Classifying the posture and activity of ewes and lambs using accelerometers and machine learning on a commercial flock

This is the final version. Available on open access from Elsevier via the DOI in this recordData Availability: A censored version of the data is available upon request.Early decision making in commercial livestock systems is key to maximising animal welfare and production. Detailed information on an animal’s phenotype is needed to facilitate this, but can be difficult to obtain in a commercial setting. Research into the use of bio-logging on sheep to continuously monitor individual behaviour and indirectly inform health and production has seen rapid growth in recent years. Much of this research, however, has been conducted on small numbers of animals in an experimental setting and over limited time periods. Previous studies have also focused on ewes and there has been little research on the potential of bio-logging for collecting behavioural data on lambs, despite clear potential relevance for production. The present study aimed to test the feasibility of deploying accelerometers on a commercial sheep flock at a key point in the annual production cycle (lambing), to validate the viability of automated monitoring of sheep behaviour in a commercial setting. Also, we aimed to develop robust machine learning algorithms that can classify both the posture and physical activity of adult sheep and lambs. We used a Random Forest machine learning algorithm to predict: two mutually exclusive postures in ewes and lambs (standing and lying), achieving average accuracies of 83.7% and 85.9% respectively; four mutually exclusive activities in ewes (grazing, ruminating, inactive and walking), achieving an average accuracy of 70.9%; and three mutually exclusive activities in lambs (inactive, suckling, walking), achieving an average accuracy of 80.8%. These performance accuracies on large numbers of individuals afford the opportunity to provide a detailed understanding of the daily activity budget of ewes and lambs. Monitoring changes in daily patterns across the annual production cycle while capturing changes in environmental conditions such as weather, day length, terrain and management could reveal key indicator metrics that may inform production and health and provide early warning systems for key issues in commercial flocks.Biotechnology & Biological Sciences Research Council (BBSRC

Behavioral fingerprinting: Acceleration sensors for identifying changes in livestock health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals

Comparison of grazing behaviour of sheep on pasture with different sward surface heights using an inertial measurement unit sensor

Grazing is the most important activity that ruminant livestock undertake daily. A number of studies have used motion sensors to study the grazing behaviour of ruminant livestock. However, few have attempted to validate their approaches against various sward surface heights (SSH). The objectives of our study were to: (1) identify and compare the effects of different SSH on the grazing behaviour of sheep by analyzing data collected by a collar mounted Inertial Measurement Unit (IMU) sensor; (2) calculate the relative importance of the extracted features on grazing identification and compare the consistency of the selected features across various SSH; (3) validate the robustness by using classifiers trained from the dataset with specific SSH to distinguish the grazing activity on the datasets from different SSH; and (4) visualize the classification results of grazing versus non-grazing activities on various SSH. Linear Discriminant Analysis (LDA) was chosen as the classification method, while Probabilistic Principal Component Analysis (PPCA) was used to reduce dimensionality of the feature space for visualization of the results. Experimental results revealed that (1) our approach achieved high classification accuracy of grazing behaviour (over 95%) on all the epochs regardless of SSH; (2) Mean of accelerometer Z-axis, Entropy of accelerometer Y-axis, Entropy of accelerometer Z-axis, Mean of gyroscope X-axis and Mean of gyroscope Y-axis were the top 5 features that contributed most in classifying the grazing versus non-grazing activities and there were consistent trends in features across the three SSH; (3) there was enough robustness when the trained LDA classifier on a specific SSH was used to classify behaviour on different SSH; and (4) there existed a clear linear boundary between the data points representing grazing and those of non-grazing behaviour. Overall, our research confirmed that IMU sensors can be a very effective tool for identifying the grazing behaviour of sheep and there is enough robustness to use a trained LDA classifier on a specific pasture SSH to classify grazing behaviour at different SSH pastures

Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data

peer-reviewedPrecision Technologies are emerging in the context of livestock farming to improve management practices and the health and welfare of livestock through monitoring individual animal behaviour. Continuously collecting information about livestock behaviour is a promising way to address several of these target areas. Wearable accelerometer sensors are currently the most promising system to capture livestock behaviour. Accelerometer data should be analysed properly to obtain reliable information on livestock behaviour. Many studies are emerging on this subject, but none to date has highlighted which techniques to recommend or avoid. In this paper, we systematically review the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants. Our review is based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies, namely (1) Data Collection, (2) Data Pre-Processing and (3) Model Development, with different techniques used at each of the 3 steps. The aim of this review is thus to (i) summarise the predictive performance of models and point out the main limitations of the 3-step methodology, (ii) make recommendations on a methodological blueprint for future studies and (iii) propose lines to explore in order to address the limitations outlined. This review shows that the 3-step methodology ensures that several major ruminant behaviours can be reliably predicted, such as grazing/eating, ruminating, moving, lying or standing. However, the areas faces two main limitations: (i) Most models are less accurate on rarely observed or transitional behaviours, behaviours may be important for assessing health, welfare and environmental issues and (ii) many models exhibit poor generalisation, that can compromise their commercial use. To overcome these limitations we recommend maximising variability in the data collected, selecting pre-processing methods that are appropriate to target behaviours being studied, and using classifiers that avoid over-fitting to improve generalisability. This review presents the current situation involving the use of sensors as valuable tools in the field of behaviour recording and contributes to the improvement of existing tools for automatically monitoring ruminant behaviour in order to address some of the issues faced by livestock farming

