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

A concept for application of integrated digital technologies to enhance future smart agricultural systems

Future agricultural systems should increase productivity and sustainability of food production and supply. For this, integrated and efficient capture, management, sharing, and use of agricultural and environmental data from multiple sources is essential. However, there are challenges to understand and efficiently use different types of agricultural and environmental data from multiple sources, which differ in format and time interval. In this regard, the role of emerging technologies is considered to be significant for integrated data gathering, analyses and efficient use. In this study, a concept was developed to facilitate the full integration of digital technologies to enhance future smart and sustainable agricultural systems. The concept has been developed based on the results of a literature review and diverse experiences and expertise which enabled the identification of stat-of-the-art smart technologies, challenges and knowledge gaps. The features of the proposed solution include: data collection methodologies using smart digital tools; platforms for data handling and sharing; application of Artificial Intelligent for data integration and analysis; edge and cloud computing; application of Blockchain, decision support system; and a governance and data security system. The study identified the potential positive implications i.e. the implementation of the concept could increase data value, farm productivity, effectiveness in monitoring of farm operations and decision making, and provide innovative farm business models. The concept could contribute to an overall increase in the competitiveness, sustainability, and resilience of the agricultural sector as well as digital transformation in agriculture and rural areas. This study also provided future research direction in relation to the proposed concept. The results will benefit researchers, practitioners, developers of smart tools, and policy makers supporting the transition to smarter and more sustainable agriculture systems

SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms

We propose a novel energy-aware federated learning (FL)-based system, namely SusFL, for sustainable smart farming to address the challenge of inconsistent health monitoring due to fluctuating energy levels of solar sensors. This system equips animals, such as cattle, with solar sensors with computational capabilities, including Raspberry Pis, to train a local deep-learning model on health data. These sensors periodically update Long Range (LoRa) gateways, forming a wireless sensor network (WSN) to detect diseases like mastitis. Our proposed SusFL system incorporates mechanism design, a game theory concept, for intelligent client selection to optimize monitoring quality while minimizing energy use. This strategy ensures the system's sustainability and resilience against adversarial attacks, including data poisoning and privacy threats, that could disrupt FL operations. Through extensive comparative analysis using real-time datasets, we demonstrate that our FL-based monitoring system significantly outperforms existing methods in prediction accuracy, operational efficiency, system reliability (i.e., mean time between failures or MTBF), and social welfare maximization by the mechanism designer. Our findings validate the superiority of our system for effective and sustainable animal health monitoring in smart farms. The experimental results show that SusFL significantly improves system performance, including a $10\%$ reduction in energy consumption, a $15\%$ increase in social welfare, and a $34\%$ rise in Mean Time Between Failures (MTBF), alongside a marginal increase in the global model's prediction accuracy

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Sheep management and production enhancement are difficult for farmers due to the lack of dynamic response and poor welfare of the sheep. Poor welfare needs to be mitigated, and each farm must receive an expert-level assessment of critical importance. To mitigate poor welfare, researchers have conducted machine learning-based studies to automate the sheep health behavior monitoring process instead of using manual assessment. However, failure to recognize some sheep health behaviors degrades the performance of the model. In addition, behavior challenges, parameters, and analysis must be considered when conducting a study based on machine learning. In this paper, we discuss the different challenges: what are the parameters of the sheep health behaviors, and how to analyze the sheep health behaviors for automated machine learning systems to be helpful in the long term? The hypothesis is based on a different review of the literature of precision-based animal welfare monitoring systems with the potential to improve management and production.info:eu-repo/semantics/publishedVersio

