Abstract
Hayvancılık üretimi, et ve süt ürünlerine artan talep ile birlikte sürekli olarak artan dünya nüfusuna gıda sağlamak için küresel ölçekte, sığır işletmelerin kapasitelerinde hızlı bir büyümeye yol açmıştır. Bununla birlikte, tüketiciler, çiftlik hayvanlarının sağlığı ve refahı ve çiftliğin çevre koşulları hakkında ciddi derecede ilgi duymaktadırlar. Hayvan ihtiyaçlarının farkındalığı, hayvan sağlığı ve refahı için yeni üretim standartlarının temelini oluşturmaktadır. Sığır davranışları, ahır çevre durumu, gıda ve su yeterliliği, sağlık, refah ve üretim verimliliği hakkında bilgi sağlayabilir. Sığır davranışlarının gerçek zamanlı olarak belirlenmesi oldukça zordur, ancak teknolojinin artan kullanılabilirliği ve kabiliyeti, hayvan davranışlarının otomatik olarak izlenmesini pratik hale getirmektedir. Yeni teknolojiler olarak görüntü işleme teknikleri, hayvan davranışı izleme gereksinimlerini elde etmek için otomatik, temassız, stressiz ve uygun maliyetli bir yol sağlayabilir. 3D görüntüleme sistemleri ve hayvan davranışlarını etkin bir şekilde tanımlamak için daha fazla kullanılmış olan 2D kameralar ile geliştirilen sistemlerin performansı analiz edilerek son teknoloji sistemlerin büyükbaşların beslenme, su içme, yatma, hareket etme, agresiflik düzeyi ve üreme davranışları bakımından değerlendirilmiştir. Hayvanların sağlık ve refah durumlarıyla birebir ilişkili olan bu davranışların erken tespiti veterinerler ve çiftçiler bakımından oldukça önemli kazançların elde edilmesini sağlamaktadır. Bu teknolojiler, özellikle büyük ölçekli işletmelerde hayvanları 7/24 izleyerek anormal davranışları ve sağlık sorunlarını erken tespit edip çiftçiye zamanında müdahale şansı tanıyarak destek olabilir. Bu sayede hem hastalıkların yayılımı engellenmiş olacak hem de hastalıklarla mücadele maliyetleri azaltılmış olacaktır.
References
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- Botreau, R., Veissier, I., Butterworth, A., Bracke, M.B.M., Keeling, L.J., 2007. Definition of criteria for overall assessment of animal welfare. Anim. Welf. 16 (2), 225–228.
- Bracke, M.B.M., Spoolder, H.A.M., 2011. Review of wallowing in pigs: implications for animal welfare. Anim. Welf. 20 (3), 347–363.
- Broom, D.M., 2006. Behaviour and welfare in relation to pathology. Appl. Anim. Behav. Sci. 97 (1), 73–83.
- Brown-Brandl, T.M., Eigenberg, R.A., Purswell, J.L., 2013a. Using thermal imaging as a method of investigating thermal thresholds in finishing pigs. Biosyst. Eng. 114 (3), 327–333.
- Brown-Brandl, T.M., Rohrer, G.A., Eigenberg, R.A., 2013b. Analysis of feeding behavior of group housed growing–finishing pigs. Comput. Electron. Agric. 96, 246–252.
- Bruinsma, J., 2003. World Agriculture: Towards 2015/2030: An FAO Perspective. Earthscan, London, pp. 2030.
- Bull, C.R., McFarlane, N.J.B., Zwiggelaar, R., Allen, C.J., Mottram, T.T., 1996. Inspection of teats by colour image analysis for automatic milking systems. Comput. Electron. Agric. 15 (1), 15–26.
- Caja, G., Castro-Costa, A., Knight, C.H., 2016. Engineering to support wellbeing of dairy animals. J. Dairy Res. 83 (2), 136–147.
- Cangar, Ö., Leroy, T., Guarino, M., Vranken, E., Fallon, R., Lenehan, J., Mee, J., Berckmans, D., 2008. Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis. Comput. Electron. Agric. 64 (1), 53–60.
