Utilization of information and communication technologies to monitor grazing behaviour in sheep

This thesis is a contribution on the study of feeding behaviour of grazing sheep and its general goal was to evaluate the effectiveness of a tri-axial accelerometer based sensor in the discrimination of the main activities of sheep at pasture, the quantification of the number of bites and the estimation of intake per bite. Based on the literature, it has been observed that feed intake at pasture is a difficult parameter to measure with direct observation, for this reason automated systems for monitoring the activities of free-ranging animals have became increasingly important and common. Among these systems, tri-axial accelerometers showed a good precision and accuracy in the classification of behavioural activities of herbivores, but they do not yet seem able to discriminate jaw movements, which are of great importance for evaluating animal grazing strategies in different pastures and for estimating the daily herbage intake. Thus, the main objective of this research was to develop and test a tri-axial accelerometer based sensor (BEHARUM) for the study of the feeding behaviour of sheep and for the estimation of the bite rate (number of bites per min of grazing) on the basis of acceleration variables. The thesis is organized in 4 main chapters. Chapter 1. This introduction section reports a literature review on the importance of studying the feeding behaviour of ruminants and on the measuring techniques developed over the years for its detection, with specific emphasis on accelerometer based sensors, which showed a good precision and accuracy in the classification of behavioural activities of herbivores. Chapter 2. This chapter describes the results of short tests performed in grazing conditions to discriminate three behavioural activities of sheep (grazing, ruminating and resting) on the base of acceleration data collected with the BEHARUM device. The multivariate statistical analysis correctly assigned 93.0% of minutes to behavioural activities. Chapter 3. This part evaluates the effectiveness of the BEHARUM in discriminating between the main behaviours (grazing, ruminating and other activities) of sheep at pasture and to identify the epoch setting (5, 10, 30, 60, 120, 180 and 300 s) with the best performance. Results show that a discriminant analysis can accurately classify important behaviours such as grazing, ruminating and other activities in sheep at pasture, with a better performance in classifying grazing behaviour than ruminating and other activities for all epochs; the most accurate classification in terms of accuracy and Coehn’s k coefficient was achieved with the 30 s epoch length. Chapter 4. This section illustrates the results of a study that aimed to derive a model to predict sheep behavioural variables like number of bites, bite mass, intake and intake rate, on the basis of variables calculated from acceleration data recorded by the BEHARUM. The experiment was carried out using micro-swards of Italian ryegrass (Lolium multiflorum L.), alfalfa (Medicago sativa L.), oat (Avena sativa L.), chicory (Cichorium intibus L.) and a mixture (Italian ryegrass and alfalfa). The sheep were allowed to graze the micro-swards for 6 minutes and the results show that the BEHARUM can accurately estimate with high to moderate precision (r2=0.86 and RMSEP=3%) the number of bites and the herbage intake of sheep short term grazing Mediterranean forages. Finally, the dissertation ends with a summary of the main implications and findings, and a general discussion and conclusions

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

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

Smart Dairy Farming: Innovative Solutions to Improve Herd Productivity

Among the most straining trends that farmers have to face there are: on one side, to guarantee welfare and adequate life conditions for animals and to reduce the environmental footprint, on the other side, to develop new strategies to improve farm management reducing costs. The current conditions and the expected developments of the dairy sector highlight a strong need for more efficient and sustainable farming systems. Studying heat stress, herd management and housing and animals\u2019 productive and reproductive performances is fundamental for the economic and environmental sustainability of the dairy chain. New and effective tools to cope with these challenges have been provided by Precision Livestock Farming (PLF), which is nowadays increasingly applied and makes possible to control quali-quantitative parameters related to production, health, behaviour, and real-time locomotion per animal. The research key challenge is to turn these data into knowledge to provide real-time support in farming optimisation. This research focuses specifically on different systems to collect, process and derive useful information from data on animal welfare and productivity. A multi-disciplinary approach has been adopted to generate a decision support system for farmers

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

Farm Animals’ Behaviors and Welfare Analysis with AI Algorithms: A Review

peer reviewedNumerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors

RumiWatch - Development and assessment of a sensor-based behavior monitoring system for ruminants

