Skip to main content
SpringerLink
Log in

Decision-Making Applications on Smart Livestock Farming

  • Chapter
  • First Online:
Information and Communication Technologies for Agriculture—Theme III: Decision

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 184))

  • 402 Accesses

Abstract

Smart livestock farming systems may provide real-time on-farm scenarios enabling fast interventions that benefit the current herd or flock. Smart decision-making technologies refer to more precise control over livestock production processes, helping farmers improve their productivity and profitability. Livestock process parameters are often faced with inaccurate, incomplete, or even conflicting data, and a way of minimizing this effect when processing data is the use of non-classical logic. The use of conceptual non-classical logic might improve smart tools allowing for non-intrusive assessment of health status and welfare, where information can be collected without the stress of disturbing or handling animals. Continuous monitoring can also offer a more complete picture of the overall health and/or well-being of animals rather than a view in time, as provided by traditional assessment. Alerting farmers to problems as they arise in real-time allows for immediate and targeted interventions to benefit the current herds or flocks. This book chapter introduces the fundamentals of managerial processes using non-classic logic and data mining and offers several applications to improve the decision-making of smart livestock farming.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 59.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. FAO. Food and Agriculture Organization of the United Nations. Retrieved October 15, 2020, from http://www.fao.org/faostat/en/#data/QL.

  2. Brueckner, J. K., & Lall, S. V. (2015). Cities in developing countries: Fueled by rural-urban migration, lacking in tenure security, and short of affordable housing. In G. Duranton, J. V. Henderson, & W. Strange (Eds.), Handbook of regional and urban economics (pp. 1399–1455). Elsevier.

    Google Scholar 

  3. Berckmans, D. (2004). Automatic online monitoring of animals by precision livestock farming. In: Proceedings of the ISAH conference on animal production in Europe: The way forward in a changing world, Saint-Malo, France, pp. 27–31.

    Google Scholar 

  4. Halachmi, I., & Guarino, M. (2016). Editorial: Precision livestock farming: A ‘per animal’ approach using advanced monitoring technologies. Animal, 10, 1482–1483. https://doi.org/10.1017/S1751731116001142

    Article  Google Scholar 

  5. Parsons, D. J., Green, D. M., Schofield, C. P., et al. (2007). Real-time control of pig growth through an integrated management system. Biosystems Engineering, 96, 257–266. https://doi.org/10.1016/j.biosystemseng.2006.10.013

    Article  Google Scholar 

  6. Frost, A. R. (2001). An overview of integrated management systems for sustainable livestock production. In C. M. Wathes, A. R. Frost, F. Gordon, et al. (Eds.), Integrated management systems for livestock (pp. 45–50). Selwyn College.

    Google Scholar 

  7. Banhazi, T. M., & Black, J. L. (2009). Precision Livestock Farming: A suite of electronic systems to ensure the application of best practice management on livestock farms. Australian Journal of Multi-Disciplinary Engineering, 7(1), 1–14. https://doi.org/10.1080/14488388.2009.11464794

    Article  Google Scholar 

  8. Schofield, C. P., Wathes, C. M., & Frost, A. R. (2002). Integrated management systems for pigs—Increasing production efficiency and welfare. In D. K. Revell & D. Taplin (Eds.), Animal production in Australia (pp. 197–200). Adelaide.

    Google Scholar 

  9. Nääs, I. A. (2002). Application of mechatronics to animal production. CIGR E-Journal, 4, 1–14.

    Google Scholar 

  10. Moura, D. J., Silva, W. T., Nääs, I. A., et al. (2008). Real time computer stress monitoring of piglets using vocalization analysis. Computers and Electronics in Agriculture, 64, 11–18. https://doi.org/10.1016/j.compag.2008.05.008

    Article  Google Scholar 

  11. Thompson, P. B. (2015). From field to fork: Food ethics for everyone. Oxford University Press.

    Book  Google Scholar 

  12. Berckmans, D. (2014). Precision livestock farming technologies for welfare management in intensive livestock systems. Revue Scientifique et Technique OIE, 33, 189–196.