- Chapinal, N., Veira, D.M., Weary, D.M., Von Keyserlingk, M.A.G., 2007. Technical note: validation of a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. J. Dairy Sci. 90 (12), 5732–5736.
- Conte, S., Bergeron, R., Gonyou, H., Brown, J., Rioja-Lang, F.C., Connor, L., Devillers, N., 2014. Measure and characterization of lameness in gestating sows using force plate, kinematic, and accelerometer methods. J. Anim. Sci. 92 (12), 5693–5703.
- Cortivo, P.D., Dias, E., Barcellos, J.O.J., Peripolli, V., Costa Jr, J.B.G., Dallago, B.S.L., McManus, C.M., 2016. Use of thermographic images to detect external parasite load in cattle. Comput. Electron. Agric. 127, 413–417.
Abstract
Livestock production, together with raising demand for dairy foods, has entail a fast growing in the capacities of cow holdings on a global scale to provide food for the ever-increasing world population. However, consumers are seriously interested with the farm animals’ health and welfare and the environmental conditions of the barn. Awareness of farm animals need forms the basis of new standards for health, welfare and production of animals. It can provide information on cattle behaviour, barn living conditions, feeding and drinking adequacy, production, health and welfare. Determining cattle behaviour in real time is quite difficult, but the increased availability and capability of the technology is making automatic monitoring practical for animal behaviour. As new technologies, image processing techniques may obtain an automatic, contactless, stress-free and cheap ways to ensure monitoring requirements in animal behaviour. The performance of the systems developed with 3D monitoring systems and 2D cameras, which have been used more and more to effectively describe animal behaviour, is reviewed by analysing, and the state-of-the-art systems are designed to help cattle feed, drink, lie, move, evaluated in terms of aggressiveness level and reproductive behaviour. Early detection of these behaviours, which are directly related to the health and welfare of animals, provides very important gains for veterinarians and farmers. These technologies may assist the farmer by tracing animals 24/7, detecting abnormal behaviour and health problems early, and giving the farmer a chance to intervene in a timely manner, especially in large-scale farms. In this way, both the spread of diseases will be prevented and the costs of fighting against diseases will be reduced.
References
- Abdul Jabbar, K., Hansen, M.F., Smith, M.L., 2017. Early and non-intrusive lameness detection in dairy cows using 3-dimensional video. Biosyst. Eng. 153, 63–69.
- Anglart, D., 2016. Automatic Estimation of Body Weight and Body Condition Score in Dairy Cows Using 3D imaging technique. Second cycle A2E SLU, Dept. of Animal Nutrition and Management, Uppsala.
- Appuhamy, J.A.D.R.N., Judy, J.V., Kebreab, E., Kononoff, P.J., 2016. Prediction of drinking water intake by dairy cows. J. Dairy Sci. 99 (9), 7191–7205.
- Averós, X., Brossard, L., Dourmad, J.Y., de Greef, K.H., Edge, H.L., Edwards, S.A., Meunier-Salaün, M.C., 2010. A meta-analysis of the combined effect of housing and environmental enrichment characteristics on the behaviour and performance of pigs. Appl. Anim. Behav. Sci. 127 (3–4), 73–85.
- Azizi, O., Kaufmann, O., Hasselmann, L., 2009. Relationship between feeding behaviour and feed intake of dairy cows depending on their parity and milk yield. Livest. Sci. 122 (2), 156–161.
- Azzaro, G., Caccamo, M., Ferguson, J.D., Battiato, S., Farinella, G.M., Guarnera, G.C., Puglisi, G., Petriglieri, R., Licitra, G., 2011. Objective estimation of body condition score by modelling cow body shape from digital images. J. Dairy Sci. 94 (4), 2126–2137.
- Bach, A., Iglesias, C., Busto, I., 2004. Technical note: a computerized system for monitoring feeding behavior and individual feed intake of dairy cattle. J. Dairy Sci. 87 (12), 4207–4209.
