Abstract
Lameness in dairy cattle has a negative effect on fertility, milk yield and various behaviors. Therefore, lameness in cattle causes significant economic losses in countries. In our article, it is aimed to detect lameness in cattle early with image processing techniques. Deep learning and image processing techniques were used in the article. In the article, YOLOv5 algorithm is used for object detection and Shufflenetv2k30 algorithm is used as image processing technology. Within the scope of the article, the images were subjected to a preprocessing (data augmentation) and then the cattle in the selected photos were identified by our trained deep learning model. The detected cattle were tagged and then the posture estimation of these tagged cattle was made. The angles between the joints of the cattle were found on the cattle whose posture pose was estimated. In the performance analysis, training was started with the weights of the Pre-training yolov5l model and the best weight output of the 200 epoch trained model was 75%. The best weight output of the model trained from zero to 400 epochs without using any model weights was 63%. Pre-training was started with the weights of the shufflenetv2k30 model and the weight output of the model trained for 400 epochs was 71%. This article will contribute to the studies to be done in the academic field and will create important data for the studies to be done in the livestock sector.
References
- [1] NAHMS. 1996. Dairy '96 Part I: Reference of 1996 Dairy Management Practices. USDA, Animal and Plant Health Inspection Service, National Animal Health Monitoring System. http://nahms.aphis.usda.gov/dairy/dairy96/DR96Pt1. pdf. (15 Mayıs 2007).
- [2] Enting, H., Kooij, D., Dijkhuizen, A.A., Huirne, R.B.M., Noordhuizen-Stassen, E.N. 1997. Economic losses due to clinical lameness in dairy cattle. Livest.Prod.Sci. 49:259-267
- [3] Clarkson, M.J., Downham, D.Y., Faull, W.B., Hughes, J.W., Manson, F.J., Merritt, J.B., Murray, R.D., Russell, W.B., Sutherst, J.E., Ward, W.R. 1996. Incidence and prevalence of lameness in dairy cattle. Vet. Rec. 138: 563-567.Yates, H. M., Evans, P., Sheel, D. W., (2013). The influence of F-doping in SnO2 thin films, Physics Procedia, 46, 19, 159–166.
- [4] Leech, F., Davis, B., M. E., Macrae, W. D. 1960. Disease, wastage and husbandry in the British dairy herd 1957–58. HMSO, London, United Kingdom. (Bach ve ark., 2007).
- [5] Gorgul, O.S. 2004. Foot diseases in dairy cattle. Sütaş, Dairy Livestock Training Center Publications, Livestock Series: 7. Breeder's handbook. Bursa. 60 pages.
- [6] Boelling, D., Pollott, G.E. 1998. Locomotion, lameness, hoof and leg traits in cattle. I. Phenotypic influences and relationships. Livest. Prod. Sci. 54: 193-203.
- [7] Kossaibati, M.A., Esslemont, R.J. 1997. The cost of production diseases in dairy herds in England. Vet. J. 154: 41-51.Antonaia, A., Menna, P., Addonizio, M. L., Crocchiolo, M., (1992). Transport properties of polycrystalline tin oxide films,” Solar Energy Materials and Solar Cells, 28, 167–173.
- [8] Flower, F.C., Sanderson, D.J., Weary, D.M. 2005. Hoof pathologies influence kinematic measures of dairy cow gait. J. Dairy Sci. 88:3166–3173.
- [9] Manson, J. F., Leaver, J.D. 1988. The influence of concentrate amount on locomotion and clinical lameness in dairy cattle. Brit. Soc. Anim. Prod. 47:185-190.
- [10] Flower, F.C., Weary, D.M. 2006. Effect of hoof pathologies on subjective assessments of dairy cow gait. J. DairySci. 89:139–146
- [11] Juarez, S.T., Robinson, P.H., DePeters, E.J., Price, E.O. 2003. Impact of lameness on behavior and productivity of lactating Holstein cows. Appl.Anim.Behav.Sci. 83:1-14.
- [12] Nordlung, K.V., Cook, N.B., Oetzel, G.R. 2004. Investigation strategies for laminitis problem herds. J. Dairy Sc. 87: (E.suppl):E27-E35 Pattern Analysis and Machine Intelligence, 39, 6, 1137-1149, (2017).
- [13] 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.
- [14] Buric, M., Pobar, M. ve Ivasic-Kos, M., Ball Detection Using Yolo and Mask RCNN,” 2018.
- [15] International Conference on Computational Science and Computational Intelligence (CSCI), 319–323, Nevada, (2018).
- [16] Chen, W., Huang, H., Peng, S., Zhou, C. ve Zhang, C., YOLO-face: a real-time face detector, The Visual Computer, (2020)
- [17] Du, J., Understanding of Object Detection Based on CNN Family and YOLO, Journal of Physics: 2nd International Conference on Machine Vision and Information Technology (CMVIT 2018),012029, Hong Kong, (2018).
