), pp. 1–9, https://doi.org/10.3965/j.ijabe.20120503.00

Banhazi, T.; Vranken, E.; Berckmans, D.; Rooijakkers, L. (2015): Word of caution for technology providers: practical problems associated with large scale deployment of PLF technologies on commercial farms. In: Precision livestock farming applications, ed. Halachmi, I., Brill, Wageningen Academic, pp. 105–112

Block, U. (2011): ISOweek: Week of the year and weekday according to ISO 8601. R package version 0.6-2. https://CRAN.R-project.org/package=ISOweek, accessed on 6 Apr 2026

BMLEH (2025): The husbandry types. https://www.tierhaltungskennzeichnung.de/en/user/husbandry-types/, accessed on 10 Sept 2025

Bruijn, B. de; Mol, R.M. de; Hogewerf, P.H.; van der Fels, J.B. (2023): A correlated-variables model for monitoring individual growing-finishing pig’s behavior by RFID registrations. Smart Agricultural Technology 4, 100189, https://doi.org/10.1016/j.atech.2023.100189

Cao, Z.; Tang, Y.; Zou, H.-Y.; Huang, Y.; Wu, L.; Xiao, Z.-L.; Feng, Z.-M.; Yin, Y.-L.; Yu, D. (2022): Long-term and stable detection of H2S in a pig house at low operating temperature based on Ce2O3/In2O3 hollow microspheres with a remote monitoring system. Sensors and Actuators B: Chemical 372, 132609, https://doi.org/10.1016/j.snb.2022.132609

Chae, H.; Lee, J.; Kim, J.; Lee, S.; Chung, Y.; Park, D. (2024): Novel Method for Detecting Coughing Pigs with Audio-Visual Multimodality for Smart Agriculture Monitoring. Sensors 24(22), https://doi.org/10.3390/s24227232

Chantziaras, I.; Meyer, D. de; Vrielinck, L.; van Limbergen, T.; Pineiro, C.; Dewulf, J.; Kyriazakis, I.; Maes, D. (2020): Environment-, health-, performance- and welfare-related parameters in pig barns with natural and mechanical ventilation. Preventive Veterinary Medicine 183, 105150, https://doi.org/10.1016/j.prevetmed.2020.105150

Chen, C.; Zhu, W.; Steibel, J.; Siegford, J.; Han, J.; Norton, T. (2020): Classification of drinking and drinker-playing in pigs by a video-based deep learning method. Biosystems Engineering 196, pp. 1–14, https://doi.org/10.1016/j.biosystemseng.2020.05.010

Dean, C.; Lawless, J.F. (1989): Tests for Detecting Overdispersion in Poisson Regression Models. Journal of the American Statistical Association 84(406), pp. 467–472, https://doi.org/10.1080/01621459.1989.10478792

D’Eath, R.B.; Jack, M.; Futro, A.; Talbot, D.; Zhu, Q.; Barclay, D.; Baxter, E.M. (2018): Automatic early warning of tail biting in pigs: 3D cameras can detect lowered tail posture before an outbreak. PLOS One 13(4), e0194524, https://doi.org/10.1371/journal.pone.0194524

Decarie, F.; Grant, C.; Dallago, G. (2025): Weighing finishing pigs in motion: A walk-over scale for accurate weight estimation. Computers and Electronics in Agriculture 232, 110019, https://doi.org/10.1016/j.compag.2025.110019

Domun, Y.; Pedersen, L.J.; White, D.; Adeyemi, O.; Norton, T. (2019): Learning patterns from time-series data to discriminate predictions of tail-biting, fouling and diarrhoea in pigs. Computers and Electronics in Agriculture 163, 104878, https://doi.org/10.1016/j.compag.2019.104878

Fuchs, P.; Adrion, F.; Shafiullah, A.Z.M.; Bruckmaier, R.M.; Umstätter, C. (2022): Detecting Ultra- and Circadian Activity Rhythms of Dairy Cows in Automatic Milking Systems Using the Degree of Functional Coupling—A Pilot Study. Frontiers in Animal Science 3, https://doi.org/10.3389/fanim.2022.839906

