Early Detection Of Lameness Research Articles

Development of a real-time cattle lameness detection system using a single side-view camera

Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lameness detection in dairy farming. Our study employs image processing techniques and deep learning methods for cattle detection, tracking, and lameness classification. We utilize two powerful object detection algorithms: Mask-RCNN from Detectron2 and the popular YOLOv8. Their performance is compared to identify the most effective approach for this application. Bounding boxes are drawn around detected cattle to assign unique local IDs, enabling individual tracking and isolation throughout the video sequence. Additionally, mask regions generated by the chosen detection algorithm provide valuable data for feature extraction, which is crucial for subsequent lameness classification. The extracted cattle mask region values serve as the basis for feature extraction, capturing relevant information indicative of lameness. These features, combined with the local IDs assigned during tracking, are used to compute a lameness score for each cattle. We explore the efficacy of various established machine learning algorithms, such as Support Vector Machines (SVM), AdaBoost and so on, in analyzing the extracted lameness features. Evaluation of the proposed system was conducted across three key domains: detection, tracking, and lameness classification. Notably, the detection module employing Detectron2 achieved an impressive accuracy of 98.98%. Similarly, the tracking module attained a high accuracy of 99.50%. In lameness classification, AdaBoost emerged as the most effective algorithm, yielding the highest overall average accuracy (77.9%). Other established machine learning algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Random Forests, also demonstrated promising performance (DT: 75.32%, SVM: 75.20%, Random Forest: 74.9%). The presented approach demonstrates the successful implementation for cattle lameness detection. The proposed system has the potential to revolutionize dairy farm management by enabling early lameness detection and facilitating effective monitoring of cattle health. Our findings contribute valuable insights into the application of advanced computer vision methods for livestock health management.

Revolutionizing Cow Welfare Monitoring: A Novel Top-View Perspective with Depth Camera-Based Lameness Classification.

This study innovates livestock health management, utilizing a top-view depth camera for accurate cow lameness detection, classification, and precise segmentation through integration with a 3D depth camera and deep learning, distinguishing it from 2D systems. It underscores the importance of early lameness detection in cattle and focuses on extracting depth data from the cow's body, with a specific emphasis on the back region's maximum value. Precise cow detection and tracking are achieved through the Detectron2 framework and Intersection Over Union (IOU) techniques. Across a three-day testing period, with observations conducted twice daily with varying cow populations (ranging from 56 to 64 cows per day), the study consistently achieves an impressive average detection accuracy of 99.94%. Tracking accuracy remains at 99.92% over the same observation period. Subsequently, the research extracts the cow's depth region using binary mask images derived from detection results and original depth images. Feature extraction generates a feature vector based on maximum height measurements from the cow's backbone area. This feature vector is utilized for classification, evaluating three classifiers: Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The study highlights the potential of top-view depth video cameras for accurate cow lameness detection and classification, with significant implications for livestock health management.

Leveraging Accelerometer Data for Lameness Detection in Dairy Cows: A Longitudinal Study of Six Farms in Germany

Lameness in dairy cows poses a significant challenge to improving animal well-being and optimizing economic efficiency in the dairy industry. To address this, employing automated animal surveillance for early lameness detection and prevention through activity sensors proves to be a promising strategy. In this study, we analyzed activity (accelerometer) data and additional cow-individual and farm-related data from a longitudinal study involving 4860 Holstein dairy cows on six farms in Germany during 2015–2016. We designed and investigated various statistical models and chose a logistic regression model with mixed effects capable of detecting lameness with a sensitivity of 77%. Our results demonstrate the potential of automated animal surveillance and hold the promise of significantly improving lameness detection approaches in dairy livestock.

