Lameness Detection via Back Line Curvature Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting signs of paralysis in dairy cows, such as those caused by claw diseases, are inefficient and time-consuming, especially in larger herds, due to the need for extensive data collection and personnel, and are affected by movement and environmental factors, leading to unreliable results.
Innovation Solution
A device using a commercially available image processing system to analyze side-view photographs of cows in a stationary position, creating a binary difference image to measure and log the curvature of the cow's back line, allowing for early detection of lameness and prompt treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If force sensors are installed on the floor to detect weight distribution, then lameness can be detected through statistical analysis, but a large number of passages must be recorded over a long period which reduces productivity
Solution Approach 1:
The patent replaces the mechanical force sensor system with an optical imaging system. A camera captures images of the cow's back, and image processing algorithms extract the back line to detect curvature changes. This substitution eliminates the need for lengthy statistical analysis of weight distribution while achieving comparable or superior detection accuracy through direct visual measurement of spinal curvature.
2Measurement precision
If cameras are used to capture cow legs and back, then lameness signs can be detected, but movement and environmental factors affect image quality and reliability
Solution Approach 1:
The system captures images at a specific moment when the cow is standing still or moving minimally, before movement artifacts can significantly degrade image quality. The back line extraction algorithm is applied to these preliminary images to detect curvature, ensuring measurements are taken under optimal conditions rather than attempting to correct for movement after the fact.
Solution Approach 2:
The patent extracts only the essential feature (the back line) from the complete image, separating it from distracting elements such as shadows, spots on the cow's coat, and background variations. By focusing solely on the back line coordinates, the system eliminates the influence of environmental factors and cow-specific patterns that would otherwise compromise measurement reliability.
3Measurement precision
If manual observation of cow gait is performed, then early detection of paralysis is possible, but time-consuming personnel requirements increase device complexity
Solution Approach 1:
The system enables self-service detection by automating the entire lameness detection process. The camera automatically captures images, the image processing algorithm automatically extracts the back line, and the curvature is automatically calculated and compared against reference values. This eliminates the need for trained personnel to manually observe and evaluate cow gait, transferring the detection function to an autonomous automated system.
Solution Approach 2:
The patent replaces the human observational system with an automated optical measurement system. Instead of relying on personnel to visually assess cow gait and back curvature, the system uses cameras and image processing algorithms to objectively measure these parameters, eliminating training requirements and personnel dependencies while maintaining or improving detection accuracy.
4Extent of automation
If back line curvature is measured from images, then automated detection is achieved, but image processing complexity increases
Solution Approach 1:
The patent extracts the back line as a simplified one-dimensional representation from the two-dimensional image, reducing computational complexity. By identifying only the relevant coordinates along the back line rather than analyzing the entire image, the system achieves automated detection with minimal processing requirements. This extraction approach filters out unnecessary information while preserving the essential curvature data needed for lameness detection.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A device for identifying a cow (13) that is likely to be lame and is to be selected from a dairy herd has, in a defined spatial relation to an automatic milking parlor or the like, a cage (12) that can be triggered by a cow (13) by means of a target trigger (16) at the cage (12), which is followed by a differential imager (18) for this image (15.1) and for an occasional reference image (15.0) of the cage (12) without a cow (13), as well as a curvature analyzer (23) for the course of the back line (21) of the cow (13) scanned in the resulting image (19). At least one program-controlled image processing (17) can be used to color the background (25) of the images (15.0, 15.1) and/or to color a strip (20) bounded by the back line (21) in the difference image (19).