Detection and Analysis of Lameness in Dairy Cows Through Computer Vision Techniques
Creators: Hamzehnezhadabkenari, Seyedemran
Publisher: name: University of Alberta Library
Year: 2024

<p>This thesis introduces a novel method for detecting lameness and identifying lame legs in dairy cows. The study is driven by the potential of using lameness indicators and gait variables, observed as the animals walk along a corridor, to distinguish between healthy and lame cows. The analysis incorporates spatial-temporal features, including trackway, walking speed, walking acceleration, and changes in head position. Gait symmetry and consistency between healthy and lame groups are thoroughly investigated. Statistical measurements and performance metrics reveal a strong correlation (p&lt;0.05) between lameness and these kinematic variables, highlighting the effectiveness of the proposed detection method.<br>
The gait characteristics are recorded from a side view through 2D videos at full-HD resolution, using a stationary camera setup that captures the whole body&#39;s movements including hooves, legs, head, and back. Using pose estimation techniques with ten anatomical keypoints, their movement patterns are extracted in Eulerian coordinates. Over 100 videos of both healthy and lame cows were recorded and processed to align with the research objectives. The selected data was then <br>
utilized to develop a lameness detection model employing digital signal processing techniques. As the ground truth for data analysis, seven observers conducted a binary assessment of the selected data.<br>
Trackway analysis led to the detection of lameness by considering the landing locations of different hooves. This method showed a high correlation in identifying asymmetry and inconsistency in movement when comparing trackways on both groups. The range of trackways between healthy and lame cows was also examined, with a detailed analysis of the variability.<br>
The evaluation of walking speed data demonstrated a high potential for detecting the lame side, where the lame leg is present. Additionally, the analysis of walking acceleration showed significant potential for identifying the lame leg. The results from the walking speed data confirmed its effectiveness in assessing asymmetry and inconsistency in movement. Based on the investigation of walking speed, acceleration, and binary assessments, lameness was highly prevalent in the rear <br>
legs among the studied animals.<br>
Head position showed a high correlation with lameness classification accuracy of 85%. Lame cows exhibited a lower position of their head and more variable results, often keeping their heads closer to the ground. <br>
Through several image processing steps, signals in Eulerian coordinates were transformed into Lagrangian coordinates, enabling further assessment of the subject and overcoming limitations such as camera placement, camera shaking, and its movement. The validation of signal extraction from this image processing approach was evaluated using a simulated cow movement video and analytical equations applied to the video data.<br>
A comprehensive evaluation of all lameness indicators utilized in the research facilitated the development of a reliable method for detecting lameness and identifying lame legs using 2D cameras. This method aims to enable prompt reactions and enhance animal welfare and overall sustainable dairy farming.</p>