Early Detection of Lameness in Cattle with Image Processing Techniques

Lameness in dairy cattle has a negative effect on fertility, milk yield and various behaviors. Therefore, lameness in cattle causes significant economic losses in countries. In our article, it is aimed to detect lameness in cattle early with image processing techniques. Deep learning and image processing techniques were used in the article. In the article, YOLOv5 algorithm is used for object detection and Shufflenetv2k30 algorithm is used as image processing technology. Within the scope of the article, the images were subjected to a preprocessing (data augmentation) and then the cattle in the selected photos were identified by our trained deep learning model. The detected cattle were tagged and then the posture estimation of these tagged cattle was made. The angles between the joints of the cattle were found on the cattle whose posture pose was estimated. In the performance analysis, training was started with the weights of the Pre-training yolov5l model and the best weight output of the 200 epoch trained model was 75%. The best weight output of the model trained from zero to 400 epochs without using any model weights was 63%. Pre-training was started with the weights of the shufflenetv2k30 model and the weight output of the model trained for 400 epochs was 71%. This article will contribute to the studies to be done in the academic field and will create important data for the studies to be done in the livestock sector.

Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

Early lameness detection is crucial to ensure the welfare and productivity of dairy cows. However, current research on early lameness identification using wearable analysis relies on the limited robustness of indirect behavioral measures, which are susceptible to individual variations and imbalances in lameness samples. In this study, we propose a semi-supervised Long short-term memory (LSTM)-Autoencoder algorithm for early lameness detection in dairy cows through time series data reconstruction. We collected gait data from all four limbs of 30 dairy cows using four IMUs. A LSTM-Autoencoder with three LSTM hidden layers was trained to learn the time series features of healthy gaits. Each gait was reconstructed, and anomaly gaits exceeding a threshold were identified by comparing reconstructed gaits with actual gaits. The gait symmetry was measured by comparing the percentage of anomaly gait between opposite limbs as an indicator of lameness severity. With a high accuracy of 97.78% and a true negative rate of 98.33%, our integrated approach outperforms traditional methods in early lameness detection and lame limb identification, enabling real-time monitoring and timely identification of lameness. The study is the first attempt at using a time series anomaly detection framework with deep learning-based gait reconstruction for lameness detection. Wearable gait analysis offers portability and real-time capabilities, providing continuous, accurate, and comprehensive gait information unaffected by lighting and field-of-view limitations. This approach holds promise for enhancing animal welfare and optimizing management practices in the dairy industry through timely identification and continuous monitoring of lameness.

The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle

This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30-42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.

Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows.

Lameness is one of the culling factors such as mastitis, low milk yield, and infertility that cause economic losses in herd management as they threaten animal health and welfare. The purpose of this study was to evaluate the early detection of lameness in Brown Swiss cattle by using a data mining algorithm by both integrating lameness scores and some image parameters such as Lab (CIE L*, a*, b*), HSB (hue, saturation, brightness), RGB (red, green, blue) by processing thermal images with ImageJ program. In the study, the variables obtained as a result of processing the skin surface temperatures and thermal images taken at the fetlock joint of 33 Brown Swiss cattle were used as independent variables. Also, healthy cows (lameness scores 1 and 2) and unhealthy cows (lameness scores 3, 4, and 5) used in the diagnosis of lameness were used as a binary response variable. Classification and regression tree (CART) was used as a data mining algorithm in the diagnosis of lameness. As a result, the CART algorithm correctly classified 12 of the 13 heads unhealthy cows according to locomotion scores. According to locomotion scores by using CART analysis in this study, independent variables that are used to classify healthy and unhealthy (lame) animals were determined as maximum temperature (Tmax), green (mean), L (max), and age (P<0.05). The cut-off values of these independent variables were predicted as 32.40, 149.14, 97.11, and 5.50 for Tmax, green (mean), L (max), and age, respectively. Also, the sensitivity, specificity, and area under the ROC curve (AUC) of the CART algorithm for locomotion scoring were found as 92.31%, 95%, and 93.7% respectively. The area under ROC curve (AUC) was found to be significant in the diagnosis of lameness (P<0.01). Results showed that the use of CART classification algorithm together with thermal camera and image processing methods is a usefull tool in the detection of lameness in the herds. It is recommended that more comprehensive studies by increasing the number of animals in the future would be more beneficial.

