Fog assisted application support for animal behaviour analysis and health monitoring in dairy farming

With the exponential growth rate of technology, the future of all activities, including dairy farming involves an omnipresence of widely connected devices. Internet of things (IoT), fog computing, cloud computing and data analytics together offer a great opportunity to increase productivity in the dairy industry. In this paper, we present a fog computing assisted application system for animal behaviour analysis and health monitoring in a dairy farming scenario. The sensed data from sensors is sent to a fog based platform for data classification and analysis, which includes decision making capabilities. The solution aims towards keeping track of the animals' well-being by delivering early warning alerts generated through behavioural analytics, thus aiding the farmer to monitor the health of their livestock and the capability to identify potential diseases at an early stage, thereby also helping in increasing milk yield and productivity. The proposed system follows a service based model, avoids vendor lock-in, and is also scalable to add new features such as the detection of calving, heat, and issues like lameness

Technologies for climate change adaptation: agricultural sector

This Guidebook presents a selection of technologies for climate change adaptation in the agricultural sector. A set of twenty two adaptation technologies are showcased that are primarily based on the principals of agroecology, but also include scientific technologies of climate and biological sciences complemented with important sociological and institutional capacity building processes that are required to make adaptation function. The technologies cover monitoring and forecasting the climate, sustainable water use and management, soil management, sustainable crop management, seed conservation, sustainable forest management and sustainable livestock management. Technologies that tend to homogenize the natural environment and agricultural production have low possibilities of success in conditions of environmental stress that are likely to result from climate change. On the other hand, technologies that allow for, and indeed promote, diversity are more likely to provide a strategy which strengthens agricultural production in the face of uncertain future climate change scenarios. In this sense, the twenty two technologies showcased in this Guidebook have been selected because they facilitate the conservation and restoration of diversity while at the same time providing opportunities for increasing agricultural productivity. Many of these technologies are not new to agricultural production practices, but they are implemented based on assessment of current and possible future impacts of climate change in a particular location. Agro-ecology is an approach that encompasses concepts of sustainable production and biodiversity promotion and therefore provides a useful framework for identifying and selecting appropriate adaptation technologies for the agricultural sector. The Guidebook provides a systematic analysis of the most relevant information available on climate change adaptation technologies in the agriculture sector. It has been compiled based on a literature review of key publications, journal articles, and e-platforms, and by drawing on documented experiences sourced from a range of organizations working on projects and programmes concerned with climate change adaptation technologies in the agricultural sector. Its geographic scope is focused on developing countries where high levels of poverty, agricultural production, climate variability and biological diversity currently intersect. Key concepts around climate change adaptation are not universally agreed. It is therefore important to understand local contexts – especially social and cultural norms - when working with national and sub-national stakeholders to make informed decisions about appropriate technology options. Thus, decision-making processes should be participative, facilitated, and consensus-building oriented and should be based on the following key guiding principles: increasing awareness and knowledge, strengthening institutions, protecting natural resources, providing financial assistance and developing context-specific strategies. For decision-making the Community–Based Adaptation framework is proposed for creating inclusive governance that engages a range of stakeholders directly with local or district government and national coordinating bodies, and facilitates participatory planning, monitoring and implementation of adaptation activities. Seven criteria are suggested for the prioritization of adaptation technologies: (i) The extent to which the technology maintains or strengthens biological diversity and is environmentally sustainable; (ii) The extent to which the technology facilitates access to information systems and awareness of climate change information; (iii) Whether the technology support water, carbon and nutrient cycles and enables stable and/or increased productivity; (iv) Income-generating potential, cost-benefit analysis and contribution to improved equity; (v) Respect for cultural diversity and facilitation of inter-cultural exchange; (vi) Potential for integration into regional and national policies and can be scaled-up; (vii) The extent to which the technology builds formal and information institutions and social networks. Finally, recommendations are set out for practitioners and policy makers: • There is an urgent need for improved climate modelling and forecasting which can provide a basis for informed decision-making and the implementation of adaptation strategies. This should include traditional knowledge. • Information is also required to better understand the behaviour of plants, animals, pests and diseases as they react to climate change. • Potential changes in economic and social systems in the future under different climate scenarios should also be investigated so that the implications of adaptation strategy and planning choices are better understood. • It is important to secure effective flows of information through appropriate dissemination channels. This is vital for building adaptive capacity and decision-making processes. • Improved analysis of adaptation technologies is required to show how they can contribute to building adaptive capacity and resilience in the agricultural sector. This information needs to be compiled and disseminated for a range of stakeholders from local to national level. • Relationships between policy makers, researchers and communities should be built so that technologies and planning processes are developed in partnership, responding to producers’ needs and integrating their knowledge

