Table 2.
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.