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Senger, P. L. (1994). The estrus detection problem: New concepts, technologies, and possibilities. Journal of Dairy Science, 77, 2745–2753.

    Article  Google Scholar 

  15. Kettlewell, P. J., & Moran, P. A. (1992). Study of heat production and heat loss in crated broiler chicken: A mathematical model for a single bird. British Poultry Science, 33, 239–252.

    Article  Google Scholar 

  16. Carvalho, V., Nääs, I. A., Mollo, M., et al. (2005). Prediction of the occurrence of lameness in dairy cows using a fuzzy-logic based expert system.—Part I. CIGR E-Journal, 7, 1–12.

    Google Scholar 

  17. Wathes, C. M., Kristensen, H. H., Aerts, M., et al. (2008). Is precision livestock farming an engineer’s daydream or nightmare, an animal’s friend or foe, and a farmer’s panacea or pitfall? Computers and Electronics in Agriculture, 64, 2–10. https://doi.org/10.1016/j.compag.2008.05.005

    Article  Google Scholar 

  18. Borchers, M. R., & Bewley, J. M. (2015). An assessment of producer precision dairy farming technology use, pre-purchase considerations, and usefulness. Journal of Dairy Science, 98, 4198–4205. https://doi.org/10.3168/jds.2014-8963

    Article  Google Scholar 

  19. Van Hertem, T., Rooijakkers, L., Berckmans, D., et al. (2017). Appropriate data and visualization is key to Precision Livestock Farming acceptance. Computers and Electronics in Agriculture, 138, 1–10. https://doi.org/10.1016/j.compag.2017.04.003

    Article  Google Scholar 

  20. Terrasson, G., Villeneuve, E., Pilnière, V., et al (2017). Precision livestock farming: A multidisciplinary paradigm. In Proceeding of the SMART 2017, the sixth international conference on smart cities, systems, devices and technologies. Retrieved April 15, 2020, from https://www.researchgate.net/profile/Eric_Villeneuve2/publication/331373949_Precision_Livestock_Farming_A_Multidisciplinary_Paradigm/links/5c764a2fa6fdcc47159e9873/Precision-Livestock-Farming-A-Multidisciplinary-Paradigm.pdf.

  21. Ramirez, B. C., Hoff, S. J., & Harmon, J. D. (2018). Thermal environment sensor array: Part 2 applying the data to assess grow-finish pig housing. Biosystems Engineering, 174, 341–351. https://doi.org/10.1016/j.biosystemseng.2018.08.003

    Article  Google Scholar 

  22. Li, H., Rong, L., & Zhang, G. (2016). Study on convective heat transfer from pig models by CFD in a virtual wind tunnel. Computers and Electronics in Agriculture, 123(Suppl C). https://doi.org/10.1016/j.compag.2016.02.027

  23. Schofield, C. P., Marchant, J. A., White, R. P., et al. (1999). Monitoring pig growth using a prototype imaging system. Biosystems Engineering, 72, 205–210. https://doi.org/10.1006/jaer.1998.0365

    Article  Google Scholar 

  24. Kashiha, M., Bahr, C., Ott, S., et al. (2014). Automatic monitoring of pig locomotion using image analysis. Livestock Science, 159, 141–148. https://doi.org/10.1016/j.livsci.2013.11.007

    Article  Google Scholar 

  25. Manteuffel, G., Puppe, B., & Schön, P. C. (2004). Vocalization of farm animals as a measure of welfare. Applied Animal Behaviour Science, 88, 163–182. https://doi.org/10.1016/j.applanim.2004.02.012

    Article  Google Scholar 

  26. Cordeiro, A. F. S., Nääs, I. A., Oliveira, S. R., et al. (2013). Understanding vocalization might help to assess stressful conditions in piglets. Animals, 3, 923–934. https://doi.org/10.3390/ani3030923

    Article  Google Scholar 

  27. Finger, G., Hemeryck, M., Duran, C. O., et al. (2014). Practical application of the pig cough monitor in a German fattening pig herd with PRDC. In: Proceedings of the 23rd IPVS congress, Cancun, Mexico, pp. 207–208.