Evaluation of feedback methods for improved detection of hindlimb lameness in horses among riding instructors and trainers.

Lameness, a wellknown issue in sport horses, impedes performance and impairs welfare. Early detection of lameness is essential for horses to receive needed treatment, but detection of hindlimb lameness is challenging. Riding instructors and trainers observe horses in motion in their daily work and could contribute to more efficient lameness detection. In this cross-sectional and prospective study, we evaluated the ability of riding instructors and trainers to assess hindlimb lameness. We also evaluated different feedback methods for improved lameness detection. For the cross-sectional part, n = 64 riding instructors and trainers of varying level and n = 23 high-level trainers were shown 13 videos of trotting horses, lameness degree: 0–3.5 (test 1) and tasked with classifying the horses as sound, left hindlimb lame, or right hindlimb lame. For the prospective part, the riding instructors and trainers of varying levels were randomly allocated to three different groups (a, b, c) and given 14 days of feedback-based, computer-aided training in identifying hindlimb lameness, where they assessed 13 videos (of which three were repeated from test 1) of horses trotting in a straight line. Participants in groups a-c received different feedback after each video (group a: correct answer and re-viewing of video at full and 65% speed; group b: correct answer, re-viewing of video at full and 65% speed, narrator providing explanations; group c: correct answer and re-viewing of video at full speed). After computer-aided training, the participants were again subjected to the video test (test 2). Participants also provided background information regarding level of training etcetera. Effects of participants' background on results were analyzed using analysis of variance, and effects of the different feedback methods were analyzed using generalized estimation equations. On test 1, 44% (group a), 48% (b), 46% (c), and 47% (high-level trainers) of horses were correctly classified. Group a participants significantly improved their test score, both with (p < 0.0001) and without (p = 0.0086) inclusion of repeated videos. For group c, significant improvement was only seen with inclusion of repeated videos (p = 0.041). For group b, no significant improvement was seen (p = 0.51). Although test 2 scores were low, computer-aided training may be useful for improving hindlimb lameness detection.

Comparison of Low- and High-Cost Infrared Thermal Imaging Devices for the Detection of Lameness in Dairy Cattle

Simple SummaryLameness has high economic and welfare cost to the U.K. dairy industry; accurate and early detection of lameness minimises this cost. Thermal imaging devices can be used as a method of detecting lameness; however, these devices are typically high-cost and fragile, limiting their usefulness in a farm setting. This study looked at the effectiveness of low-cost thermal imaging devices when used as lameness detection aids, by comparing one to a research-specification thermal imaging device. Thermal images were taken of cattle feet, and each cow was assessed for lameness. Both devices tested were able to determine whether the cattle were lame; however, the research-specification device performed marginally better at this function. This minimal difference in effectiveness between these devices suggests that low-cost thermal imaging devices could be used as a lameness detection aid; increased use of these devices by farmers may increase lameness detection rates and benefit animal welfare.Lameness has a high economic cost to the U.K. dairy industry; accurate and early detection of lameness minimises this cost. Infrared thermal imaging (IRT) devices have shown promising results for use as a lameness detection aid in cattle when used in research settings; these devices are typically high-cost, limiting their adoption. This study analysed the effectiveness of low-cost IRT devices (LCDs) as lameness detection aids, by comparing both maximum environmentally adjusted temperature values and hindfeet temperature difference collected by an LCD to the mobility score of the cow; this test was repeated for data collected by a research-specification device. Data collection occurred during routine milking of 83 cattle; each cow’s mobility was scored afterwards. Significant differences were found between lame and sound cows with the LCD, upon analysis of both methods. There was no significant difference between the data captured by differing devices. The maximum sensitivity and specificity values for the LCD were calculated as 66.95 and 64.53, respectively, compared with 70.34 and 70.94, respectively, for the research-specification device; optimum threshold values for these were equivalent for both devices, suggesting IRT lameness identification is not device-dependent. It was concluded that a minimal difference in effectiveness between tested devices suggests that LCDs could be used as a lameness detection aid; consequently, there is potential for widespread adoption as on-farm detection aids.