- Barkema, H.W., von Keyserlingk, M.A.G., Kastelic, J.P., Lam, T.J.G.M., Luby, C., Roy, J.P., LeBlanc, S.J., Keefe, G.P., Kelton, D.F., 2015. Invited review: changes in the dairy industry affecting dairy cattle health and welfare. J. Dairy Sci. 98 (11), 7426–7445.
- Bercovich, A., Edan, Y., Alchanatis, V., Moallem, U., Parmet, Y., Honig, H., Maltz, E., Antler, A., Halachmi, I., 2013. Development of an automatic cow body condition scoring using body shape signature and Fourier descriptors. J. Dairy Sci. 96 (12), 8047–8059.
- Bewley, J.M., Boyce, R.E., Hockin, J., Munksgaard, L., Eicher, S.D., Einstein, M.E., Schutz, M.M., 2010. Influence of milk yield, stage of lactation, and body condition on dairy cattle lying behaviour measured using an automated activity monitoring sensor. J. Dairy Res. 77 (01), 1–6.
- Bewley, J.M., Peacock, A.M., Lewis, O., Boyce, R.E., Roberts, D.J., Coffey, M.P., Kenyon, S.J., Schutz, M.M., 2008. Potential for estimation of body condition scores in dairy cattle from digital images. J. Dairy Sci. 91 (9), 3439–3453.
- Botreau, R., Veissier, I., Butterworth, A., Bracke, M.B.M., Keeling, L.J., 2007. Definition of criteria for overall assessment of animal welfare. Anim. Welf. 16 (2), 225–228.
- Bracke, M.B.M., Spoolder, H.A.M., 2011. Review of wallowing in pigs: implications for animal welfare. Anim. Welf. 20 (3), 347–363.
- Broom, D.M., 2006. Behaviour and welfare in relation to pathology. Appl. Anim. Behav. Sci. 97 (1), 73–83.
- Brown-Brandl, T.M., Eigenberg, R.A., Purswell, J.L., 2013a. Using thermal imaging as a method of investigating thermal thresholds in finishing pigs. Biosyst. Eng. 114 (3), 327–333.
- Brown-Brandl, T.M., Rohrer, G.A., Eigenberg, R.A., 2013b. Analysis of feeding behavior of group housed growing–finishing pigs. Comput. Electron. Agric. 96, 246–252.
- Bruinsma, J., 2003. World Agriculture: Towards 2015/2030: An FAO Perspective. Earthscan, London, pp. 2030.
- Bull, C.R., McFarlane, N.J.B., Zwiggelaar, R., Allen, C.J., Mottram, T.T., 1996. Inspection of teats by colour image analysis for automatic milking systems. Comput. Electron. Agric. 15 (1), 15–26.
- Caja, G., Castro-Costa, A., Knight, C.H., 2016. Engineering to support wellbeing of dairy animals. J. Dairy Res. 83 (2), 136–147.
- Cangar, Ö., Leroy, T., Guarino, M., Vranken, E., Fallon, R., Lenehan, J., Mee, J., Berckmans, D., 2008. Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis. Comput. Electron. Agric. 64 (1), 53–60.
- Chapinal, N., Veira, D.M., Weary, D.M., Von Keyserlingk, M.A.G., 2007. Technical note: validation of a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. J. Dairy Sci. 90 (12), 5732–5736.
- Conte, S., Bergeron, R., Gonyou, H., Brown, J., Rioja-Lang, F.C., Connor, L., Devillers, N., 2014. Measure and characterization of lameness in gestating sows using force plate, kinematic, and accelerometer methods. J. Anim. Sci. 92 (12), 5693–5703.
- Cortivo, P.D., Dias, E., Barcellos, J.O.J., Peripolli, V., Costa Jr, J.B.G., Dallago, B.S.L., McManus, C.M., 2016. Use of thermographic images to detect external parasite load in cattle. Comput. Electron. Agric. 127, 413–417.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Horticultural Production |
| Journal Section | Reviews |
| Authors | |
| Publication Date | December 31, 2021 |
| Published in Issue | Year 2021 Volume: 2 Issue: 4 |
Lapseki MYO Uygulamalı Araştırmalar Dergisi ücretsizdir. Yayınlanacak makaleler için herhangi bir ücret talep edilmez