    Google Scholar 

  28. Dawkins, M. S., Lee, H.-J., Waitt, C. D., et al. (2009). Optical flow patterns in broiler chicken flocks as automated measures of behaviour and gait. Applied Animal Behaviour Science, 119, 203–209. https://doi.org/10.1016/j.applanim.2009.04.009

    Article  Google Scholar 

  29. Dawkins, M. S., Cain, R., & Roberts, S. J. (2012). Optical flow, flock behaviour and chicken welfare. Animal Behaviour, 84, 219–223. https://doi.org/10.1016/j.anbehav.2012.04.036

    Article  Google Scholar 

  30. Kashiha, M., Pluk, A., Bahr, C., et al. (2013). Development of an early warning system for a broiler house using computer vision. Biosystems Engineering, 116, 36–45. https://doi.org/10.1016/j.biosystemseng.2013.06.004

    Article  Google Scholar 

  31. Aydin, A. (2017). Development of an early detection system for lameness of broilers using computer vision. Computers and Electronics in Agriculture, 136, 140e146. https://doi.org/10.1016/j.compag.2017.02.019

    Article  Google Scholar 

  32. Nääs, I. A., Lozano, L. C. M., Mehdizadeh, S. A., et al. (2018). Paraconsistent logic used for estimating the gait score of broiler chickens. Biosystems Engineering, 173, 115–123. https://doi.org/10.1016/j.biosystemseng.2017.11.012

    Article  Google Scholar 

  33. Mehdizadeh, S. A., Neves, D. P., Tscharke, M., et al. (2015). Image analysis method to evaluate beak and head motion of broiler chickens during feeding. Computers and Electronics in Agriculture, 114, 88–95. https://doi.org/10.1016/j.compag.2015.03.017

    Article  Google Scholar 

  34. Pereira, E. M., Nääs, I. A., & Garcia, R. G. (2014). Identification of acoustic parameters for broiler welfare estimate. Engenharia Agrícola, 34, 413–421. https://doi.org/10.1590/S0100-69162014000300004

    Article  Google Scholar 

  35. Fontana, I., Tullo, E., Carpentier, L., et al. (2017). Sound analysis to model weight of broiler chickens. Poultry Science, 96, 3938–3943. https://doi.org/10.3382/ps/pex215

    Article  Google Scholar 

  36. Llaria, A., Terrasson, G., Arregui, H.. et al. (2015). Geolocation and monitoring platform for extensive farming in mountain pastures. In Proceedings of the IEEE International Conference on Industrial Technology (ICIT 15), pp. 2420-2425.

    Google Scholar 

  37. Anderson, D. M. (2007). Virtual fencing-past, present and future. Rangeland Journal, 26, 65–78. https://doi.org/10.1071/RJ06036

    Article  Google Scholar 

  38. Hostiou, N., Fagon, J., Chauvat, S., et al. (2017). Impact of precision livestock farming on work and human-animal interactions on dairy farms. A review. Biotechnology, Agronomy, Society and Environment, 21, 268–275.

    Article  Google Scholar 

  39. Leliveld, L. M. C., Düpjan, S., Tuchscherer, A., et al. (2017). Vocal correlates of emotional reactivity within and across contexts in domestic pigs (Sus scrofa). Physiology & Behavior, 181, 117–126. https://doi.org/10.1016/j.physbeh.2017.09.010

    Article  Google Scholar 

  40. Abe, J. M. (2015). Paraconsistent intelligent based-systems: New trends in the applications of paraconsistency. Book Series: Intelligent Systems Reference Library, Springer-Verlag, 94, 306.