Embedded microcomputer-based force plate system validation when evaluating lameness severity differentiation under an induced synovitis model in lactating dairy cattle

Bovine lameness has relatively large negative economic and welfare implications on the U.S. dairy industry. Due to the ramifications, early lameness detection will aid in assisting dairy producers to mitigate downstream effects through early treatment. The objective of this study was to determine the minimum standing time required among 2-, 3-, 4-, 5-, and 10 min time intervals to obtain an accurate weight distribution estimate for each leg when attempting to detect lameness. An embedded microcomputer-based force plate system was developed to measure vertical forces from individual cow limb weight distribution to detect bovine lameness when utilizing an induced synovitis lameness model. The force plate has four quadrants, with each load cell quadrant measuring the force placed on it from a single limb. The force plate recorded weight (kg) every second from each load cell quadrant, after which, a 60 s moving average for weight distribution was calculated. A sequential study design was employed to evaluate non-lame and induced lameness to ensure time requirements were consistent. Prior to induction, the force plate system was used to measure weight distribution every second for 15 min. After lameness induction, additional 15 min increments were recorded every 24 h for seven days. Lameness was induced by injecting the left hind distal interphalangeal joint in three cows with amphotericin B, 12 h prior to the start of the study. Data were analyzed using a linear mixed effect that included the fixed effects of day relative to lameness induction, time period, foot and injected foot. Cow within replicate was included as a random effect. Cumulative minutes were assessed up to 15 min by comparing the least square rolling 60 s cumulative means expressed as a percentage of each animal’s BW percentage placed on each leg for 2-, 3-, 4-, 5-, and 10 min intervals. Results indicate that the minimum time needed for accurate lameness detection in cows was 2 min.

Early Detection of Locomotion Disorders in Gilts Using a Novel Visual Analogue Scale; Associations with Chronic Stress and Reproduction.

Simple SummaryLameness in sows causes pain and poor welfare. Early detection is crucial if treatment is likely to be effective. Locomotion scoring is the best way to achieve this, but existing scoring systems are not sensitive enough to detect subtle deviations from optimal locomotion. Our objective was to develop a new visual analogue scale (VAS) to measure the locomotory ability of sows over time. Effectiveness in detecting slight deviations was tested in young female pigs by comparing the scale to an existing categorical scoring system. The VAS detected slight deviations from optimal locomotion over time more effectively than the categorical locomotion scoring system. It was also positively associated with hair cortisol concentrations (chronic stress) and measures of reproductive performance. If used by farmers, the VAS could potentially help in lameness prevention and thereby improve sow welfare and performance.Locomotion scoring is crucial for the early detection of lameness, which reduces sow welfare and performance. Our objective was to test the effectiveness of a visual analogue scale (VAS) to measure locomotory ability (OVERALL) compared to a categorical scoring system (CAT) and to investigate associations with hair cortisol and reproductive performance. Locomotion was scored in gilts (n = 51) at service, on day 57 and day 108 of pregnancy, and at weaning, using a VAS (150 mm line: 0 mm (perfect)–150 mm (severely lame)), and a CAT (1 (perfect)–5 (severely lame)). Hair cortisol concentration was measured on day 108 of pregnancy. Reproductive performance data (parity 1–4) were acquired from farm records. VAS detected deviations from optimal locomotion more effectively than the CAT (F3,145 = 2.70; p ≤ 0.05 versus F3,195 = 2.45; p = 0.065). Higher OVERALL scores at service (REG = 0.003 ± 0.0012; F1,48 = 4.25; p ≤ 0.05) and on day 57 (REG = 0.003 ± 0.0013; F1,48 = 6.95; p ≤ 0.05) were associated with higher hair cortisol concentrations on day 108. Positive associations were detected between OVERALL at service and the number of piglets born dead (REG = 0.01 ± 0.006; F1,36 = 4.24; p ≤ 0.05), and total born (REG = 0.1 ± 0.03; F1,120 = 4.88; p ≤ 0.05). The VAS better facilitates early detection of lameness, which could help to prevent detrimental effects, possibly mediated by chronic stress, on reproductive performance.