Gómez, Y.; Stygar, A.H.; Boumans, I J M M; Bokkers, E.A.M.; Pedersen, L.J.; Niemi, J.K.; Pastell, M.; Manteca, X.; Llonch, P. (2021): A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare. Frontiers in veterinary science Volume 8, https://doi.org/10.3389/fvets.2021.660565

Groher, T.; Heitkämper, K.; Umstätter, C. (2020a): Digital technology adoption in livestock production with a special focus on ruminant farming. Animal: an international journal of animal bioscience 14(11), pp. 2404–2413, https://doi.org/10.1017/S1751731120001391

Groher, T.; Heitkämper, K.; Walter, A.; Liebisch, F.; Umstätter, C. (2020b): Status quo of adoption of precision agriculture enabling technologies in Swiss plant production. Precision Agriculture 21(6), pp. 1327–1350, https://doi.org/10.1007/s11119-020-09723-5

Grolemund, G.; Wickham, H. (2011): Dates and Times Made Easy with lubridate. Journal of Statistical Software 40(3), pp. 1–25

Hakansson, F.; Jensen, D.B. (2022): Automatic monitoring and detection of tail-biting behavior in groups of pigs using video-based deep learning methods. Frontiers in veterinary science 9, 1099347, https://doi.org/10.3389/fvets.2022.1099347

Heitkämper, K.; Mielewczik, M.; Bozzolini, G.; Groher, T.; Umstätter, C. (2021): Stand der Mechanisierung in der Schweizer Landwirtschaft : Teil 2: Tierhaltung, Agroscope Transfer 352, https://doi.org/10.34776/at352g

Hilbe, J.M. (Hg.) (2014): Modeling Count Data. Cambridge, Cambridge University Press

Hollander, M.; Wolfe, D.A.; Chicken, E. (2015): Nonparametric Statistical Methods. Weinheim, Wiley-VCH

Hou, G.; Li, R.; Tian, M.; Ding, J.; Zhang, X.; Yang, B.; Chen, C.; Huang, R.; Yin, Y. (2024): Improving Efficiency: Automatic Intelligent Weighing System as a Replacement for Manual Pig Weighing. Animals 14(11), https://doi.org/10.3390/ani14111614

Islam, M.M.; Scott, S.D. (2022): Exploring the Effects of Precision Livestock Farming Notification Mechanisms on Canadian Dairy Farmers. Cham, Springer International Publishing, pp. 247–266

Jesus, G.; Casimiro, A.; Oliveira, A. (2017): A Survey on Data Quality for Dependable Monitoring in Wireless Sensor Networks. Sensors 17(9), https://doi.org/10.3390/s17092010

Kashiha, M.; Bahr, C.; Ott, S.; Moons, C.P.; Niewold, T.A.; Ödberg, F.O.; Berckmans, D. (2014): Automatic weight estimation of individual pigs using image analysis. Computers and Electronics in Agriculture 107, pp. 38–44, https://doi.org/10.1016/j.compag.2014.06.003

Kassambara, A. (2020): ggpubr: ‘ggplot2’ Based Publication. Ready Plots. R package version 0.4.0. https://CRAN.R-project.org/package=ggpubr, accessed on 6 Apr 2026

Kassambara, A. (2023): ggcorrplot: Visualization of a Correlation Matrix using ‘ggplot2’. R package version 0.1.4.1. https://CRAN.R-project.org/package=ggcorrplot, accessed on 6 Apr 2026

Kassambara, A.; Mundt, F. (2020): factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7. https://CRAN.R-project.org/package=factoextra, accessed on 6 Apr 2026

Kazda, M.; Hartje, J.; Clauß, M. (2025): A low cost sensor system for determining emissions in open stable systems. In: 2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6

