Automated behavioural classification and identification through sensors has the potential to improve health and welfare of the animals. Position of a sensor, sampling frequency and window size of segmented signal data has a major impact on classification accuracy in activity recognition and energy needs for the sensor, yet, there are no studies in precision livestock farming that have evaluated the effect of all these factors simultaneously. The aim of this study was to evaluate the effects of position (ear and collar), sampling frequency (8, 16 and 32 Hz) of a triaxial accelerometer and gyroscope sensor and window size (3, 5 and 7 s) on the classification of important behaviours in sheep such as lying, standing and walking. Behaviours were classified using a random forest approach with 44 feature characteristics. The best performance for walking, standing and lying classification in sheep (accuracy 95%, F-score 91%-97%) was obtained using combination of 32 Hz, 7 s and 32 Hz, 5 s for both ear and collar sensors, although, results obtained with 16 Hz and 7 s window were comparable with accuracy of 91%-93% and F-score 88%-95%. Energy efficiency was best at a 7 s window. This suggests that sampling at 16 Hz with 7 s window will offer benefits in a real-time behavioural monitoring system for sheep due to reduced energy needs.

Lameness in sheep is the biggest cause of concern regarding poor health and welfare among sheep-producing countries. Best practice for lameness relies on rapid treatment, yet there are no objective measures of lameness detection. Accelerometers and gyroscopes have been widely used in human activity studies and their use is becoming increasingly common in livestock. In this study, we used 23 datasets (10 non-lame and 13 lame sheep) from an accelerometer-and gyroscope-based ear sensor with a sampling frequency of 16 Hz to develop and compare algorithms that can differentiate lameness within three different activities (walking, standing and lying). We show for the first time that features extracted from accelerometer and gyroscope signals can differentiate between lame and non-lame sheep while standing, walking and lying. The random forest algorithm performed best for classifying lameness with an accuracy of 84.91% within lying, 81.15% within standing and 76.83% within walking and overall correctly classified over 80% sheep within activities. Both accelerometer-and gyroscope-based features ranked among the top 10 features for classification. Our results suggest that novel behavioural differences between lame and non-lame sheep across all three activities could be used to develop an automated system for lameness detection.

Previous research has shown that sensors monitoring lying behaviours and feeding can detect early signs of ill health in calves. There is evidence to suggest that monitoring change in a single behaviour might not be enough for disease prediction. In calves, multiple behaviours such as locomotor play, self-grooming, feeding and activity whilst lying are likely to be informative. However, these behaviours can occur rarely in the real world, which means simply counting behaviours based on the prediction of a classifier can lead to overestimation. Here, we equipped thirteen pre-weaned dairy calves with collar-mounted sensors and monitored their behaviour with video cameras. Behavioural observations were recorded and merged with sensor signals. Features were calculated for 1–10-s windows and an AdaBoost ensemble learning algorithm implemented to classify behaviours. Finally, we developed an adjusted count quantification algorithm to predict the prevalence of locomotor play behaviour on a test dataset with low true prevalence (0.27%). Our algorithm identified locomotor play (99.73% accuracy), self-grooming (98.18% accuracy), ruminating (94.47% accuracy), non-nutritive suckling (94.96% accuracy), nutritive suckling (96.44% accuracy), active lying (90.38% accuracy) and non-active lying (90.38% accuracy). Our results detail recommended sampling frequencies, feature selection and window size. The quantification estimates of locomotor play behaviour were highly correlated with the true prevalence (0.97; p < 0.001) with a total overestimation of 18.97%. This study is the first to implement machine learning approaches for multi-class behaviour identification as well as behaviour quantification in calves. This has potential to contribute towards new insights to evaluate the health and welfare in calves by use of wearable sensors.

Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to “concept drift”, which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions.

Simple SummaryVery little is known about where our pet rabbits come from: Who the breeders are, how good/or bad the conditions are that breeding rabbits are kept in, or whether breeders are being monitored by local authorities. This study aimed to bring to light information on breeding rabbits and breeders in the UK. Several methods of data collection were used combining data from online sales adverts, with a breeder survey and a council freedom of information request. From 3446 online rabbit sale adverts we found 94.5% of adverts were from England and only 1% of breeders were licenced. Out of 33 breeders surveyed, 51.5% provided smaller housing than recommended and housed most rabbits singly, against recommendations, and males were most likely to be housed singly, in too small conditions. However, most provided toys and a diet compliant with recommended guidelines. The most commonly sold/bred rabbits were breeds with flat-faces, which can cause significant health and well-being problems. A freedom of information request sent to 10% of UK councils revealed inconsistency in licensing conditions and confusion about eligibility. Without appropriate guidelines for housing and husbandry and regulation, rabbits within the pet rabbit breeding industry are at risk of compromised welfare. AbstractConditions of pet rabbit breeding colonies and breeder practices are undocumented and very little is known about the pet rabbit sales market. Here, multiple methods were employed to investigate this sector of the UK pet industry. A freedom of information request sent to 10% of councils revealed confusion and inconsistency in licensing conditions. Data from 1-month of online sale adverts (3446) identified 646 self-declared breeders, of which 1.08% were licensed. Further, despite veterinary advice to vaccinate rabbits from five weeks, only 16.7% rabbits were vaccinated and 9.2% of adult rabbits were neutered. Thirty-three breeders completed a questionnaire of which 51.5% provided smaller housing than recommended, the majority housed rabbits singly and bucks were identified as most at risk of compromised welfare. However, most breeders provided enrichment and gave a diet compliant with recommended guidelines. Mini-lops and Netherland dwarfs were the most commonly sold breeds, both of which are brachycephalic, which can compromise their health and wellbeing. From sales data extrapolation, we estimate that 254,804 rabbits are purposefully bred for the UK online pet sales market each year. This data is the first of its kind and highlights welfare concerns within the pet rabbit breeding sector, which is unregulated and difficult to access.