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Rilevamento automatico delle modificazioni comportamentali nei suini per il monitoraggio del benessere e dello stato sanitario

  1. Dawkins, M. Using behaviour to assess animal welfare. Animal Welfare 13, 3–7 (2004).

  2. Kyriazakis, I. & Houdijk, J. Food intake and performance of pigs during health, disease and recovery. In Proceedings of 62nd Easter

    School in the Agricultural and Food Sciences, 493–513 (Nottingham University Press, Nottingham, UK, 2007).

  3. Godfray, H. C. J. et al. Food security: The challenge of feeding 9 billion people. Science 327, 812–818 (2010).

  4. Dell’Omo, G. et al. Early behavioural changes in mice infected with bse and scrapie: automated home cage monitoring reveals prion strain differences. European Journal of Neuroscience 16, 735–742 (2002).

  5. Moinard, C., Mendl, M., Nicol, C. & Green, L. A case control study of on-farm risk factors for tail biting in pigs. Appl Anim Behav Sci 81, 333–355 (2003).

  6. Zonderland, J. J. et al. Characteristics of biter and victim piglets apparent before a tail-biting outbreak. animal 5, 767–775 (2011).

  7. Nasirahmadia, A., Edwards, S. A. & Sturm, B. Implementation of machine vision for detecting behaviour of cattle and pigs. Livestock Science 202, 25–38 (2017).

  8. Lee, H.-J., Roberts, S. J., Drake, K. A. & Dawkins, M. S. Prediction of feather damage in laying hens using optical flows and markov models. J R Soc Interface 8, 489–499 (2011).

  9. Colles, F. M. et al. Monitoring chicken flock behaviour provides early warning of infection by human pathogen campylobacter. Proc

    Biol Sci 283 (2016).

  10. Leroy, T. et al. Eyenamic: Real-time measurement of pig activity in practical conditions. In Proceedings of The Fourth Workshop on Smart Sensors in Livestock Monitoring, 12–14 (2006).

  11. Youssef, A., Exadaktylos, V. & Berckmans, D. A. Towards real-time control of chicken activity in a ventilated chamber. Biosystems Engineering 135, 31–43 (2015).

  12. Cachat, J. et al. Three-dimensional neurophenotyping of adult zebrafish behavior. PLoS One 6, 1–14 (2011).

  13. Kröner, C. et al. 3d tracking of mosquitoes: A field compatible technique to understand malaria vector behaviour. In Imaging and Applied Optics 2016, TW5A.4 (Optical Society of America, 2016).

  14. Hong, W. et al. Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning. Proc Natl Acad Sci USA 112, E5351–E5360 (2015).

  15. Mittek, M., Psota, E. T., Pérez, L. C., Schmidt, T. &Mote, B. Health monitoring of group-housed pigs using depth-enabled multi-object tracking. In Proceedings of Int Conf Pattern Recognit, Workshop on Visual observation and analysis of Vertebrate And Insect Behavior (2016).

  16. Lao, F. et al. Automatic recognition of lactating sow behaviors through depth image processing. Computers and Electronics in Agriculture 125, 56–62 (2016).

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