Kendall, M.G. (1938): A New Measure of Rank Correlation. Biometrika 30(1/2), pp. 81–93, https://doi.org/10.2307/2332226

Khan, D.; Zaman, H.; Sultan, A.; Ahmad, M.T.; Bibi, R.; Athar, H.; Bibi, S.; Ahmed, U.; Yasin, U.; Shahbaz, M. (2025): Exploring the Potential of Precision Livestock Farming Technologies for Enhancing Animal Welfare and Public Health. Scholars Journal of Agriculture and Veterinary Sciences 12(06), pp. 205–212, https://doi.org/10.36347/sjavs.2025.v12i06.001

Kittawornrat, A.; Zimmerman, J.J. (2011): Toward a better understanding of pig behavior and pig welfare. Animal Health Research Reviews 12(1), pp. 25–32, https://doi.org/10.1017/S1466252310000174

Li, Y.; Fu, C.; Yang, H.; Li, H.; Zhang, R.; Zhang, Y.; Wang, Z. (2023): Design of a Closed Piggery Environmental Monitoring and Control System Based on a Track Inspection Robot. Agriculture 13(8), 1501, https://doi.org/10.3390/agriculture13081501

Little, S.B.; Browning, G.F.; Woodward, A.P.; Billman-Jacobe, H. (2022): Water consumption and wastage behaviour in pigs: implications for antimicrobial administration and stewardship. Animal: an international journal of animal bioscience 16(8), 100586, https://doi.org/10.1016/j.animal.2022.100586

Lüdecke, D.; Ben-Shachar, M.S.; Patil, I.; Waggoner, P.; Makowski, D. (2021): performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Software 6(60), 3139, https://doi.org/10.21105/joss.03139

Lyu, Q.; Dai, X.; Zhou, B.; Wang, F.; Dai, L.; Xiao, H.; Ni, J.-Q. (2025): Mapping competences for digital empowerment: a systematic review of smallholder farmers’ adoption of digital technologies. Universal Access in the Information Society, https://doi.org/10.1007/s10209-025-01238-y

Maechler, M.; Rousseeuw, P.; Struyf, A.; Hubert, M.; Hornik, K. (2024): cluster: Cluster Analysis Basics and Extensions. R package version 2.1.8. https://CRAN.R-project.org/package=cluster, accessed on 6 Apr 2026

Marku, D.; Hoxha-Jahja, A.; Pfaff, S. (2024): Exploring Barriers to Technology Adoption in Livestock Farming: A Descriptive Comparative Analysis Between Finland, Germany, and Albania. In: 10th International Scientific Conference ERAZ - Knowledge Based Sustainable Development, June 6, 2024, Association of Economists and Managers of the Balkans, Belgrade, Serbia, pp. 233–241

Maselyne, J.; Adriaens, I.; Huybrechts, T.; Ketelaere, B. de; Millet, S.; Vangeyte, J.; van Nuffel, A.; Saeys, W. (2016): Measuring the drinking behaviour of individual pigs housed in group using radio frequency identification (RFID). Animal: an international journal of animal bioscience 10(9), pp. 1557–1566, https://doi.org/10.1017/S1751731115000774

Maselyne, J.; Saeys, W.; Ketelaere, B. de; Mertens, K.; Vangeyte, J.; Hessel, E.F.; Millet, S.; van Nuffel, A. (2014): Validation of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing-finishing pigs. Computers and Electronics in Agriculture 102, pp. 10–18, https://doi.org/10.1016/j.compag.2013.12.015

Moser, J.; Kohler, S.; Hentgen, J.; Meylan, M.; Schüpbach-Regula, G. (2024): Assessment of Ammonia Concentrations and Climatic Conditions in Calf Housing Using Stationary and Mobile Sensors. Animals 14(13), p. 2001, https://doi.org/10.3390/ani14132001

Müller, K.; Wickham, H. (2023): tibble: Simple Data Frames. Version 3.2.1. https://CRAN.R-project.org/package=tibble, accessed on 6 Apr 2026