Дигитално управление на технологични процеси в говедовъдни ферми. Oбзор

The article evaluates the development of the Internet of Things (I₀T), digital technologies, various types of biological and biometric sensors and blockchain technologies in dairy and beef cattle breeding. The peculiarities, tendencies and perspectives for digital transformation and digitalization of the cattle farms and complexes have been studied. Precise technologies (PFL) make it possible to collect a sufficient cloud of data in accordance with the physiological and technological requirements of the various categories of animals of the species Bos taurus and the welfare of cattle. Biological and biometric sensors help farmers to increase the quantity and improve the quality of their products. Blockchain technologies present cattle breeding in detail, as transparent, stable and predictable in the eyes of the consumer. Cattle breeding is a sub-sector of animal husbandry in which there is no integration, but flexible digital management is applied.В статията е направена оценка на развитието на Интернет на нещата(I₀T), цифрови технологии, различни видове биологични и биометрични сензори и блокчеин технологии в млечното и месодайно говедовъдство. Проучени да особеностите, тенденциите и перспективите за цифрова трансформация и дигитализация на говедовъдните ферми и комплекси. Прецизните технологии (PFL) позволяват да се събере достатъчен облак от данни, съобразен с физиологичните и технологичните изисквания на различните категории животни на вида Bos taurus и хуманно отношение към говедата. Биологичните и биометрични сензори съдействат на фермерите да увеличат количеството и да усъвършенстват качеството на произведената продукция. Блокчейн технологиите представят детайлно говедовъдството, като прозрачно, стабилно и предвидимо в очите на потребителя. Говедовъдството е подотрасъл на животновъдството, в който липсва интеграция, но се прилага гъвкаво дигитално управление

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity

The growing demand for chicken production has emphasized the importance of maintaining optimal conditions to improve quality and productivity.The integration of Artificial Intelligence (AI) and the Internet of Things(IoT) is recommended for the efficient management of the farm's environment. A potential solution is presented in this paper, utilizing IoT-based sensor nodes with ARM Cortex M3 - LPC 1769 and LORA technology to monitor chicken farms across diverse regions.The proposed solution incorporates a low-cost edge computing server-Jetson Nano device equipped with a machine learning model to categorize and monitor live environmental conditions in poultry farms. Real-time data from various branches is collected and analyzed using machine learning classification techniques including logistic regression, K nearest neighbors, and support vector machines.The performance of these algorithms is compared to identify the most effective approach. Upon evaluation, the K nearest neighbors emerges as the superior performer, achieving an impressive accuracy of 99.72% and an execution duration of 0.087 seconds on the Jetson Nano edge computing device. This cost-effective technology is tailored for small businesses in regions where farmers can gain valuable insights from data-driven decisions and closely monitor their operations. By incorporating AIoT into farm management, the challenges faced by small-scale poultry farming can be addressed, empowering farmers with enlightened techniques to improve overall productivity and quality

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

Smart Sensing Applications in the Agriculture and Food Industry

This chapter is structured in three main sections: an introduction to the smart concept and smart quality control, a review of the state of the art in integrated sensors, embedded systems, and the third one which is dedicated to a review of three case studies. The case studies refer to three results lines that are under taken by the LPF-TAGRALIA in the field of smart sensing. It provides examples of how to develop smart capabilities within standard low cost sensors. A variety of smart capabilities have been selected such as dynamic analysis of physical magnitudes, transmission diagnosis and such reliability and a full range of examples of analytical models of wood drying that can be incorporated to sensor chips to enhance sensor performs and to enable the term smart sensor. Each of the three sections of the chapter is independent and so the reader can decide where to start from according to their particular expertise. For unfamiliar readers with smart technologies, all of them might be of interest, while for experienced readers in the subject the case studies directly are probably the most relevant issue

