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We present an artificial intelligence (AI)-enhanced monitoring framework designed to assist personnel in evaluating and maintaining animal welfare using a modular architecture. This framework integrates multiple deep learning models to automatically compute metrics relevant to assessing animal well-being. Using deep learning for AI-based vision adapted from industrial applications and human behavioral analysis, the framework includes modules for markerless animal identification and health status assessment (e.g., locomotion score and body condition score). Methods for behavioral analysis are also included to evaluate how nutritional and rearing conditions impact behaviors. These models are initially trained on public datasets and then fine-tuned on original data. We demonstrate the approach through two use cases: a health monitoring system for dairy cattle and a piglet behavior analysis system. The results indicate that scalable deep learning and edge computing solutions can support precision livestock farming by automating welfare assessments and enabling timely, data-driven interventions.

Lameness is one of the most challenging problems in the dairy industry. Control is impeded because farmers often underestimate the number of lame cows. The objectives of this study were to assess German farmers' awareness of lameness in their herds and to determine the associations between farmers' awareness and their management practices, farm characteristics as well as with farmers' education, personality traits and attitudes. As a part of a large cross-sectional study, veterinarians visited farms in three structurally different regions of Germany: north ( n = 253), east ( n = 252), and south ( n = 260). The cows ( n = 84,998) were scored for locomotion and farmers were asked to estimate the number of cows that were lame or did not walk soundly. The ratio of farmers' estimated prevalence and the veterinarians' observed prevalence (Farmer's Detection Index; FDI) was calculated. The median lameness prevalence assessed by the veterinarians was 23.1, 39.1, and 23.2%, and the median prevalence of lame cows estimated by the farmers was 9.5, 9.5, and 7.1% in the north, east, and south, respectively. On average, farmers were conscious of only 45.3% (north), 24.0% (east), and 30.0% (south) of their lame cows. Farmers managing their herds according to organic principles had a higher FDI than farmers who managed their herds conventionally. Surprisingly, no significant associations between FDI and factors concerning claw health management could be detected. Therefore, increased awareness did not seem to be necessarily linked to improved management. Moreover, the FDI was not significantly associated with farmers' education or herd size. In the south, more extraverted farmers had a lower FDI. Those farmers who totally agreed with the statement, “I am satisfied with my herd's health,” had a lower FDI than farmers who disagreed or were undecided. Moreover, farmers who disagreed or were undecided with the statement, “It affects me to see a cow in pain” had a higher FDI than those farmers who agreed to the statement. The results indicate that poor awareness of lameness was linked to the farmers' attitude and personality. Therefore, new approaches concerning the consultation regarding lameness control, such as the use of Motivational Interviewing, might be useful in the future.

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.

Background Lameness is a significant problem for the dairy industry worldwide. No previous studies have evaluated the prevalence of lameness or digital dermatitis (DD) in dairy cattle herds in Egypt. A total of 16,098 dairy cows from 55 dairy herds in 11 Egyptian governorates underwent visual locomotion scoring using a 4-point scoring system. Cows that had a lameness score ≥ 2 were considered clinically lame. Following manure removal with water and using a flashlight, the cows’ hind feet were examined in the milking parlour to identify DD lesions and classify with M-score. Furthermore, each cow was assigned a hock score (a 3-point scale) and a hygiene score (a 4-point scale). The cow-, within-and between-herd prevalence of lameness and DD and associated 95% confidence intervals (CI) were calculated. The prevalence of hock lesions and poor cow hygiene was also calculated. Results Of the examined cows, 6,883 were found to be clinically lame (42.8%, 95% CI = 42.0–43.5%). The average within-herd prevalence of lameness was 43.1% (95% CI = 35.9–50.3%). None of the dairy herds recruited into the study were found to be free from clinical lameness. The average within-herd prevalence of DD was 6.4% (95% CI = 4.9–8.0%). The herd-level prevalence of DD was 92.7% (95% CI = 85.9–99.6%). Active DD lesions (M1, M2, M4.1) were identified in 464 cows (2.9%) while inactive lesions (M3, M4) were identified in 559 cows (3.5%). The within-herd prevalence of hock lesions (score 2 or 3) was 12.6% (95% CI = 4.03–21.1%) while a severe hock lesion had within-herd prevalence of 0.31% (95% CI = 0.12–0.51%). Cow-level prevalence of hock lesions was 6.2% (n = 847, 95% CI = 5.8–6.2%). The majority of examined cows had a hygiene score of 4 (n = 10,814, prevalence = 70.3%, 95% CI = 69.5–71%). Conclusions The prevalence of lameness was higher than prevalence estimates reported for other countries which could be due to differing management and/or environmental factors. DD was identified at lower prevalence in most herds but with high herd-level prevalence. Poor cow hygiene was notable in most herds. Measures to reduce the prevalence of lameness and to improve cow hygiene in dairy cattle herds in Egypt are therefore needed.

This study investigated the potential use of infrared thermography (IRT) as a routine tool for the early diagnosis of laminitis in dairy cows, with a long-term goal of automating the method. The specific study objectives were as follows: (1) to establish any relationship between the maximum temperature (MT) of the coronary band and locomotion scores (LS); (2) to correlate the MT of different hoof regions (sole, interdigital space and coronary band) with lameness diseases; and (3) to assess whether parity influences hoof temperature. Thermal images of hind feet of 368 cows were captured with an infrared camera. Coronary band MTs were significantly higher in cows with LS ≥3 (cranial [CR] = 34.15 ± 2.07°C, caudal [CD] = 32.48 ± 3.02°C) than in cows with LS = 1 (CR = 32.13 ± 4.72°C, CD = 30.09 ± 5.81°C). Parity significantly influenced MTs, with lower temperatures recorded across all hoof regions in multiparous cows (≥3 calvings) than in primiparous cows. Additionally, hoof MTs were higher in cows with interdigital dermatitis (CR = 32.17 ± 2.24°C, CD = 30.66 ± 3.67°C, sole = 26.91 ± 2.48°C, interdigital space = 33.83 ± 2.40°C) than in healthy cows. These findings support the use of IRT to identify early signs of lameness and highlight the need for further research to enable automated thermographic monitoring in dairy herds.