Murtagh, F.; Legendre, P. (2014): Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? Journal of Classification 31(3), pp. 274–295, https://doi.org/10.1007/s00357-014-9161-z

Neethirajan, S. (2020): The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research 29, 100367, https://doi.org/10.1016/j.sbsr.2020.100367

Oli, D.; Gyawali, B.; Acharya, S.; Oshikoya, S. (2025): Factors influencing learning attitude of farmers regarding adoption of farming technologies in farms of Kentucky, USA. Smart Agricultural Technology 10, 100801, https://doi.org/10.1016/j.atech.2025.100801

Pedersen, T.L. (2025): patchwork: The Composer of Plots. R package version 1.3.1. 10.32614/CRAN.package.patchwork, accessed on 6 Apr 2026

Probst, J.; Volkmann, N.; Lensches, C.; Heseker, P.; Thimm, G.; Lieboldt, M.; Traulsen, I.; Kemper, N. (2023): First approach of using sows’ water consumption data to detect the onset of farrowing. Poster (Poster 13, Session 53). In: Book of Abstracts of the 74th Annual Meeting of the European Federation of Animal Science, ed. European Federation of Animal Science (EAAP), Lyon, France, Wageningen Academic Publishers

R Core Team (2019): R: A language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing

Reeves, M.C.; Grøva, L.; Jessiman, L.; Dwyer, C.M. (2025): Norwegian sheep farmers’ perception of the advantages and disadvantages of Precision Livestock Farming (PLF) technologies. Journal of Rural Studies 117, 103684, https://doi.org/10.1016/j.jrurstud.2025.103684

Reichardt, M.; Jürgens, C. (2009): Adoption and future perspective of precision farming in Germany: results of several surveys among different agricultural target groups. Precision Agriculture 10(1), pp. 73–94, https://doi.org/10.1007/s11119-008-9101-1

Reza, M.N.; Ali, M.R.; Haque, M.A.; Jin, H.; Kyoung, H.; Choi, Y.K.; Kim, G.; Chung, S.-O. (2025): A review of sound-based pig monitoring for enhanced precision production. Journal of animal science and technology 67(2), pp. 277–302, https://doi.org/10.5187/jast.2024.e113

Robinson, D.; Hayes, A.; Couch, S. (2024): broom: Convert Statistical Objects into Tidy Tibbles. R package version 1.0.7. https://CRAN.R-project.org/package=broom, accessed on 6 Apr 2026

Rousseeuw, P.J. (1987): Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, pp. 53–65, https://doi.org/10.1016/0377-0427(87)90125-7

Schloerke, B.; Cook, D.; Larmarange, J.; Briatte, F.; Marbach, M.; Thoen, E.; Elberg, A.; Crowley, J. (2021): GGally: Extension to ‘ggplot2’. R package version 2.1.1. https://CRAN.R-project.org/package=GGally, accessed on 6 Apr 2026

Schodl, K.; Stygar, A.; Steininger, F.; Egger-Danner, C. (2024): Sensor data cleaning for applications in dairy herd management and breeding. Frontiers in Animal Science 5, https://doi.org/10.3389/fanim.2024.1444948

Stygar, A.H.; Gómez, Y.; Berteselli, G.V.; Dalla Costa, E.; Canali, E.; Niemi, J.K.; Llonch, P.; Pastell, M. (2021): A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle. Frontiers in veterinary science 8, https://doi.org/10.3389/fvets.2021.634338

Tavares, J.; Filho, P.B.; Coldebella, A.; Oliveira, P. (2014): The water disappearance and manure production at commercial growing-finishing pig farms. Livestock Science 169, pp. 146–154, https://doi.org/10.1016/j.livsci.2014.09.006

Trabachini, A.; Moreira, M.; Harada, É.D.; Amorim, M.D.; Silva-Miranda, K.O. (2025): Precision Livestock Farming Applied to Swine Farms—A Systematic Literature Review. Animals 15(14), https://doi.org/10.3390/ani15142138

