Journal of Dairy Science ( IF 3.5 ) Pub Date : 2018-04-05 Andrés Schlageter-Tello, Tom Van Hertem, Eddie A.M. Bokkers, Stefano Viazzi, Claudia Bahr, Kees Lokhorst
The objective of this study was to determine if a 3-dimensional computer vision automatic locomotion scoring (3D-ALS) method was able to outperform human observers for classifying cows as lame or nonlame and for detecting cows affected and nonaffected by specific type(s) of hoof lesion. Data collection was carried out in 2 experimental sessions (5 mo apart). In every session all cows were assessed for (1) locomotion by 2 observers (Obs1 and Obs2) and by a 3D-ALS; and (2) identification of different types of hoof lesions during hoof trimming (i.e., skin and horn lesions and combinations of skin/horn lesions and skin/hyperplasia). Performances of observers and 3D-ALS for classifying cows as lame or nonlame and for detecting cows affected or nonaffected by types of lesion were estimated using the percentage of agreement (PA), kappa coefficient (κ), sensitivity (SEN), and specificity (SPE). Observers and 3D-ALS showed similar SENlame values for classifying lame cows as lame (SENlame comparison Obs1-Obs2 = 74.2%; comparison observers-3D-ALS = 73.9–71.8%). Specificity values for classifying nonlame cows as nonlame were lower for 3D-ALS when compared with observers (SPEnonlame comparison Obs1-Obs2 = 88.5%; comparison observers-3D-ALS = 65.3–67.8%). Accordingly, overall performance of 3D-ALS for classifying cows as lame and nonlame was lower than observers (Obs1-Obs2 comparison PAlame/nonlame = 84.2% and κlame/nonlame = 0.63; observers-3D-ALS comparisons PAlame/nonlame = 67.7–69.2% and κlame/nonlame = 0.33–0.36). Similarly, observers and 3D-ALS had comparable and moderate SENlesion values for detecting horn (SENlesion Obs1 = 68.6%; Obs2 = 71.4%; 3D-ALS = 75.0%) and combinations of skin/horn lesions (SENlesion Obs1 = 51.1%; Obs2 = 64.5%; 3D-ALS = 53.3%). The SPEnonlesion values for detecting cows without lesions when classified as nonlame were lower for 3D-ALS than for observers (SPEnonlesion Obs1 = 83.9%; Obs2 = 80.2%; 3D-ALS = 60.2%). This was translated into a poor overall performance of 3D-ALS for detecting cows affected and nonaffected by horn lesions (PAlesion/nonlesion Obs1 = 80.6%; Obs2 = 78.3%; 3D-ALS = 63.5% and κlesion/nonlesion Obs1 = 0.48; Obs2 = 0.44; 3D-ALS = 0.25) and skin/horn lesions (PAlesion/nonlesion Obs1 = 75.1%; Obs2 = 75.9%; 3D-ALS = 58.6% and κlesion/nonlesion Obs1 = 0.35; Obs2 = 0.42; 3D-ALS = 0.10), when compared with observers. Performance of observers and 3D-ALS for detecting skin lesions was poor (SENlesion for Obs1, Obs2, and 3D-ALS <40%). Comparable SENlame and SENlesion values for observers and 3D-ALS are explained by an overestimation of lameness by 3D-ALS when compared with observers. Thus, comparable SENlame and SENlesion were reached at the expense high number of false positives and low SPEnonlame and SPEnonlesion. Considering that observers and 3D-ALS showed similar performance for classifying cows as lame and for detecting horn and combinations of skin/horn lesions, the 3D-ALS could be a useful tool for supporting dairy farmers in their hoof health management.
中文翻译:
人类观察者的性能和基于3维计算机视觉的自动运动评分方法来检测奶牛的me行和蹄部病变
这项研究的目的是确定3维计算机视觉自动运动评分(3D-ALS)方法是否能够胜过人类观察者的分类,将母牛分为as脚或非non脚,以及检测受特定类型影响和不受影响的母牛蹄病变。在2个实验阶段(相隔5个月)中进行了数据收集。在每个环节中,对所有母牛进行(1)2位观察员(Obs1和Obs2)和3D-ALS的运动评估;(2)在修蹄过程中识别不同类型的蹄部病变(即,皮肤和角部病变以及皮肤/角部病变和皮肤/增生的组合)。使用一致性百分比(PA),kappa系数(κ),敏感性(SEN)和特异性(SPE)。观察者和3D-ALS表现出相似的SEN跛为跛脚奶牛分类为跛值(SEN跛比较OBS1-OBS2 = 74.2%;比较观察员-3D-ALS = 73.9-71.8%)。与观察者相比,将3D-ALS分类为非lame母牛的特异性值较低(SPE nonlame比较Obs1-Obs2 = 88.5%;比较观察者3D-ALS = 65.3–67.8%)。因此,将3D-ALS分类为la足和非lam足的奶牛的整体性能要低于观察者(Obs1-Obs2比较PA me足/非non足= 84.2%,κme足/非lam足= 0.63;观察者3D-ALS比较PA me足/非non足= 67.7–69.2%,κme脚/非lam脚= 0.33–0.36)。同样,观察者和3D-ALS也有中等程度的SEN病变检测角的值(SEN病变Obs1 = 68.6%; Obs2 = 71.4%; 3D-ALS = 75.0%)以及皮肤/角病变的组合(SEN病变Obs1 = 51.1%; Obs2 = 64.5%; 3D-ALS = 53.3% )。在SPE nonlesion用于检测奶牛而不时列为nonlame病变值分别为3D-ALS比观察员(SPE下nonlesion OBS1 = 83.9%; OBS2 = 80.2%; 3D-ALS = 60.2%)。这导致3D-ALS在检测受牛角病变影响和未受其影响的母牛方面总体表现不佳(PA病变/非病变Obs1 = 80.6%; Obs2 = 78.3%; 3D-ALS = 63.5%和κ病变/非病变Obs1 = 0.48 ; Obs2 = 0.44; 3D-ALS = 0.25)和皮肤/角病变(PA病变/非病变)Obs1 = 75.1%;Obs2 = 75.9%;3D-ALS = 58.6%,κ病变/非病变Obs1 = 0.35;Obs2 = 0.42;与观察者比较时,3D-ALS = 0.10)。观察者和3D-ALS检测皮肤病变的性能很差(Obs1,Obs2和3D-ALS的SEN病变<40%)。与观察者相比,观察者和3D-ALS的la足和SEN病变值具有可比性,这是由于3D-ALS对la足的高估所致。因此,可比SEN跛脚和SEN病变牺牲大量的误报和低SPE均达到nonlame和SPE nonlesion。考虑到观察者和3D-ALS在将母牛分类为la足,检测牛角以及皮肤/牛角病变的组合方面表现出相似的性能,因此3D-ALS可能是支持奶农对其蹄进行健康管理的有用工具。