A cross-sectional study was conducted to estimate the prevalence of clinical lameness in high-producing Holstein cows housed in 50 freestall barns in Minnesota during summer. Locomotion and body condition scoring were performed on a total of 5,626 cows in 53 high-production groups. Cow records were collected from the nearest Dairy Herd Improvement Association test date, and herd characteristics were collected at the time of the visit. The mean prevalence of clinical lameness (proportion of cows with locomotion score ≥3 on a 1-to-5 scale, where 1 = normal and 5 = severely lame), and its association with lactation number, month of lactation, body condition score, and type of stall surface were evaluated. The mean prevalence of clinical lameness was 24.6%, which was 3.1 times greater, on average, than the prevalence estimated by the herd managers on each farm. The prevalence of lameness in first-lactation cows was 12.8% and prevalence increased on average at a rate of 8 percentage units per lactation. There was no association between the mean prevalence of clinical lameness and month of lactation (for months 1 to 10). Underconditioned cows had a higher prevalence of clinical lameness than normal or overconditioned cows. The prevalence of lameness was lower in freestall herds with sand stalls (17.1%) than in freestall herds with mattress stall surfaces (27.9%). Data indicate that the best 10th percentile of dairy farms had a mean prevalence of lameness of 5.4% with only 1.47% of cows with locomotion score = 4 and no cows with locomotion score = 5.

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.

Thirty-five lactating Jersey cows were recruited to the study. They were grouped according to locomotion score (LS), where low scores indicate normal gait. LS-1 (n = 12), LS-2 (n = 12) and LS-3 (n = 11) were used. Locomotion scores were balanced for parity and stage of lactation. Lying behaviour was recorded using IceTag™ data loggers attached to the cows for four consecutive days. The study animals remained in the straw based yards with grooved concrete flooring throughout the duration of the study. All data were normally distributed and assessed using a one-way ANOVA with a post hoc Tukey test. There were no statistically significant differences between locomotion score and the time spent lying, active and standing of zero-grazed lactating Jersey dairy cattle housed on straw yards. Lame cows (LS-3) had significantly shorter lying bouts than sound cows (LS-1) (34 min vs. 42 min, respectively). There has been limited research to date measuring the lying behaviour of cattle on straw and into the Jersey breed. The cows had longer than expected standing times and an increased frequency of lying bouts. This may have been attributed to the stocking density in which the cows were kept. We also reported a prevalence of lameness within the herd of 38%.

Background The effects of lameness on fertility have been documented frequently but few data are available from seasonally breeding, pasture-based herds (such as those used in Ireland) where cows are housed during the winter months but managed at pasture for the remainder of the year. This study determined the prevalence of lameness in a group of 786 cows in 10 pasture-based Irish dairy herds before, during and after the breeding season and assessed the relationship between lameness and the reproductive performance in these herds through serial locomotion scoring during the grazing period. Results Lameness prevalences of 11.6 % before, 14.6 % during and 11.6 % after the breeding season were found and these compared favourably to results from housed cattle and are similar to other studies carried out in grazing herds. A Cox proportional hazards model with locomotion score as time varying covariate was used. After controlling for the effect of farm, month of calving, body condition score at calving, body condition score loss after calving and economic breeding index, cows identified as lame during the study were less likely to become pregnant. Cows lame before the earliest serve date but no longer lame during the breeding season, cows becoming lame after the earliest serve date and cows identified lame both before and after this date were respectively 12 %, 35 % and 38 % less likely to become pregnant compared to cows never observed lame during the study. However, these findings were only significant for cows becoming lame after the earliest serve date and cows lame both before and after the start of breeding. Conclusions This study found that the reproductive efficiency was significantly (p < 0.05) lower in cows becoming lame during the breeding season and cows lame before and during the breeding season compared to non-lame cows. Cows no longer lame during the breeding season had a lower Submission Rate to first serve within 3 weeks of earliest serve date. However, the Pregnancy Rate was not significantly (p > 0.05) lower in these animals compared to cows never diagnosed as lame. In addition to lameness status, nutritional status and genetics were found to influence the reproductive performance in pasture-based Irish dairy herds.

In this study, artificial neural networks (ANNs) were employed to investigate the relationship between locomotion score and production traits. A total number of 123 dairy cows from a free-stall housing farm were used in this study. To compare the effectiveness of the ANNs for the prediction of locomotion score, the multiple linear regression (MLR) model was developed using the eight production traits, body condition score, parity, days in milk, daily milk yield, milk fat percent, milk protein percent, daily milk fat yield, and daily milk protein yield as input variables to predict the locomotion score. The ANN predictions gave a higher coefficient of determination (R2) values with lower mean squared error (MSE) than MLR. The R2 and MSE of the MLR model were 0.53 and 0.36, respectively. However, the ANN model for the same dataset produced much improved results with R2 = 0.80 ‏ and MSE = 0.16, respectively. Globally, the results of this study showed that the connectionist network model was a better tool to predict locomotion scores compared to the multiple linear regression.