Performance of human observers and an automatic 3-dimensional computer-vision-based locomotion scoring method to detect lameness and hoof lesions in dairy cows

Abstract

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 SEN values for classifying lame cows as lame (SEN 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 (SPE 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 PA = 84.2% and κ = 0.63; observers-3D-ALS comparisons PA = 67.7-69.2% and κ = 0.33-0.36). Similarly, observers and 3D-ALS had comparable and moderate SEN values for detecting horn (SEN Obs1 = 68.6%; Obs2 = 71.4%; 3D-ALS = 75.0%) and combinations of skin/horn lesions (SEN Obs1 = 51.1%; Obs2 = 64.5%; 3D-ALS = 53.3%). The SPE values for detecting cows without lesions when classified as nonlame were lower for 3D-ALS than for observers (SPE 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 (PA Obs1 = 80.6%; Obs2 = 78.3%; 3D-ALS = 63.5% and κ Obs1 = 0.48; Obs2 = 0.44; 3D-ALS = 0.25) and skin/horn lesions (PA Obs1 = 75.1%; Obs2 = 75.9%; 3D-ALS = 58.6% and κ 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 (SEN for Obs1, Obs2, and 3D-ALS <40%). Comparable SEN and SEN values for observers and 3D-ALS are explained by an overestimation of lameness by 3D-ALS when compared with observers. Thus, comparable SEN and SEN were reached at the expense high number of false positives and low SPE and SPE. 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.

Reliability of a beef cattle locomotion scoring system for use in clinical practice

“…For this reason, all values have been provided so that readers can interpret as required. However, in the authors’ opinion, the intra‐observer and inter‐observer agreement across the 40 video clips is considered acceptable when compared with similar studies in the literature 5 16 21–23…”

Grazing Cow Behavior and Lameness

“…Other studies have used the mean of multiple scorers' assessments, worked to improve alignment between scorers [28,29], recorded video of the cow walking for thorough observation with the benefit of replay and playback speed adjustment and have performed hoof inspections [30]. Future research should consider using multiple scorers, video and including severely lame cows in preference to the single highly trained and skilled manual mobility scorer approach used here.…”

A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications

“…According to the different levels of lameness with different back arch curvatures, the thresholds of the back arch curvatures were set. However, because cows are complex individuals and time-varying living organisms [49,74,75], the variables used for detecting lameness are limited by the substantial differences between individual cows [76], and such differences must be considered by an effective automatic lameness detection system [53]. Previous studies defined thresholds and standards for lameness detection for different groups of cows but did not focus on individual differences [49,53,70].…”