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming.

Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle

Timely lameness detection is one of the major and costliest health problems in dairy cattle that farmers and practitioners haven't yet solved adequately. The primary reason behind this is the high initial setup costs, complex equipment and lack of multi-vendor interoperability in currently available solutions. On the other hand, human observation based solutions relying on visual inspections are prone to late detection with possible human error, and are not scalable. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows' health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, we have developed an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage.The proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. Using long-range pedometers specifically designed for use in dairy cattle, we monitor the activity of each cow in the herd. The accelerometric data from these sensors is aggregated at the fog node to form a time series of behavioral activities, which are further analyzed in the cloud. Our hybrid clustering and classification model identifies each cow as either Active, Normal or Dormant, and further, Lame or Non-Lame. The detected lameness anomalies are further sent to farmer's mobile device by way of push notifications. The results indicate that we can detect lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Moreover, with fog based computational assistance in the setup, we see an 84% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach.

Risk Factors and Detection of Lameness Using Infrared Thermography in Dairy Cows – A Review

Abstract Lameness in dairy cows is a worldwide problem, usually a consequence of hoof diseases. Hoof problems have a negative impact on animal health and welfare as well as the economy of the farm. Prevention and early diagnosis of lameness should prevent the development of the disease and consequent high costs of animal treatment. In this review, the most common causes of both infectious and noninfectious lesions are described. Susceptibility to lesions is primarily influenced by the quality of the horn. The quality of the horn is influenced by internal and external conditions such as hygiene, nutrition, hormonal changes during calving and lactation, the animal’s age or genetic predisposition. The next part of this review summarizes the basic principles and possibilities of using infrared thermography in the early detection of lameness in dairy cows.

Optimising lameness detection in dairy cattle by using handheld infrared thermometers.

Lameness is one of the most serious economic and welfare issues in the dairy industry. Early detection of lameness can be difficult, but provision of early treatment is crucial. Previous studies have used infrared thermography to show that increased foot temperature (FT) is associated with lameness and foot lesions. However, poor accuracy has limited the management application potential. This study analysed ambient‐temperature (AT)‐adjusted foot‐surface temperatures and temperature differences between the hind feet of individual cows to enhance lameness detection. Cow FTs were recorded on a 990‐cow farm using an infrared thermometer fortnightly for 6 months. Additionally, mobility level was scored using the AHDB Dairy 4‐point scale. The averages of FTs and ATs were 23.83 ± 0.03°C and 13.99 ± 1.60°C, respectively. The FT of cows with lameness was significantly higher than that of cows without lameness (P < 0.001). Increases in FTs correlated with the mobility score (MS) (P < 0.001). According to receiver operating characteristic (ROC) curves, the optimal threshold based on actual FTs was 23.3°C with 78.5% sensitivity and 39.2% specificity. However, the ROC curve for the AT‐adjusted FT and FT difference parameters showed minimal improvements over the FT in detecting lameness. In conclusion, the infrared thermometer results demonstrated the association between elevated FTs and lameness, but further improvements to this detection technique will be required before it can be implemented as a management tool for detecting cows that could benefit from treatment. With additional validation, the technique could be used as a screening device to identify cows in need of further assessment.

