Claudia Bahr

Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity

The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farms daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cows performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY=0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well.

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.

Manual and automatic locomotion scoring systems in dairy cows: A review

The objective of this review was to describe, compare and evaluate agreement, reliability, and validity of manual and automatic locomotion scoring systems (MLSSs and ALSSs, respectively) used in dairy cattle lameness research. There are many different types of MLSSs and ALSSs. Twenty-five MLSSs were found in 244 articles. MLSSs use different types of scale (ordinal or continuous) and different gait and posture traits need to be observed. The most used MLSS (used in 28% of the references) is based on asymmetric gait, reluctance to bear weight, and arched back, and is scored on a five-level scale. Fifteen ALSSs were found that could be categorized according to three approaches: (a) the kinetic approach measures forces involved in locomotion, (b) the kinematic approach measures time and distance of variables associated to limb movement and some specific posture variables, and (c) the indirect approach uses behavioural variables or production variables as indicators for impaired locomotion. Agreement and reliability estimates were scarcely reported in articles related to MLSSs. When reported, inappropriate statistical methods such as PABAK and Pearson and Spearman correlation coefficients were commonly used. Some of the most frequently used MLSSs were poorly evaluated for agreement and reliability. Agreement and reliability estimates for the original four-, five- or nine-level MLSS, expressed in percentage of agreement, kappa and weighted kappa, showed large ranges among and sometimes also within articles. After the transformation into a two-level scale, agreement and reliability estimates showed acceptable estimates (percentage of agreement ≥ 75%; kappa and weighted kappa ≥ 0.6), but still estimates showed a large variation between articles. Agreement and reliability estimates for ALSSs were not reported in any article. Several ALSSs use MLSSs as a reference for model calibration and validation. However, varying agreement and reliability estimates of MLSSs make a clear definition of a lameness case difficult, and thus affect the validity of ALSSs. MLSSs and ALSSs showed limited validity for hoof lesion detection and pain assessment. The utilization of MLSSs and ALSSs should aim to the prevention and efficient management of conditions that induce impaired locomotion. Long-term studies comparing MLSSs and ALSSs while applying various strategies to detect and control unfavourable conditions leading to impaired locomotion are required to determine the usefulness of MLSSs and ALSSs for securing optimal production and animal welfare in practice.

Embryonic development and the physiological factors that coordinate hatching in domestic chickens

Embryonic growth and development is influenced by both endogenous and exogenous factors. The purpose of this review is to discuss the critical stages of chick embryonic development in relation to functional maturation of numerous organ systems, the acquisition of thermoregulation, and the hatching process. In addition, the mechanism of hatching, including sound synchronization and hormonal and environmental stimulation, will be discussed. Finally, the importance of effective hatching synchronization mechanisms will also be highlighted.

Different stressors elicit different responses in the salivary biomarkers cortisol, haptoglobin, and chromogranin A in pigs

Most commonly, salivary cortisol is used in pig stress assessment, alternative salivary biomarkers are scarcely studied. Here, salivary cortisol and two alternative salivary biomarkers, haptoglobin and chromogranin A were measured in a pig stress study. Treatment pigs (n = 24) were exposed to mixing and feed deprivation, in two trials, and compared to untreated controls (n = 24). Haptoglobin differed for feed deprivation vs control. Other differences were only found within treatment. Treatment pigs had higher salivary cortisol concentrations on the mixing day (P < 0.05). Chromogranin A concentrations were increased on the day of refeeding (P < 0.05). Haptoglobin showed a similar pattern to chromogranin A. Overall correlations between the salivary biomarkers were positive. Cortisol and chromogranin A were moderately correlated (r = 0.49, P < 0.0001), correlations between other markers were weaker. The present results indicate that different types of stressors elicited different physiological stress responses in the pigs, and therefore including various salivary biomarkers in stress evaluation seems useful.

Monitoring the hatch time of individual chicken embryos

This study investigated variations in eggshell temperature (T(egg)) during the hatching process of broiler eggs. Temperature sensors monitored embryo temperature by registering T(egg) every minute. Measurements carried out on a sample of 40 focal eggs revealed temperature drops between 2 to 6°C during the last 3 d of incubation. Video cameras recorded the hatching process and served as the gold standard reference for manually labeling the hatch times of chicks. Comparison between T(egg) drops and the hatch time of individuals revealed a time synchronization with 99% correlation coefficient and an absolute average time difference up to 25 min. Our findings suggest that attaching temperature sensors to eggshells is a precise tool for monitoring the hatch time of individual chicks. Individual hatch monitoring registers the biological age of chicks and facilitates an accurate and reliable means to count hatching results and manage the hatch window.

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%.

Effect of merging levels of locomotion scores for dairy cows on intra- and interrater reliability and agreement

Locomotion scores are used for lameness detection in dairy cows. In research, locomotion scores with 5 levels are used most often. Analysis of scores, however, is done after transformation of the original 5-level scale into a 4-, 3-, or 2-level scale to improve reliability and agreement. The objective of this study was to evaluate different ways of merging levels to optimize resolution, reliability, and agreement of locomotion scores for dairy cows. Locomotion scoring was done by using a 5-level scale and 10 experienced raters in 2 different scoring sessions from videos from 58 cows. Intra- and interrater reliability and agreement were calculated as weighted kappa coefficient (κw) and percentage of agreement (PA), respectively. Overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were determined for the 5-level scale and after transformation into 4-, 3-, and 2-level scales by merging different combinations of adjacent levels. Intrarater reliability (κw) ranged from 0.63 to 0.86, whereas intrarater agreement (PA) ranged from 60.3 to 82.8% for the 5-level scale. Interrater κw=0.28 to 0.84 and interrater PA=22.6 to 81.8% for the 5-level scale. The specific intrarater agreement was 76.4% for locomotion level 1, 68.5% for level 2, 65% for level 3, 77.2% for level 4, and 80% for level 5. Specific interrater agreement was 64.7% for locomotion level 1, 57.5% for level 2, 50.8% for level 3, 60% for level 4, and 45.2% for level 5. Specific intra- and interrater agreement suggested that levels 2 and 3 were more difficult to score consistently compared with other levels in the 5-level scale. The acceptance threshold for overall intra- and interrater reliability (κw and κ ≥0.6) and agreement (PA ≥75%) and specific intra- and interrater agreement (≥75% for all levels within locomotion score) was exceeded only for the 2-level scale when the 5 levels were merged as (12)(345) or (123)(45). In conclusion, when locomotion scoring is performed by experienced raters without further training together, the lowest specific intra- and interrater agreement was obtained in levels 2 and 3 of the 5-level scale. Acceptance thresholds for overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were exceeded only in the 2-level scale.