SmartHerd Management: A Microservices Based Fog Computing Assisted IoT Platform towards Data Driven Smart Dairy Farming

Internet of things (IoT), fog computing, cloud computing and data driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing assisted end-to-end IoT platform for animal behaviour analysis and health monitoring in a dairy farming scenario. The platform follows a microservices oriented design to assist the distributed computing paradigm, and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6 month mature real-world deployment, wherein the data from wearables on cows is sent to a fog based platform for data classification and analysis, which includes decision making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog based computational assistance in the SmartHerd setup, we see an 84\% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach

Connected Cows: Utilizing Fog and Cloud Analytics toward Data-Driven Decisions for Smart Dairy Farming

The Internet of Things (IoT) is about connecting people, processes, data, and things, and is changing the way we monitor and interact with things. An active incorporation of information and communication technology coupled with sophisticated data analytics approaches has the potential to transform some of the oldest industries in the world, including dairy farming. It presents a great opportunity for verticals such as the dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. Dairy farms have all the constraints of a modern business -- they have a fixed production capacity, a herd to manage, expensive farm labor, and other varied farm-related processes to take care of. In this technology-driven era farmers look for assistance from smart solutions to increase profitability and to help manage their farms well. We present an end-to-end IoT application system with fog assistance and cloud support that analyzes data generated from wearables on cows' feet to detect anomalies in animal behavior that relate to illness such as lameness. The solution leverages behavioral analytics to generate early alerts toward the animals' well being, thus assisting the farmer in livestock monitoring. This in turn also helps in increasing productivity and milk yield by identifying potential diseases early on. The project specializes in detecting lameness in dairy cattle at an early stage, before visible signs appear to the farmer or an animal expert. Our trial results in a real-world smart dairy farm setup, consisting of a dairy herd of 150 cows in Ireland, demonstrate that the designed system delivers a lameness detection alert up to three days in advance of manual observation

Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle

Timely lameness detection is one of the major and costliest health problems in dairy cattle that farmers and practitioners haven't yet solved adequately. The primary reason behind this is the high initial setup costs, complex equipment and lack of multi-vendor interoperability in currently available solutions. On the other hand, human observation based solutions relying on visual inspections are prone to late detection with possible human error, and are not scalable. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows' health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, we have developed an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage. The proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. Using long-range pedometers specifically designed for use in dairy cattle, we monitor the activity of each cow in the herd. The accelerometric data from these sensors is aggregated at the fog node to form a time series of behavioral activities, which are further analyzed in the cloud. Our hybrid clustering and classification model identifies each cow as either Active, Normal or Dormant, and further, Lame or Non-Lame. The detected lameness anomalies are further sent to farmer's mobile device by way of push notifications. The results indicate that we can detect lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Moreover, with fog based computational assistance in the setup, we see an 84% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach

A module placement scheme for fog-based smart farming applications

As in Industry 4.0 era, the impact of the internet of things (IoT) on the advancement of the agricultural sector is constantly increasing. IoT enables automation, precision, and efficiency in traditional farming methods, opening up new possibilities for agricultural advancement. Furthermore, many IoT-based smart farming systems are designed based on fog and edge architecture. Fog computing provides computing, storage, and networking services to latency-sensitive applications (such as Agribots-agricultural robots-drones, and IoT-based healthcare monitoring systems), instead of sending data to the cloud. However, due to the limited computing and storage resources of fog nodes used in smart farming, designing a modules placement scheme for resources management is a major challenge for fog based smart farming applications. In this paper, our proposed module placement algorithm aims to achieve efficient resource utilization of fog nodes and reduce application delay and network usage in Fog-based smart farming applications. To evaluate the efficacy of our proposal, the simulation was done using iFogSim. Results show that the proposed approach is able to achieve significant reductions in latency and network usage

