Arno Pluk

Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle

Currently, diagnosis of lameness at an early stage in dairy cows relies on visual observation by the farmer, which is time consuming and often omitted. Many studies have tried to develop automatic cow lameness detection systems. However, those studies apply thresholds to the whole population to detect whether or not an individual cow is lame. Therefore, the objective of this study was to develop and test an individualized version of the body movement pattern score, which uses back posture to classify lameness into 3 classes, and to compare both the population and the individual approach under farm conditions. In a data set of 223 videos from 90 cows, 76% of cows were correctly classified, with an 83% true positive rate and 22% false positive rate when using the population approach. A new data set, containing 105 videos of 8 cows that had moved through all 3 lameness classes, was used for an ANOVA on the 3 different classes, showing that body movement pattern scores differed significantly among cows. Moreover, the classification accuracy and the true positive rate increased by 10 percentage units up to 91%, and the false positive rate decreased by 4 percentage units down to 6% when based on an individual threshold compared with a population threshold.

Automatic measurement of touch and release angles of the fetlock joint for lameness detection in dairy cattle using vision techniques

This paper describes a synchronized measurement system combining image and pressure data to automatically record the angle of the metacarpus and metatarsus bones of the cow with respect to a vertical line, which is useful for lameness detection in dairy cattle. A camera system was developed to record the posture and movement of the cow and the timing and position of hoof placement and release were recorded using a pressure sensitive mat. Experiments with the automatic system were performed continuously on a farm in Ghent (Belgium) for 5 wk in September and October 2009. In total, 2,219 measurements were performed on 75 individual lactating Holstein cows. As a reference for the analysis of the calculated variables, the locomotion of the cows was visually scored from recorded videos by a trained observer into 3 classes of lameness [53.5% were scored with gait score (GS)1, 33.3% were scored with GS2, and 9.3% were scored with GS3]. The contact data of the pressure mat and the camera images recorded by the system were synchronized and combined to measure different angles of the legs of the cows, together with the range of motion of the leg. Significant differences were found between the different gait scores in the release angles of the front hooves, in the range of motion of the front hooves, and in the touch angles of the hind hooves. The contact data of the pressure mat and the camera images recorded by the system were synchronized and combined to measure different angles of the legs of the cows, together with the range of motion of the leg. With respect to the classification of lameness, the range of motion of the front hooves (42.1 and 42.8%) and the release angle of the front hooves (41.7 and 42.0%) were important variables. In 83.3% of the cows, a change in GS led to an increase in within-cow variance for the range of motion or the release angle of the front hooves. In 76.2% of the cows, an increase in GS led to a decrease in range of motion or an increase in release angle of the front hooves.

Evaluation of Step Overlap as an Automatic Measure in Dairy Cow Locomotion

The aim of this study was to explore the possibility of capturing cow locomotion activity by computer vision techniques and to calculate the correlation between step overlap and manually measured locomotion scores. In two experiments, a total of 208 video recordings of 85 individual lactating cows were gait scored visually by an observer. The side-view videos were recorded when cows were freely passing the experimental setup. After image processing, the imprint location, step overlap, body size, and relative step overlap were calculated. The values of automatically measured step overlap showed a high correlation with the manually measured step overlap (R2 = 0.739, p < 0.001; R2 = 0.809, p < 0.001). The maximal step overlap allowed differentiation between gait scores 1 and 3 (p = 0.032) and between gait scores 2 and 3 (p = 0.039). The difference between gait scores 1 and 2 was not significant (p = 0.079). There was a large variation between individual cows, in both the progress of lameness and the influence on step overlap. Changes in step overlap were also seen that were not matched by changes in gait score. Step overlap is a variable that shows a relationship with manual gait scores, but it is not strong enough to be used as a single classifier for lameness in all cows.

Online lameness detection in dairy cattle using Body Movement Pattern (BMP)

In this paper a method for real time lameness detection in dairy cattle based on back posture analysis is presented. The system utilizes image processing techniques to automatically detect lameness based on new definition called Body Movement Pattern (BMP). The traditional Locomotion Scoring (LS) system that is widely used for manual lameness detection by expert people failed to classify the selected cows from a commercial farm in Estonia. The proposed system not only performs the detection in automatic way but also categorizes the level of lameness with high accuracy. Two ellipses are fitted on the back posture and the parameters of the ellipses in relation to the head position are used as features for classification of the lameness degree. The new method has been tested on more than 1200 cows with success rate of 92%.

Image Based Separation of Dairy Cows for Automatic Lameness Detection with a Real Time Vision System

Automation of lameness detection with vision techniques has a high potential to improve the early recognition of lame cows and would have a positive impact on time efficient herd management. In order to get individual information about gait features from cows passing a corridor in row e. g. after leaving the milking parlour an automatic separation of cows in a sequence is necessary. The presented results are based on video recordings done on farm, where cows walk from the milking parlour after milking through a corridor in row of 10 to 20 animals. To cope with problems such as stopping for a while in front of the camera, overlap of cows, non-uniform time interval between cows, etc. an algorithm for cow separation is proposed based on local image filtering and statistical analysis of binary images frame by frame. Filters to enhance horizontal and vertical edges in an image are utilized for shadow and background reduction. Binarization on filtered images is made by using statistical analysis. The column-based summation of binarized images related to a threshold is used to decide when the next cow in a row is detected after another has passed already. First results show 95% correct cow separation.

Synchronized recording of pressure distribution and posture for automatic lameness detection in dairy cattle

Lameness, an increasing animal welfare problem, has a negative impact on milk production, body condition and reproductive performance in dairy cows. This paper describes a synchronized system which might be useful for automatic lameness detection in dairy cattle.

Automatic Identification of Activity Levels of Broiler Chickens with Different Gait Score in a Small Flock.

This paper extends existing activity identification methods and describes a new method to identify and assess the activity of chickens with different gait scores in a small flock. The chickens were scored for their degree of lameness by experts according to the method of Kestin et al. (1992). Five birds were selected in each of six gait score groups (GS0 to GS5). The chickens used were in total 30 Ross-308 chickens at the age of 26 days. The experimental period of in total 12 days was subdivided into two times six days were two different experiments were carried out.