Tuyttens, F.A.M.; Molento, C.F.M.; Benaissa, S. (2022): Twelve Threats of Precision Livestock Farming (PLF) for Animal Welfare. Frontiers in veterinary science 9, 889623, https://doi.org/10.3389/fvets.2022.889623

Venables, W.N.; Ripley, B.D. (2002): Modern Applied Statistics with S. Fourth Edition. https://www.stats.ox.ac.uk/pub/MASS4/, accessed on 6 Apr 2026

Vranken, E.; Berckmans, D. (2017): Precision livestock farming for pigs. Animal Frontiers 7(1), pp. 32–37, https://doi.org/10.2527/af.2017.0106

Wei, T.; Simko, V. (2017): R package “corrplot”: Visualization of a Correlation Matrix (Version 0.84). https://github.com/taiyun/corrplot, accessed on 6 Apr 2026

Werkheiser, I. (2020): Technology and responsibility: a discussion of underexamined risks and concerns in Precision Livestock Farming. Animal Frontiers 10(1), pp. 51–57, https://doi.org/10.1093/af/vfz056

Wickham, H. (2016): ggplot2: Elegant Graphics for Data Analysis. New York, Springer

Wickham, H. (2020): tidyr: Tidy Messy Data. R package version 1.1.2. https://CRAN.R-project.org/package=tidyr, accessed on 6 Apr 2026

Wickham, H.; François, R.; Henry, L.; Müller, K.; Vaughan, D. (2023a): dplyr: A Grammar of Data Manipulation. R package version 1.1.4. https://CRAN.R-project.org/package=dplyr, accessed on 6 Apr 2026

Wickham, H.; Henry, L. (2025): purrr: Functional Programming Tools. R package version 1.0.4. https://CRAN.R-project.org/package=purrr, accessed on 6 Apr 2026

Wickham, H.; Hester, J.; Bryan, J. (2024): readr: Read Rectangular Text Data. R package version 2.1.5. https://CRAN.R-project.org/package=readr, accessed on 6 Apr 2026

Wickham, H.; Pedersen, T.; Seidel D (2023b): scales: Scale Functions for Visualization. R package version 1.3.0. https://CRAN.R-project.org/package=scales, accessed on 6 Apr 2026

Wilke, C. (2024): cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 1.1.3. https://CRAN.R-project.org/package=cowplot, accessed on 6 Apr 2026

Williams, D.E. (2019): Low Cost Sensor Networks: How Do We Know the Data Are Reliable? ACS sensors 4(10), pp. 2558–2565, https://doi.org/10.1021/acssensors.9b01455

Witte, J.-H.; Heseker, P.; Probst, J.; Kemper, N.; Traulsen, I.; Gómez, J. (2024): Tail Posture as a Predictor of Tail Biting in Pigs: A Camera-Based Monitoring System. The 11th European Conference on Precision Livestock Farming, 9–12 September 2024, Bologna, Italy, pp. 341—349

Yu, G. (2023): ggplotify: Convert Plot to ‘grob’ or ‘ggplot’ Object. R package version 0.1.2. https://CRAN.R-project.org/package=ggplotify, accessed on 6 Apr 2026

Zou, X.; Liu, W.; Huo, Z.; Wang, S.; Chen, Z.; Xin, C.; Bai, Y.; Liang, Z.; Gong, Y.; Qian, Y.; Shu, L. (2023): Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things. Sensors 23(5), https://doi.org/10.3390/s23052528

Downloads

Zusätzliche Dateien

Veröffentlicht

10.06.2026

Zitationsvorschlag

Filipiak, M., Thimm, G.-F., Hölscher, P., & Stachowicz, J. (2026). Praktische Bewertung sensorbasierter Technologien in Schweinemastbetrieben. Agricultural engineering.Eu, 81(2). https://doi.org/10.15150/ae.2026.3360

Ausgabe

Rubrik

Fachartikel

Am häufigsten gelesenen Artikel dieser/dieses Autor/in