An adaptive pig face recognition approach using convolutional neural networks

The evolution of agriculture towards intensive farming leads to an increasing demand for animal identification associated with high traceability, driven by the need for quality control and welfare management in agricultural animals. Automatic identification of individual animals is an important step to achieve individualised care in terms of disease detection and control, and improvement of the food quality. For example, as feeding patterns can differ amongst pigs in the same pen, even in homogenous groups, automatic registration shows the most potential when applied to an individual pig. In the EU for instance, this capability is required for certification purposes. Although the RFID technology has been gradually developed and widely applied for this task, chip implanting might still be time-consuming and costly for current practical applications. In this paper, a novel framework composed of computer vision algorithms, machine learning and deep learning techniques is proposed to offer a relatively low-cost and scalable solution of pig recognition. Firstly, pig faces and eyes are detected automatically by two Haar feature-based cascade classifiers and one shallow convolutional neural network to extra high-quality images. Secondly, face recognition is performed by employing a deep convolutional neural network. Additionally, class activation maps generated by grad-CAM and saliency maps are utilised to visually understand how the discriminating parameters have been learned by the neural network. By applying the proposed approach on 10 randomly selected pigs filmed in farm condition, the proposed method demonstrates the superior performance against the state-of-art method with an accuracy of 83% over 320 testing images. The outcome of this study will facilitate the real-application of AI-based animal identification in swine production

Airborne Vision-Based Remote Sensing Imagery Datasets From Large Farms Using Autonomous Drones For Monitoring Livestock

Livestock have high economic value and monitoring of them in large farms regularly is a labour-intensive task and costly. The emergence of smart data on individual animals and their surroundings opens up new opportunities for early detection and disease prevention, better animal care and traceability, better sustainability and farm economics. Precision Livestock Farming (PLF) relies on the constant and automated gathering of livestock data to support the expertise and management decisions made by farmers, vets, and authorities. The high mobility of UAVs combined with a high level of autonomy, sensor-driven technologies and AI decision-making abilities can provide many advantages to farmers in exploiting instant information from every corner of a large farm. The key objectives of this research are to i) explore various drone-mounted vision-based remote sensing modalities, particularly, visual band sensing and a thermal imager, ii) develop UAV-assisted autonomous PLF technologies and ii) collect data with various parameters for the researchers to establish further advanced AI-based approaches for monitoring livestock in large farms effectively by fusing a rich set of features acquired using vision-based multi-sensor modalities. The collected data suggest that the fuse of distinctive features of livestock obtained from multiple sensor modalities can be exploited to help farmers experience better livestock management in large farms through PLF

Lameness Detection as a Service: Application of Machine Learning to an Internet of Cattle

Lameness is a big problem in the dairy industry, farmers are not yet able to adequately solve it because of the high initial setup costs and complex equipment in currently available solutions, and as a result, we propose an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to identify lame dairy cattle. As part of a real world trial in Waterford, Ireland, 150 dairy cows were each fitted with a long range pedometer. The mobility data from the sensors attached to the front leg of each cow is aggregated at the fog node to form time series of behavioral activities (e.g. step count, lying time and swaps per hour). These are analyzed in the cloud and lameness anomalies are sent to farmer’s mobile device using push notifications. The application and model automatically measure and can gather data continuously such that cows can be monitored daily. This means there is no need for herding the cows, furthermore the clustering technique employed proposes a new approach of having a different model for subsets of animals with similar activity levels as opposed to a one size fits all approach. It also ensures that the custom models dynamically adjust as weather and farm condition change as the application scales. The initial results indicate that we can predict lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Index Terms—Lameness, Internet of Things (IoT), Data Analytics, Smart Agriculture, Machine Learning, Micro services, Fog Computing. I