Moderate lameness leads to marked behavioral changes in dairy cows

Lameness is one of the most prevalent diseases affecting the welfare of cows in modern dairy production. Lameness leads to behavioral changes in severely lame cows, which have been investigated in much detail. For early detection of lameness, knowledge of the effects of moderate lameness on cow behavior is crucial. Therefore, the behavior of nonlame and moderately lame cows was compared on 17 Swiss dairy farms. On each farm, 5 to 11 nonlame (locomotion score 1 of 5) and 2 to 7 moderately lame (locomotion score 3 of 5) cows were selected for data collection in two 48-h periods (A, B) separated by an interval of 6 to 10 wk. Based on visual locomotion scoring, 142 nonlame and 66 moderately lame cows were examined in period A and 128 nonlame and 53 moderately lame cows in period B. Between these 2 periods, the cows underwent corrective hoof trimming. Lying behavior, locomotor activity, and neck activity were recorded by accelerometers (MSR145 data logger, MSR Electronics GmbH, Seuzach, Switzerland), and feeding and rumination behaviors by noseband sensors (RumiWatch halter, ITIN + HOCH GmbH, Liestal, Switzerland). Furthermore, visits to the brush and the concentrate feeder, and the milking order position were recorded. In comparison with nonlame cows, moderately lame cows had a longer lying duration, a longer average lying bout duration, and a greater lateral asymmetry in lying duration. Average locomotor activity, locomotor activity during 1 h after feed delivery or push-ups, and average neck activity were lower in moderately lame cows. Eating time and the number of eating chews (jaw movements) were reduced in moderately lame compared with nonlame cows, whereas no effect of moderate lameness was evident for ruminating time, number of ruminating chews and boluses, and average number of ruminating chews per bolus. Moderately lame cows visited the concentrate feeder and the brush less frequently, and they were further back in the milking order compared with nonlame cows. In conclusion, nonlame and moderately lame cows differed in a biologically relevant way in many of the behavioral variables investigated in this study. Therefore, the use of these behavioral changes seems to be promising to develop a tool for early lameness detection.

Development of an early detection system for lameness of broilers using computer vision

Lameness is one of the most important causes of poor welfare in poultry. Previous studies have documented approximately 30% of the chickens were seriously lame. In this research, a novel technique was developed for early detection of lameness in broilers. For this purpose, broiler chickens with five different predefined gait scores were continuously monitored by a digital camera as they walked throughout a test corridor. Then, image analysis algorithm was applied to detect some feature variables (speed, step frequency, step length and the lateral body oscillation) of broilers. Afterwards, a correlation test was performed to define the coefficient of correlation between the feature variables (step frequency, step length, speed and LBO) obtained by the proposed algorithm and the gait score levels of the birds, which respectively resulted in r=0.831, 0.882 (p<0.05), 0.844, 0.861. Furthermore, each feature variable was investigated to find statistical differences between gait scores (as a measure of lameness) of broilers. It was performed to assess the effects of gait score on speed, step length, step frequency and lateral body oscillations of the broilers. The results showed that all investigated feature variables were efficacious to detect lameness in broilers starting from GS3. Since correlations were found between the feature variables (step frequency, step length, speed and LBO) obtained by the proposed algorithm and the gait score levels of the birds on the one hand and the statistical differences between gait score levels of broilers on the other hand; the results recommend that this fully-automated detection system has potential to be used as a real-time monitoring tool for early detection of lameness in broilers starting from GS3. However, to define lower gait scores than GS3, either new feature variables like foot curls and wing use should be inserted into the proposed system or this system should be combined with other automatic behaviour analysis tools for early detection of lameness in future research. It is very important to detect lameness at an early stage because it allows farmers and veterinarians to take immediate management actions in time.

