Table 2.

Detection method of cow lameness based on 3D and thermal infrared cameras.

Source	Camera	Test Environment	Objective of the Study	Characteristic	Research Method	Algorithm Used	Result
Nikkhah et al. [80]	FLIR Inframetric 760, Boston, MA	Within the barn	Explore the relationship between hoof temperature and hoof health of cows	Temperatures of cow hooves	Chi-square analysis	/	Using infrared thermography (IRT) to measure skin temperature may reveal inflammation associated with laminitis in the early/middle stage
Alsaaod and Büscher [81]	Longwave thermal camera	Milking parlor	Investigate IRT as a noninvasive diagnostic tool for early detection of foot pathologies in dairy cows	Temperatures of cows’ hooves	Analysis of temperature difference between healthy and diseased hooves	Threshold classification	The sensitivity of thermal infrared imaging to detect hoof damage was greater than 80%
Stokes et al. [82]	/	Milking parlor	Examine the potential of IRT as a noninvasive tool for rapidly screening for the presence of digital dermatitis	Temperatures of cow hooves	Comparison of temperature changes in cow hooves caused by different hoof diseases	Threshold classification	Damage to hooves and skin causes a rise in peak skin temperature
Alsaaod et al. [83]	Ti25 Thermal Imager	In a closed, indoor environment	Evaluate IRT as a tool for the detection of digital dermatitis lesions and to determine an optimal temperature cut-off value	Temperatures of cow hooves	The two highest temperatures were used to evaluate disease in hind feet and hooves	Threshold classification	The sensitivity of hind foot disease detection was 89.1%, and the specificity was 66.6%
Viazzi et al. [79]	3D Kinect camera 2D Nikon D7000 camera	The alley after a sorting gate	Evaluate the use of a 3D camera from the top view to improve the back-posture extraction and to compare it with the 2D camera	Back arch	Decision tree	BMP detection algorithm, 3D back posture calculation algorithm	A 3D camera method is suitable for an automatic lameness detection system
Van Hertem et al. [84]	Microsoft Kinect Xbox 3D- camera	After-milking sorting gate	Optimize the classification output of a computer vision-based algorithm for automated lameness scoring	Back arch	Classification models such as logistic regression of ordered polynomials	BMP detection algorithm	Continuous measurements of cow lameness can improve the classification ability of a computer vision system
Jabbar et al. [85]	/	A custom race next to the milking parlor	Examine the ability of the spine arch analysis method to detect early-stage lameness	Spinal posture and gait	Image processing, data feature analysis	Threshold classification	Accuracy of lameness detection was 95.7%
Van Hertem et al. [86]	Kinect	Corridor	Evaluate whether a multi-sensor system was a better classifier for lameness than the single-sensor-based detection models	Back arch and speed	Comparison between single predictor and multivariate analysis	Binary logistic regression	Gait and posture measurement systems based on video are superior to the behavior and performance sensing technique for lameness detection
Harris-Bridge et al. [87]	FLIR SC620 camera	claw trimming crush	Determine whether the temperature data were more effective and accurate in detecting lameness	Temperature of cow hooves	Scatter plots and Pearson’s Product Moment correlations	Parametric statistical, linear model, maximum temperature detection	The highest temperature is the most accurate measurement method
Hansen et al. [88]	3D Kinect-like depth camera	a narrow walkway beneath	Explore a methodology for simultaneously monitoring multiple animal health parameters	Curvature of the spinal column	Image processing, spatial analysis	curvature of the spine threshold classification	Accuracy of lameness detection was 83% 2

² The “/” means there was no discussion of the factor in the article.