Precision livestock farming towards broiler welfare

Due to intensification of the livestock system the ratio between number of broilers and number of farmers have been increasing, making impossible the individualized attention to animals without the use of appropriate tools. Increasingly societal concern on broiler welfare requires farmers to find means to improve animal welfare level. Precision livestock farming (PLF) emerges as a possible solution as it enables the monitoring of animals and its environment 24/7. The present study aims to provide information on how PLF technologies can address broiler welfare and to evaluate reasons for their adoption (or non-adoption) by farmers. The results discussions and analysis are based in the three main pillars that guide the present research: animal welfare, PLF technologies and innovation adoption. Methodologically, the study consists of two different steps. Initially, a systematic review of the literature was carried out to identify which are the PLF technologies related to broiler welfare and to assess how they address birds ́ welfare. Results indicate that most PLF technologies are related to image analysis and mainly focused on broiler health improvements. In the second stage, an empirical research was carried out with broiler farmers in the Southern Brazil. From this survey, information on broiler farmers ́ opinions towards broiler welfare and PLF potentialities were assessed as well as on the determinants and limiting factors for technologies adoption. In general, Brazilian broiler farmers attribute great importance to broiler welfare and perceive the current level of welfare as high; however higher scores for importance than for perception indicate that there is room for welfare improvements. In broiler farmers ́ opinions, providing animals food/water and good housing and health conditions are more important than provide means for the animals to express their natural behaviors. Broiler farmers believe that technologies can help them on welfare improvements and are willing to adopt them even when no extra income come from this. Broiler farmers with less experience, producing chicken grillers, having other farm activity besides broiler production and presenting high beliefs on PLF potentialities regarding animal welfare improvements are more likely to adopt PLF technologies. Major limiting factors for PLF technologies adoption are regarding technology high prices, maintenance requirements and to possible financial consequences with technical problems. It is expected the present thesis to be useful to clarify about PLF technologies opportunities in the broiler farmers point of view and that the results obtained to be valuable to increase PLF adoption, which can potentially improve animal and farmers welfare alike.A intensificação do sistema produtivo aumentou a relação entre o número de frangos de corte e o número de trabalhadores rurais, impossibilitando a atenção individualizada aos animais sem o uso de ferramentas adequadas. Em paralelo, a sociedade pressiona os produtores a encontrarem meios para aumentar o nível bem-estar animal (BEA). Tecnologias da zootecnia de precisão (ZP)surgem como possívelsolução, pois possibilitam o monitoramento dos animais e de seu ambiente de forma contínua. O presente estudo objetiva fornecer informações sobre como as tecnologias da ZP abordam o bem-estar de frangos de corte e avaliar os fatores que influenciam a sua adoção pelos produtores. A discussão e a análise dos resultados baseiam-se em três pilares, a saber: BEA, tecnologias da ZP e adoção de inovações. Metodologicamente, o estudo é composto por duas etapas distintas. Inicialmente, uma revisão sistemática da literatura foi realizada para identificar quais são as tecnologias da ZP relacionadas ao bem-estar de frangos de corte e para avaliar como elas abordam o bem-estar das aves. Os resultados indicam que a maioria das tecnologias está relacionada à análise de imagens e principalmente focada na melhoria da saúde dos frangos. Na segunda etapa, foi realizada uma pesquisa empírica com produtores de frangos de corte no Sul do Brasil. A partir desta pesquisa, foram avaliadas informações sobre as opiniões dos criadores de frangos de corte em relação ao BEA e às potencialidades das tecnologias, bem como sobre os fatores determinantes e limitantes para adoção de tecnologias. Em geral, os avicultores brasileiros atribuem grande importância ao bem-estar dos frangos e consideram alto o nível atual de BEA; no entanto, maiores escores para importância do que para percepção indicam que há espaço para melhorias. Na opinião dos produtores, fornecer aos animais comida/água e boas condições de alojamento e saúde é mais importante do que fornecer meios para que os animais expressem seus comportamentos naturais. Os produtores acreditam que as tecnologias podem ajudá-los a aumentar o BEA e estão dispostos a adotá-las mesmo que isso não resulte em maior renda. Produtores com menos experiência, que produzem grillers, que possuem mais de uma atividade agropecuária e que acreditam nas potencialidades das tecnologias em melhorar o BEA são mais propensos a adotar tecnologias. Os principais fatores limitantes para a adoção de tecnologias são os preços elevados, as exigências de manutenção e as possíveis consequências financeiras com problemas técnicos. Espera-se que a presente tese seja útil para esclarecer sobre as oportunidades da ZP do ponto de vista dos produtores e que os resultados obtidos sejam valiosos para aumentar a adoção de tecnologias, as quais podem melhorar o BEA e o bem-estar dos produtores