Sensor inteligente para monitorización de animales con técnicas de bajo consumo

El estudio del comportamiento animal permite conocer los mecanismos de supervivencia de las especies, analizar su bienestar e incluso detectar enfermedades. Tradicionalmente, este estudio ha sido realizado por parte del personal Veterinario, a través de la observación del animal. Sin embargo, esta tarea supone una gran inversión de tiempo.En este Trabajo Fin de Máster se ha desarrollado un sensor inteligente adherido al cuerpo del animal que permite obtener una descripción precisa y constante de sus movimientos. Este tipo de dispositivos se conocen habitualmente como wearables, y suelen presentar un gran inconveniente en términos de duración de la batería, que limita muchas veces su implementación. Como respuesta a este problema, en este proyecto se utilizan diversas técnicas y tecnologías que permiten minimizar el consumo energético, como por ejemplo realizar la lectura del movimiento del animal mientras los procesadores del sensor están dormidos.La información inercial recabada por el sensor se envía a un servidor externo situado en Internet, lo que se conoce habitualmente como la “nube”. Con la finalidad de monitorizar a medio plazo un número elevado de animales en un espacio reducido, se ha seleccionado la tecnología WiFi para el envío.Como protocolo de nivel de aplicación y formato de los datos enviados se han utilizado MQTT y SenML, respectivamente. Ambos son estándares de IoT y permiten, entre otras cosas, reducir el consumo y asegurar la interoperabilidad con otras aplicaciones. El almacenamiento de la información enviada se produce en una base de datos de series temporales. A través de Internet, es posible acceder a ella para su representación y futuro procesado.Para la validación del sistema propuesto al completo se han diseñado dos prototipos del sensor inteligente: uno inicial para la experimentación en un escenario controlado con el movimiento del cuerpo humano, y otro más compacto en forma de placa de circuito impreso para la monitorización de las ovejas de la Facultad de Veterinaria de la Universidad de Zaragoza.<br /

Towards the improvement of sheep welfare: Exploring the use of qualitative behavioural assessment (QBA) for the monitoring and assessment of sheep

Challenges faced by sheep in Australia in terms of disease, injury and management may compromise not only health and productivity but also welfare. These challenges represent a growing concern for both producers and the public. Hence there is an obvious need for the development of measures to allow producers, who may have limited access to stock or are constrained by time and/or resource availability, to monitor their sheep. There is a clear benefit to producers being able to readily identify animals whose welfare might be compromised and thus are in need of further care. However, the assessment of animal welfare is challenging under commercial conditions and to date, few measures are available to help producers recognise animals in compromised welfare states. Qualitative behavioural assessment (QBA) is an approach that captures the expressive behaviour of an animal, through the integration and summary of details of behavioural events, posture, and movement. In this way, QBA represents a valuable tool that offers insight into the physical and physiological aspects of animal welfare, and when used in conjunction with other key measures helps to provide a more complete and comprehensive picture of an animal’s welfare state. Furthermore, QBA should be used together with other welfare measures, where it has been proposed to guide the interpretation of welfare data. As a welfare tool, QBA has been applied to assess the behavioural expression in numerous livestock species including pigs and cattle, however, this methodology is less well studied in sheep and more work is needed to validate QBA for practical application. The aim of the research described in this thesis was to investigate whether the QBA methodology could be applied to assess the welfare of sheep subject to various welfare issues relevant to the Australian sheep industry. To this end, over four experimental chapters, QBA was applied to video footage captured of sheep in various states of compromised welfare, including those suffering from common injury and diseases; lameness, inappetence, flystrike, and gastro-intestinal parasitism, and those experiencing pain caused by routine husbandry procedures (ear tagging, castration, mulesing, and tail docking). Moreover, in two experimental chapters (Chapters 4 & 6), video footage was captured of sheep in positive welfare states (reduced gastro-intestinal parasite burden, and habituation to human presence). This video footage was also analysed quantitatively and other welfare measures including those of health/disease status, physical condition and locomotive activity were collected for validation purposes in each study. Over four experimental chapters, it was demonstrated that observers, blind to experimental procedures and treatments, can reach a significant consensus in their interpretation and assessment of the behavioural expression of sheep, and that these assessments can relate meaningfully to the welfare state of the animal. In Chapter 3, observers were able to distinguish between flystruck and non-flystruck sheep using the QBA methodology, and the behavioural expression scores given to each sheep corresponded to the severity of strike and the condition of the wool. In Chapter 4, observers identified differences in the behavioural expression of sheep that related to the severity of gastro-intestinal parasitism (subclinical v. clinical). Moreover, it was discovered that the treatment of sheep to lessen gastro-intestinal parasite burden altered the behavioural expression of parasitised sheep. A significant consensus was also reached amongst observers in the assessment of lambs subject to routine husbandry procedures (ear tagging, castration, mulesing, and tail docking) in Chapter 5. Observers were able to distinguish lambs that were subject to these painful husbandry procedures and were administered either a placebo or analgesics (Tri-Solfen® and meloxicam), from the control lambs which were only restrained. Hence suggesting that the pain caused by these husbandry procedures alters the behavioural patterns and demeanour of lambs in a way that is identifiable to observers using the QBA methodology. Lastly, when observers viewed video footage of sheep traversing a walk-over-weigh (WoW) apparatus in Chapter 6, they were able to distinguish sheep that were either lame or habituated to the test apparatus and human presence, from the control animals. However, in this Chapter, observers were not able to distinguish between all treatment groups evaluated based on their behavioural expression, specifically differences in the demeanour of inappetent and control sheep was not evident, nor were observers able to distinguish between lame and habituated sheep. In summary, the research presented in this thesis indicates that assessments of behavioural expression can be used under most of those conditions investigated to distinguish sheep in poor welfare states due to injury or disease, from those that are healthy. Furthermore, it appears that observers can reliably identify differences in behavioural expression related to positive welfare states. This work has detailed the behavioural expression of sheep as perceived by observers and has led to a greater understanding of the behavioural expression of sheep in different welfare states. It appears that through the assessment of demeanour or body language, QBA offers both relevant and valid assessments which may help producers gain an insight into the welfare state of their sheep. It is suggested that when used in conjunction with other select behavioural measures, QBA may represent a valuable tool for producers to improve the welfare of sheep in their care

