EN
Classification of digital dermatitis with image processing and machine learning methods
Öz
In this study, it was aimed to perform the detection and grading of Digital Dermatitis (DD) disease, which is common in dairy cattle and causes serious economic losses, using artificial intelligence techniques in a computer environment with high accuracy without the need for any expert intervention.
Within the scope of the study, because of the examinations performed on 168 cows of Holstein breed, aged 4-7 years, whose lameness was detected in dairy farms located in the center and districts of Burdur region, pictures of lesions due to DD were taken, and 4 groups were formed according to the degree of size. The photographs obtained were first labelled according to the degree of disease by a faculty member specialized in podiatry. Afterwards, the tagged photographs were reproduced using artificial intelligence image augmentation techniques, and a sample of 1,000 datasets was carried out for each disease degree. The photographs that make up the dataset were processed using the inception v3 deep learning algorithm and more than 2,000 numerical features were extracted. Then, machine learning models were developed using 6 different machine learning algorithms to classify these features. The results obtained were examined in detail with the help of tables and graphics, and it showed that the developed artificial intelligence models could be used in the classification of DD case photos with a cumulative accuracy value above 0.87.
Anahtar Kelimeler
Destekleyen Kurum
Burdur Mehmet Akif Ersoy Üniversity
Proje Numarası
5
Teşekkür
This study was supported within the scope of the "Diagnosis and treatment of foot diseases in dairy cattle" project, which is the 5th subproject of the main project titled "Increasing the Sectoral Competitiveness of the Province of Burdur: Integrated Development by Differentiating in Agriculture" under the coordination of Burdur Mehmet Akif Ersoy University, Agriculture and Livestock Development Project Coordinator.
Kaynakça
- Bassett, D.R., Toth, L.P., LaMunion, S.R., Crouter, S.E. (2017). Step Counting: A Review of Measurement Considerations and Health-Related Applications. Sports Med. 47, 1303–1315. https://doi.org/10.1007/s40279-016-0663-1
- Biemans, F., Bijma, P., Boots, N., De Jong, M. (2017). Digital Dermatitis in dairy cattle: The contribution of different disease classes to transmission. Epidemics 23. https://doi.org/10.1016/j.epidem.2017.12.007
- Bruijnis, M.R.N., Beerda, B., Hogeveen, H., Stassen, E.N. (2012). Assessing the welfare impact of foot disorders in dairy cattle by a modeling approach. Animal 6, 962–970. https://doi.org/10.1017/S1751731111002606
- Cha, E., Hertl, J.A., Bar, D., Gröhn, Y.T. (2010). The cost of different types of lameness in dairy cows calculated by dynamic programming. Prev. Vet. Med. 97, 1–8. https://doi.org/10.1016/j.prevetmed.2010.07.011
- Clegg, S.R., Mansfield, K.G., Newbrook, K., Sullivan, L.E., Blowey, R.W., Carter, S.D., Evans, N.J. (2015). Isolation of digital dermatitis treponemes from hoof lesions in Wild North American Elk (Cervus elaphus) in Washington State, USA. J. Clin. Microbiol. 53, 88–94. https://doi.org/10.1128/JCM.02276-14
- Demirkan, I., Murray, R., Carter, S. (2000). Skin diseases of the bovine digit associated with lameness. Vet Bull 70, 149–171.
- Döpfer, D., Koopmans, A., Meijer, F.A., Szakáll, I., Schukken, Y.H., Klee, W., Bosma, R.B., Cornelisse, J.L., van Asten, A.J., ter Huurne, A.A. (1997). Histological and bacteriological evaluation of digital dermatitis in cattle, with special reference to spirochaetes and Campylobacter faecalis. Vet. Rec. 140, 620–623. https://doi.org/10.1136/vr.140.24.620
- Garbarino, E.J., Hernandez, J.A., Shearer, J.K., Risco, C.A., Thatcher, W.W. (2004). Effect of lameness on ovarian activity in postpartum holstein cows. J. Dairy Sci. 87, 4123–4131. https://doi.org/10.3168/jds.S0022-0302(04)73555-9
Ayrıntılar
Birincil Dil
İngilizce
Konular
Sağlık Kurumları Yönetimi
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
31 Aralık 2022
Gönderilme Tarihi
20 Haziran 2022
Kabul Tarihi
27 Ekim 2022
Yayımlandığı Sayı
Yıl 2022 Cilt: 7 Sayı: 3
APA
Yiğitarslan, K., & Kırbaş, İ. (2022). Classification of digital dermatitis with image processing and machine learning methods. Veterinary Journal of Mehmet Akif Ersoy University, 7(3), 195-200. https://doi.org/10.24880/maeuvfd.1133145
AMA
1.Yiğitarslan K, Kırbaş İ. Classification of digital dermatitis with image processing and machine learning methods. Veterinary Journal of Mehmet Akif Ersoy University. 2022;7(3):195-200. doi:10.24880/maeuvfd.1133145
Chicago
Yiğitarslan, Kürşad, ve İsmail Kırbaş. 2022. “Classification of digital dermatitis with image processing and machine learning methods”. Veterinary Journal of Mehmet Akif Ersoy University 7 (3): 195-200. https://doi.org/10.24880/maeuvfd.1133145.
EndNote
Yiğitarslan K, Kırbaş İ (01 Aralık 2022) Classification of digital dermatitis with image processing and machine learning methods. Veterinary Journal of Mehmet Akif Ersoy University 7 3 195–200.
IEEE
[1]K. Yiğitarslan ve İ. Kırbaş, “Classification of digital dermatitis with image processing and machine learning methods”, Veterinary Journal of Mehmet Akif Ersoy University, c. 7, sy 3, ss. 195–200, Ara. 2022, doi: 10.24880/maeuvfd.1133145.
ISNAD
Yiğitarslan, Kürşad - Kırbaş, İsmail. “Classification of digital dermatitis with image processing and machine learning methods”. Veterinary Journal of Mehmet Akif Ersoy University 7/3 (01 Aralık 2022): 195-200. https://doi.org/10.24880/maeuvfd.1133145.
JAMA
1.Yiğitarslan K, Kırbaş İ. Classification of digital dermatitis with image processing and machine learning methods. Veterinary Journal of Mehmet Akif Ersoy University. 2022;7:195–200.
MLA
Yiğitarslan, Kürşad, ve İsmail Kırbaş. “Classification of digital dermatitis with image processing and machine learning methods”. Veterinary Journal of Mehmet Akif Ersoy University, c. 7, sy 3, Aralık 2022, ss. 195-00, doi:10.24880/maeuvfd.1133145.
Vancouver
1.Kürşad Yiğitarslan, İsmail Kırbaş. Classification of digital dermatitis with image processing and machine learning methods. Veterinary Journal of Mehmet Akif Ersoy University. 01 Aralık 2022;7(3):195-200. doi:10.24880/maeuvfd.1133145