Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

The groundbreaking impact of digitalization and artificial intelligence in sheep farming

The integration of digitalization and Artificial Intelligence (AI) has marked the onset of a new era of efficient sheep farming in multiple aspects ranging from the general well-being of sheep to advanced web-based management applications. The resultant improvement in sheep health and consequently better farming yield has already started to benefit both farmers and veterinarians. The predictive analytical models embedded with machine learning (giving sense to machines) has helped better decision-making and has enabled farmers to derive most out of their farms. This is evident in the ability of farmers to remotely monitor livestock health by wearable devices that keep track of animal vital signs and behaviour. Additionally, veterinarians now employ advanced AI-based diagnostics for efficient parasite detection and control. Overall, digitalization and AI have completely transformed traditional farming practices in livestock animals. However, there is a pressing need to optimize digital sheep farming, allowing sheep farmers to appreciate and adopt these innovative systems. To fill this gap, this review aims to provide available digital and AI-based systems designed to aid precision farming of sheep, offering an up-to-date understanding on the subject. Various contemporary techniques, such as sky shepherding, virtual fencing, advanced parasite detection, automated counting and behaviour tracking, anomaly detection, precision nutrition, breeding support, and several mobile-based management applications are currently being utilized in sheep farms and appear to be promising. Although artificial intelligence and machine learning may represent key features in the sustainable development of sheep farming, they present numerous challenges in application

Study the Automated System for Tracking the Movement Of Feeding Cows in the Cloud

Nowadays, in order to improve productivity and make sure that animals are healthy, it is crucial to use current technology in agriculture. A revolutionary technology that integrates the IoT, cloud, data analytics, & automated feeding cow tracking in the cloud is a game-changer for managing and overseeing cattle operations on a daily basis. Many tasks related to cattle raising are still done by hand on farms today. Specifically, most farms depend on the farmer's eye for animal health rather than on machinery. It is possible for managers to forecast the health of farm animals based on data collected from monitoring their behavior. We present a WSN-based livestock monitoring system in this article. With the use of internet of things (IoT) devices and cloud computing, the suggested system can keep tabs on livestock. The livestock's movements were tracked by attaching IoT collars on their necks. By uploading data from livestock observation systems to cloud platforms, farming managers can keep tabs on real-time data. We found out through testing that the suggested method can keep tabs on farm animals in real time

A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming

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

Sub-GHz Wrist-Worn Antennas for Wireless Sensing Applications: A Review

With recent advances in wearable wrist-worn wireless sensing applications, the demand for smartwatches and wristbands is rapidly increasing due to their widespread adoption in applications such as smart health monitoring, security, and fitness tracking. Currently, these devices primarily operate in the 2.45 GHz band, leveraging the availability of Bluetooth and Wi-Fi wireless technologies. However, the use of Sub-GHz frequencies (e.g., 433 MHz, 868 MHz, 915 MHz, 923 MHz) for wearable systems has also gained interest due to the emergence of wireless technologies like long-range wide area network (LoRaWAN), narrowband-IoT (NB-IoT) and Sigfox, which offer the potential for long-range wireless communications and sensing applications. In recent times, there has been a notable surge in the commercial production of a variety of Sub-GHz wrist-worn wireless sensing devices for health monitoring and tracking applications. Nevertheless, communications at Sub-GHz frequencies present significant challenges in antenna design, primarily due to the practical size constraints of wrist-worn devices and the necessity for using electrically small antennas. This paper meticulously reviews wrist-worn Sub-GHz antennas reported in the literature, analyzing key antenna parameters such as antenna topology, size, impedance bandwidth, peak realized gain, radiation efficiency, and specific absorption rate (SAR). Additionally, it underlines antenna design challenges, limitations, current trends, and presents potential future perspectives. To the best of the author’s knowledge, there is currently no existing literature comprehensively reviewing Sub-GHz wrist-worn antennas. Therefore, this paper represents the inaugural effort to provide a comprehensive review in this specific domain

Real-time automatic integrated monitoring of barn environment and dairy cattle behaviour: Technical implementation and evaluation on three commercial farms

Due to increasing herd sizes and automation on dairy farms there is an important need for automated monitoring of cow production, health, and welfare. Despite much progress in automatic monitoring techniques, there is still a need to integrate data from multiple sources to create a comprehensive overview and accurate diagnosis of a cow’s state. To aid the technological development of data integration, a prototype of an open and customizable automatic system that integrates data from multiple sensors relating to barn environment and cow behaviour was developed. The system integrates data from sensors that measure barn climate (e.g., temperature, humidity, wind speed), air quality (e.g., CO2 concentration), water use and temperature, the moisture and temperature of the litter and cow behaviour (e.g., lying, eating, ruminating). An external weather system and video recording system are also included. The system’s architecture consists of four main elements: sensors, nodes, gateways, and backend. The data are recorded by sensors, then locally processed on custom-developed sensor nodes, and then transmitted via radio channels to local gateways that combine the data from multiple nodes and transmit them to distributed digital storage (“the cloud”) via a 3G/4G cellular network. On the cloud, the data are further processed and stored in a database. The data are then presented to the user continuously and in real time on a dashboard that can be accessed via the internet. In the design of the local wireless network, care was taken to avoid data packet collision and thus to minimize data loss. To test the system’s performance, the system was installed and operated on three commercial dairy cattle farms for one year. The system provided high data stability with minimal loss and outliers, showing that the system is reliable and suitable for long term application on commercial dairy farms. The system’s architecture, communication network, and data processing and visualization applications form an open framework for research and development purposes, allowing it to be customized and fine-tuned before being deployed as a management assistant on commercial dairy farms. Missing elements that should be added in the future are the integration of the data from the milking parlour and cow identification. Algorithms to integrate information from multiple sensors can be added to provide a comprehensive system that monitors all aspects related to cow welfare, health, and production automatically, remotely and in real time, thereby supporting farmers in important management decision-making

Seven Years after the Manifesto: Literature Review and Research Directions for Technologies in Animal Computer Interaction

As technologies diversify and become embedded in everyday lives, the technologies we expose to animals, and the new technologies being developed for animals within the field of Animal Computer Interaction (ACI) are increasing. As we approach seven years since the ACI manifesto, which grounded the field within Human Computer Interaction and Computer Science, this thematic literature review looks at the technologies developed for (non-human) animals. Technologies that are analysed include tangible and physical, haptic and wearable, olfactory, screen technology and tracking systems. The conversation explores what exactly ACI is whilst questioning what it means to be animal by considering the impact and loop between machine and animal interactivity. The findings of this review are expected to form the first grounding foundation of ACI technologies informing future research in animal computing as well as suggesting future areas for exploratio