    MATH  Google Scholar 

  41. Abe, J. M., Akama, S., & Nakamatsu, K. (2015). Introduction to annotated logics—Foundations for paracomplete and paraconsistent reasoning. Series Title Intelligent Systems Reference Library (Vol. 88, 1st ed., p. 190). Springer International Publishing.

    MATH  Google Scholar 

  42. Fonseca, F. N., Abe, J. M., Nääs, I. A., et al. (2019). Automatic prediction of stress in piglets (Sus Scrofa) using infrared skin temperature. Computers and Electronics in Agriculture, 168, 105–148. https://doi.org/10.1016/j.compag.2019.105148

    Article  Google Scholar 

  43. Silva, J. P., Nääs, I. A., Abe, J. M., et al. (2019). Classification of piglet (Sus Scrofa) stress conditions using vocalization pattern and applying paraconsistent logic Eτ. Computers and Electronics in Agriculture, 166, 105–020. https://doi.org/10.1016/j.compag.2019.105020

    Article  Google Scholar 

  44. Neethirajan, S. (2017). Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research, 12, 15–29. https://doi.org/10.1016/j.sbsr.2016.11.004

    Article  Google Scholar 

  45. White, B. J., Amrine, D. E., & Larson, R. L. (2018). Big data analytics and precision animal agriculture symposium: Data to decisions. Journal of Animal Science, 96, 1531–1539. https://doi.org/10.1093/jas/skx065

    Article  Google Scholar 

  46. Morota, G., Ventura, R. V., Silva, F. F., et al. (2018). Big data analytics and precision animal agriculture symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science, 96, 1540–1550. https://doi.org/10.1093/jas/sky014

    Article  Google Scholar 

  47. Steensels, M., Antler, A., Bahr, C., et al. (2016). A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, body weight and voluntary visits to the milking robot. Animal, 10, 1493–1500. https://doi.org/10.1017/S1751731116000744

    Article  Google Scholar 

  48. Pereira, D. F., Miyamoto, B. C. D., Maia, G. D. N., et al. (2013). Machine vision to identify broiler breeder behavior. Computers and Electronics in Agriculture, 99, 194–199. https://doi.org/10.1016/j.compag.2013.09.012

    Article  Google Scholar 

  49. Vale, M. M., Moura, D. J., Nääs, I. A., et al. (2008). Data mining to estimate broiler mortality when exposed to heatwave. Science in Agriculture, 65, 223–229. https://doi.org/10.1590/S0103-90162008000300001

    Article  Google Scholar 

  50. Wang, Y., Yang, W., Winter, P., et al. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering, 100, 117–125. https://doi.org/10.1016/j.biosystemseng.2007.08.008

    Article  Google Scholar 

  51. Fournel, S., Rousseau, A. N., & Laberge, B. (2017). Rethinking environment control strategy of confined animal housing systems through precision livestock farming. Biosystems Engineering, 155, 96e123. https://doi.org/10.1016/j.biosystemseng.2016.12.005

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate Program in Production Engineering, Universidade Paulista, São Paulo, Brazil

    Irenilza de Alencar Nääs & Jair Minoro Abe

Authors
  1. Irenilza de Alencar Nääs
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jair Minoro Abe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Irenilza de Alencar Nääs .

Editor information

Editors and Affiliations

  1. Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Dionysis D. Bochtis

  2. Department of Electrical and Computer Engineering, University of Aarhus, Aarhus N, Denmark

    Claus Grøn Sørensen

  3. Department of Resources Management and Agricultural Engineering, Agricultural University of Athens, Athens, Greece

    Spyros Fountas

  4. Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Vasileios Moysiadis

  5. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

de Alencar Nääs, I., Abe, J.M. (2022). Decision-Making Applications on Smart Livestock Farming. In: Bochtis, D.D., Sørensen, C.G., Fountas, S., Moysiadis, V., Pardalos, P.M. (eds) Information and Communication Technologies for Agriculture—Theme III: Decision. Springer Optimization and Its Applications, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-030-84152-2_10

Download citation

Publish with us

Policies and ethics

Search

Navigation