Evaluation of two methodologies for lameness detection in dairy cows based on postural and gait abnormalities observed during milking and while restrained at headlock stanchions

Lameness is a critical issue on dairies with an impact on production and animal welfare. Early lameness detection followed by effective treatments could improve prognosis and cure rate of lame cows. Current methods for lameness detection are based on locomotion score (LS) that requires observation of cows walking, preferably at the exit of the milking parlor. This is a time-consuming task that is difficult to implement on large dairies. Therefore, a common methodology for lameness detection is based on milkers’ and cow pushers’ observations of cows walking to the milking parlor or standing at the milking stall (MPP). Observation of postural abnormalities predictive of lameness while cows are locked at stanchions (S) can be used as an alternative detection method. The objective of this research was to study the association between postural and gait abnormalities observed with S and MPP methodologies and lameness using LS≥3 as the reference method, as well as to evaluate the epidemiological characteristics of those methods as a diagnostic test for lameness. A secondary objective was to describe the type of hoof lesions observed with postural and gait abnormalities detected with LS, MPP, and S methodologies. A cross-sectional study design was performed on 2274 cows from one farm in California (US). Arched back, cow-hocked, wide-stance, and favored-limb postures as well as uneven gait were observed. Both lameness detection methodologies, S and MPP, indicated that arched back and favored-limb were postural abnormalities associated with lameness. However, the epidemiological test characteristics for each of the postures evaluated as a diagnostic test for lameness indicated that both detection methods, S and MPP, had good specificity (>0.91) but poor sensitivity (0.04–0.39). A convenience sample of 104 cows, selected based on LS>3, favored-limb, presence of two or more abnormal postures, and gait anomalies with either S or MPP methods, received a hoof examination. Lesions were observed on cows selected by LS (17/24), MPP (21/30), and S (33/60) criteria, suggesting a lack of concordance between lameness detection methodologies and visible hoof lesions. Nevertheless, due to the lack of acceptance of LS as the lameness detection method on large commercial dairies in California, it is imperative that future research evaluates modifications of S and MPP lameness detection techniques, considering hoof lesion as reference method.

Thermographic assessment of hoof temperature in dairy cows with different mobility scores

The aim of this study was to determine if temperature varies in hooves of cows with different mobility scores (MS) using infrared thermography. A total of 120 Friesian, Kiwi cross and Jersey cows were used, which were grouped according to their mobility scores (MS 0–3: n=30 in each MS group). Animals were introduced in a crush for clinical examination and then thermograms from the anterior, posterior, lateral and sole views were taken. Thermogram analysis of different anatomical hoof zones was performed. The temperatures obtained for different views were: anterior (20.8°C–24.7°C), posterior (20.7°C–25.9°C), lateral (19.2°C–22.4°C) and sole (16.5°C–20.1°C). The observed zonal temperature for each view was independent and similar for all MS, however significant differences between cows with MS 0 and MS 3 are described. For this purpose, a threshold temperature for the sole view>16.6°C was set allowing the identification of 83% cows with MS 1 and 60% those cows with MS 0. The best trade off was obtained for the posterior view, with a threshold temperature>23.5°C, allowing the identification ~70% of both cows with MS 0 and MS 3. Using IRT we were able to show that cows with MS 3 had higher hoof temperatures compared to cows with MS 0 in the anterior, posterior and sole views. None of the evaluated thermographic views showed significant temperatures changes for cows with MS 1 and MS 2. IRT is not a practical method to be used on farm and does not contribute to early lameness detection.

Intra-rater reliability among postural and gait abnormalities detected using locomotion scoring with observations in milking parlor and headlock stanchions

Dairy cow lameness is an important health issue, with a negative impact in animal welfare and productivity. Lameness has been assessed using a locomotion scoring (LS) system based on behaviors and postures related to gait. However, LS evaluation is a time consuming task that is not routinely performed on large dairies. Implementation of methods for early lameness detection that are easily incorporated into management could increase the rate of detection. The objective of this study was to evaluate the intra-rater reliability among postural and gait abnormalities detected using locomotion scoring with observations in milking parlor (MP) and headlock stanchions (S).