Sustainable and competitive milk production is highly dependent on securing the performance potential, health and fertility of dairy cows. Therefore, farmers can benefit from sensor data of animal monitoring systems to improve health management and work processes in dairy farming. The research during this PhD thesis aimed to contribute to the development and evaluation of a scientifically validated, sensor-based animal monitoring system that comprises a device for measurement of ingestive behavior and a device for measurement of movement behavior in cattle that interact as a system with system-specific software. Further aim of this thesis was to evaluate application potentials for this animal monitoring system by means of calving prediction in dairy cows and measurement of chewing activity in horses. The underlying experimental work was structured into four separate studies. The aim of the first study was to develop and validate a novel scientific monitoring device for automated measurement of rumination and eating behavior in dairy cows. Research works for this study aimed to provide a complete and detailed technical specification of the functionality of this device and to perform a validation under field conditions in stable-fed cows. The objective of the second study was to develop and validate a novel algorithm to monitor lying, standing, and walking behavior based on the output of a triaxial accelerometer collected from loose-housed dairy cows. The third study aimed to use automated measurements of ingestive behavior obtained from the developed sensor device to develop and validate a predictive model for calving in dairy cows. The aim of the fourth study was to investigate the suitability and validity of the developed sensor system for automated measurement of chewing activity in horses. In conclusion, the RumiWatch noseband sensor and pedometer that were developed and validated in the current project represent a suitable measuring instrument for automated recording of ingestive and locomotor behavior in dairy cows. The system-specific software is suitable for research purposes and shows a high performance for classification of extended parameters of rumination, eating, lying, standing, and walking behavior. The achieved validation results indicate that the measuring performance satisfies scientific requirements. Further application potentials were demonstrated by means of automated calving prediction in dairy cows and automated measurement of chewing activity in horses. The development and validation of a predictive model for calving time using measurements of the RumiWatch noseband sensor revealed a high amount of false positive alerts that was prohibitive for application of the model in farming practice. However, the analyses showed that particularly parameters of ruminating behavior have predictive value and should be taken into consideration for future research on calving prediction models. Furthermore, it was successfully demonstrated that it is feasible to apply the RumiWatch noseband sensor to horses. The results of direct observation compared with the automatic measurement showed a very high overall agreement of the observed and automatically measured data and, after minor refinements, this measuring device has the potential to become a valuable and easy-to-use tool for equine research and management.Eine nachhaltige und wettbewerbsfähige Milchproduktion erfordert in hohem Masse die Sicherstellung des Leistungspotentials, der Gesundheit und der Fruchtbarkeit von Milchkühen. Sensordaten, die durch technische Monitoringsysteme für die Überwachung des Tierverhaltens generiert werden, können hierbei einen wichtigen Beitrag für die Verbesserung der Arbeitsprozesse und des Gesundheitsmanagement in der Milchviehhaltung leisten. Die Zielsetzung dieses Dissertationsprojekts stellte einen Beitrag zur Entwicklung und wissenschaftlichen Evaluation eines technischen Monitoringsystems für die Tieraktivität dar. Im Rahmen der Forschungsvorhaben sollten Messinstrumente für die Erfassung des Ingestionsverhaltens und des Bewegungsverhaltens entwickelt und wissenschaftlich validiert werden, die unter Hinzunahme einer systemspezifischen Software zum nicht-invasiven, systematischen Gesundheitsmonitoring bei Milchkühen dienen. Zudem sollten Anwendungspotentiale für dieses Monitoringsystem anhand der Abkalbungsvorhersage bei Milchkühen und der Messung der Kauaktivität bei Pferden evaluiert werden. Die zugrundeliegenden experimentellen Arbeiten waren in vier separate Studien unterteilt. Die erste Studie beinhaltete die Entwicklung und Validierung eines neuartigen wissenschaftlichen Messinstruments für die automatisierte Erfassung des Wiederkau- und Futteraufnahmeverhaltens bei Milchkühen. Die Forschungsarbeiten im Rahmen dieser Studie umfassten die Bereitstellung eines umfassenden technischen Funktionsbeschriebs dieses Messinstruments und die Durchführung einer Validierungsstudie unter Praxisbedingungen bei stallgefütterten Milchkühen. Die Zielsetzung der zweiten Studie war die Entwicklung und Validierung eines neuartigen Algorithmus zur Erfassung des Geh-, Steh- und Liegeverhaltens von Milchkühen in Laufstallhaltung basierend auf den Messdaten eines triaxialen Accelerometers. In der dritten Studie wurde die Nutzung von Messdaten des Ingestionsverhaltens zur Entwicklung und Validierung eines Modells für die automatisierte Kalbungsvorhersage beabsichtigt. Ziel der vierten Studie war die Untersuchung der Eignung und Validität des entwickelten Sensorsystems für die automatisierte Erfassung des Kauverhaltens von Pferden. In einer Gesamtbetrachtung ist festzustellen, dass der RumiWatch-Nasebandsensor und das RumiWatch-Pedometer erfolgreich als Messinstrumente für die Erfassung des Ingestions- und Bewegungsverhaltens von Milchkühen entwickelt wurden. Die systemspezifische Software ist für wissenschaftliche Zwecke geeignet und zeigt eine hohe Validität bei der Messung erweiterter Parameter des Wiederkau-, Futteraufnahme-, Geh-, Steh- und Liegeverhaltens. Die erzielte Messgenauigkeit bei der Validierung der beiden Messinstrumente entspricht wissenschaftlichen Ansprüchen. Weitere Anwendungspotentiale wurden anhand der automatisierten Kalbungsvorhersage bei Milchkühen und Messung der Kauaktivität bei Pferden demonstriert. Bei der Entwicklung und Validierung eines Vorhersagemodells für den Kalbezeitpunkt basierend auf Messdaten des RumiWatch-Nasenbandsensors zeigte sich eine hohe Anzahl an Falsch-Positiv-Alarmen, die prohibitiv für die Anwendung des entwickelten Modells in der landwirtschaftlichen Praxis ist. Dennoch konnte gezeigt werden, dass insbesondere Parameter des Wiederkauverhaltens für die zukünftige Erarbeitung von Vorhersagemodellen für den Kalbezeitpunkt herangezogen werden sollten. Zudem konnte der RumiWatch-Nasenbandsensor erfolgreich auch bei Pferden eingesetzt werden. Der Vergleich von Direktbeobachtungen und Sensormessungen zeigte hierbei eine hohe Übereinstimmung zwischen beobachteten und automatisch erfassten Messwerten. Nach geringfügigen Modifikationen ist dem Nasenbandsensor somit auch ein hohes Potential für den Einsatz in Forschung und Praxis bei Pferden zuzuschreiben

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