Abstract
Süt sığırlarında topallık, fertilite, süt verimi ve çeşitli davranışlar üzerinde olumsuz etkiye sahiptir. Bu nedenle sığırlarda topallık, ülkelerde önemli ekonomik kayıplara neden olmaktadır. Makalemizde görüntü işleme teknikleri ile sığırlarda topallığın erken dönemde tespit edilmesi amaçlanmaktadır. Makalede derin öğrenme ve görüntü işleme teknikleri kullanılmıştır. Makalede nesne algılama için YOLOv5 algoritması, görüntü işleme teknolojisi olarak da Shufflenetv2k30 algoritması kullanılmıştır. Makale kapsamında görüntüler bir ön işleme (veri artırma) işlemine tabi tutulmuş ve daha sonra seçilen fotoğraflardaki sığırlar eğitimli derin öğrenme modelimiz tarafından tanımlanmıştır. Tespit edilen sığırlar etiketlendi ve daha sonra bu etiketlenen sığırların postür tahmini yapıldı. Postür duruşu tahmin edilen sığırlarda eklemler arasındaki açılar bulundu. Performans analizinde eğitime Pre-training yolov5l modelinin ağırlıkları ile başlanmış ve 200 epoch eğitimli modelin en iyi ağırlık çıkışı %75 olmuştur. Herhangi bir model ağırlığı kullanılmadan sıfırdan 400 çağa kadar eğitilen modelin en iyi ağırlık çıktısı %63 olmuştur. Ön eğitime shufflenetv2k30 modelinin ağırlıkları ile başlanmış ve 400 epoch için eğitilmiş modelin ağırlık çıkışı %71 olmuştur. Bu makale akademik alanda yapılacak çalışmalara katkı sağlayacak ve hayvancılık sektöründe yapılacak çalışmalara önemli veriler oluşturacaktır.
References
- [1] NAHMS. 1996. Dairy '96 Part I: Reference of 1996 Dairy Management Practices. USDA, Animal and Plant Health Inspection Service, National Animal Health Monitoring System. http://nahms.aphis.usda.gov/dairy/dairy96/DR96Pt1. pdf. (15 Mayıs 2007).
- [2] Enting, H., Kooij, D., Dijkhuizen, A.A., Huirne, R.B.M., Noordhuizen-Stassen, E.N. 1997. Economic losses due to clinical lameness in dairy cattle. Livest.Prod.Sci. 49:259-267
- [3] Clarkson, M.J., Downham, D.Y., Faull, W.B., Hughes, J.W., Manson, F.J., Merritt, J.B., Murray, R.D., Russell, W.B., Sutherst, J.E., Ward, W.R. 1996. Incidence and prevalence of lameness in dairy cattle. Vet. Rec. 138: 563-567.Yates, H. M., Evans, P., Sheel, D. W., (2013). The influence of F-doping in SnO2 thin films, Physics Procedia, 46, 19, 159–166.
- [4] Leech, F., Davis, B., M. E., Macrae, W. D. 1960. Disease, wastage and husbandry in the British dairy herd 1957–58. HMSO, London, United Kingdom. (Bach ve ark., 2007).
- [5] Gorgul, O.S. 2004. Foot diseases in dairy cattle. Sütaş, Dairy Livestock Training Center Publications, Livestock Series: 7. Breeder's handbook. Bursa. 60 pages.
- [6] Boelling, D., Pollott, G.E. 1998. Locomotion, lameness, hoof and leg traits in cattle. I. Phenotypic influences and relationships. Livest. Prod. Sci. 54: 193-203.
- [7] Kossaibati, M.A., Esslemont, R.J. 1997. The cost of production diseases in dairy herds in England. Vet. J. 154: 41-51.Antonaia, A., Menna, P., Addonizio, M. L., Crocchiolo, M., (1992). Transport properties of polycrystalline tin oxide films,” Solar Energy Materials and Solar Cells, 28, 167–173.
- [8] Flower, F.C., Sanderson, D.J., Weary, D.M. 2005. Hoof pathologies influence kinematic measures of dairy cow gait. J. Dairy Sci. 88:3166–3173.
- [9] Manson, J. F., Leaver, J.D. 1988. The influence of concentrate amount on locomotion and clinical lameness in dairy cattle. Brit. Soc. Anim. Prod. 47:185-190.
- [10] Flower, F.C., Weary, D.M. 2006. Effect of hoof pathologies on subjective assessments of dairy cow gait. J. DairySci. 89:139–146
- [11] Juarez, S.T., Robinson, P.H., DePeters, E.J., Price, E.O. 2003. Impact of lameness on behavior and productivity of lactating Holstein cows. Appl.Anim.Behav.Sci. 83:1-14.
- [12] Nordlung, K.V., Cook, N.B., Oetzel, G.R. 2004. Investigation strategies for laminitis problem herds. J. Dairy Sc. 87: (E.suppl):E27-E35 Pattern Analysis and Machine Intelligence, 39, 6, 1137-1149, (2017).
- [13] 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.
- [14] Buric, M., Pobar, M. ve Ivasic-Kos, M., Ball Detection Using Yolo and Mask RCNN,” 2018.
- [15] International Conference on Computational Science and Computational Intelligence (CSCI), 319–323, Nevada, (2018).
- [16] Chen, W., Huang, H., Peng, S., Zhou, C. ve Zhang, C., YOLO-face: a real-time face detector, The Visual Computer, (2020)
- [17] Du, J., Understanding of Object Detection Based on CNN Family and YOLO, Journal of Physics: 2nd International Conference on Machine Vision and Information Technology (CMVIT 2018),012029, Hong Kong, (2018).
Details
| Primary Language | English |
|---|---|
| Subjects | Decision Support and Group Support Systems, Information Systems (Other) |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | October 13, 2023 |
| Publication Date | October 13, 2023 |
| Acceptance Date | September 25, 2023 |
| Published in Issue | Year 2023 Volume: 5 Issue: 3 |
This work is licensed under a Creative Commons Attribution 4.0 International License