Scopus
YÖKSİS Eşleşti
Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows
Tropical Animal Health and Production · Şubat 2023
YÖKSİS Kayıtları
Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows
Springer Science and Business Media LLC · 2023 SCI-Expanded
DOÇENT ÖZCAN ŞAHİN →
Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows
Tropical Animal Health and Production · 2023 SCI-Expanded
DOÇENT ÖZCAN ŞAHİN →
Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows
Tropical Animal Health and Production · 2023 SCI-Expanded
DOÇENT ÖZCAN ŞAHİN →
Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows
Springer Science and Business Media LLC · 2023 SCI-Expanded
PROFESÖR İBRAHİM AYTEKİN →
Makale Bilgileri
DergiTropical Animal Health and Production
Yayın TarihiŞubat 2023
Cilt / Sayfa55
Scopus ID2-s2.0-85146926942
Özet
Lameness is one of the culling factors such as mastitis, low milk yield, and infertility that cause economic losses in herd management as they threaten animal health and welfare. The purpose of this study was to evaluate the early detection of lameness in Brown Swiss cattle by using a data mining algorithm by both integrating lameness scores and some image parameters such as Lab (CIE L*, a*, b*), HSB (hue, saturation, brightness), RGB (red, green, blue) by processing thermal images with ImageJ program. In the study, the variables obtained as a result of processing the skin surface temperatures and thermal images taken at the fetlock joint of 33 Brown Swiss cattle were used as independent variables. Also, healthy cows (lameness scores 1 and 2) and unhealthy cows (lameness scores 3, 4, and 5) used in the diagnosis of lameness were used as a binary response variable. Classification and regression tree (CART) was used as a data mining algorithm in the diagnosis of lameness. As a result, the CART algorithm correctly classified 12 of the 13 heads unhealthy cows according to locomotion scores. According to locomotion scores by using CART analysis in this study, independent variables that are used to classify healthy and unhealthy (lame) animals were determined as maximum temperature (Tmax), green (mean), L (max), and age (P<0.05). The cut-off values of these independent variables were predicted as 32.40, 149.14, 97.11, and 5.50 for Tmax, green (mean), L (max), and age, respectively. Also, the sensitivity, specificity, and area under the ROC curve (AUC) of the CART algorithm for locomotion scoring were found as 92.31%, 95%, and 93.7% respectively. The area under ROC curve (AUC) was found to be significant in the diagnosis of lameness (P<0.01). Results showed that the use of CART classification algorithm together with thermal camera and image processing methods is a usefull tool in the detection of lameness in the herds. It is recommended that more comprehensive studies by increasing the number of animals in the future would be more beneficial.
Yazarlar (5)
1
Gizem Coşkun
ORCID: 0000-0003-2519-7885
2
Ozcan Sahin
3
Rabia Albayrak Delialioǧlu
4
Altay Yasin
ORCID: 0000-0003-4049-8301
5
Ibrahim Aytekin
Anahtar Kelimeler
CART
Classification tree
Data mining
Image processing
Lameness
Thermal camera
Kurumlar
Ankara Üniversitesi
Ankara Turkey
Eskişehir Osmangazi Üniversitesi
Eskisehir Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
6
Atıf
5
Yazar
6
Anahtar Kelime