Sensor-based approaches to monitoring the behaviour, health, and welfare of dairy calves

Currently, the detection of ill health in UK farmed calves is based on intermittent visual observation which is subjective and poorly accurate. Sensor-based monitoring may offer an improved alternative. For example, sensors could be used to monitor behaviour and detect signs of ill health in calves. However, substantial individual variation exists for many behaviours, the extent of which is poorly understood in calves. Here, within- and between- individual variation in calf feeding behaviours are quantified using data from computerised milk feeders. Results show that substantial, temporally stable individual differences exist. In addition, the average behavioural expression of two distinct feeding behaviours were positively and significantly correlated and the between-individual differences observed were shown to be consistent over time and context, and to be associated with weight gain. This improves our understanding of normal variation in calf feeding behaviour, which could be helpful in detecting potential behavioural changes indicative of ill health. Machine learning models were trained and tested using feeding data from computerised milk feeders to detect ill health. In a separate study, a similar methodology was used to detect ill health using reticulo-rumen temperature boluses. Results indicate low and moderate predictive performance, respectively. Study limitations and areas for future research are discussed. Finally, the development of novel technologies to enable a more holistic approach to behavioural monitoring in calves is explored. Results show that signals from a single collar-based sensor can be used to accurately detect nine different behaviours as well as to quantify rarely occurring behaviours, such as locomotor play. Quantifying play behaviour could provide a useful indicator of positive welfare in calves. It is also shown that these behaviours can be detected using computer vision, but that further work is needed to enable generalisation to new camera angles and scenes. Overall, this thesis highlights the potential of sensor-based technologies to improve our understanding of behavioural variation in calves, as well as to monitor a greatly more diverse range of behaviours than previously attempted. It is hoped that this work will contribute towards the improvement of health and welfare in calves

Sensor-based approaches to monitoring the behaviour, health, and welfare of dairy calves

Currently, the detection of ill health in UK farmed calves is based on intermittent visual observation which is subjective and poorly accurate. Sensor-based monitoring may offer an improved alternative. For example, sensors could be used to monitor behaviour and detect signs of ill health in calves. However, substantial individual variation exists for many behaviours, the extent of which is poorly understood in calves. Here, within- and between- individual variation in calf feeding behaviours are quantified using data from computerised milk feeders. Results show that substantial, temporally stable individual differences exist. In addition, the average behavioural expression of two distinct feeding behaviours were positively and significantly correlated and the between-individual differences observed were shown to be consistent over time and context, and to be associated with weight gain. This improves our understanding of normal variation in calf feeding behaviour, which could be helpful in detecting potential behavioural changes indicative of ill health. Machine learning models were trained and tested using feeding data from computerised milk feeders to detect ill health. In a separate study, a similar methodology was used to detect ill health using reticulo-rumen temperature boluses. Results indicate low and moderate predictive performance, respectively. Study limitations and areas for future research are discussed. Finally, the development of novel technologies to enable a more holistic approach to behavioural monitoring in calves is explored. Results show that signals from a single collar-based sensor can be used to accurately detect nine different behaviours as well as to quantify rarely occurring behaviours, such as locomotor play. Quantifying play behaviour could provide a useful indicator of positive welfare in calves. It is also shown that these behaviours can be detected using computer vision, but that further work is needed to enable generalisation to new camera angles and scenes. Overall, this thesis highlights the potential of sensor-based technologies to improve our understanding of behavioural variation in calves, as well as to monitor a greatly more diverse range of behaviours than previously attempted. It is hoped that this work will contribute towards the improvement of health and welfare in calves