Edge Computing for Cattle Behavior Analysis

peer reviewedSmartphones, particularly iPhone, can be relevantinstruments for researchers because they are widely used aroundthe world in multiple domains of applications such as animalbehavior. iPhone are readily available on the market, containmany sensors and require no hardware development. They areequipped with high performance inertial measurement units(IMU) and absolute positioning systems analyzing user’s move-ments, but they can easily be diverted to analyze likewise thebehaviors of domestic animals such as cattle. Using smartphonesto study animal behavior requires the improvement of theautonomy to allow the acquisition of many variables at a highfrequency over long periods of time on a large number ofindividuals for their further processing through various modelsand decision-making tools. Indeed, storing, treating data at theiPhone level with an optimal consumption of energy to maximizebattery life was achieved by using edge computing on the iPhone.This processing reduced the size of the raw data by 42% onaverage by eliminating redundancies. The decrease in samplingfrequency, the selection of the most important variables andpostponing calculations to the cloud allowed also an increasein battery life by reducing of amount of data to transmit. Inall these use cases, the lambda architectures were used to ingeststreaming time series data from the Internet of Things. Cattle,farm animals’ behavior consumes relevant data from InertialMeasurement Unit (IMU) transmitted or locally stored on thedevice. Data are discharged offline and then ingested by batchprocessing of the Lambda Architecture

Modelagem e persistência de dados sensoriais para a pecuária de precisão: Sensor data for precision livestock: modeling and persistence

Para manter-se competitivo na cadeia de produção bovina, é importante inovar na produção e gestão do agronegócio. Desse modo, surgem alguns desafios, como o aumento da demanda relacionada ao bem-estar animal, a segurança alimentar, a rastreabilidade bovina, a sustentabilidade, entre outros. A pecuária de precisão pode contribuir para atingir esses objetivos. Diversos estudos para a pecuária de precisão são desenvolvidos em parceria entre a Embrapa Gado de Corte e a Faculdade de Computação (FACOM), da Universidade Federal do Mato Grosso do Sul (UFMS). Esses são projetos que possuem uma heterogeneidade de sensores e geram um grande volume de dados provenientes de diferentes fontes e padrões. Este trabalho tem como finalidade definir um modelo de dados semântico, bem como um modelo físico para persistência dos dados oriundos dos diversos sensores e sistemas utilizados na pecuária de precisão, visando à integração e ao compartilhamento padronizado dessas informações

Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms |, |, |, |, |, |, and |. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.U01 OH010841/OH/NIOSH CDC HHSUnited States/U54 OH007544/OH/NIOSH CDC HHSUnited States

An Optimized Kappa Architecture for IoT Data Management in Smart Farming

peer reviewedAgriculture 4.0 is a domain of IoT in full growth which produces large amounts of data from machines, robots, and sensors networks. This data must be processed very quickly, especially for the systems that need to make real-time decisions. The Kappa architecture provides a way to process Agriculture 4.0 data at high speed in the cloud, and thus meets processing requirements. This paper presents an optimized version of the Kappa architecture allowing fast and efficient data management in Agriculture. The goal of this optimized version of the classical Kappa architecture is to improve memory management and processing speed. the Kappa architecture parameters are fine tuned in order to process data from a concrete use case. The results of this work have shown the impact of parameters tweaking on the speed of treatment. We have also proven that the combination of Apache Samza with Apache Druid offers the better performances

Pet sense: sistema de monitorização de animais em hospitalização

The observation and treatment of animals in veterinary hospitals is still very dependent on manual procedures, including the collection of vital signs (temperature, heart rate, respiratory rate and blood pressure). These manual procedures are time-consuming and invasive, affecting the animal’s well-being. In this work, we purpose the use of IoT technologies to monitor animals in hospitalization, wearing sensors to collect vitals, and low-cost hardware to forward them into a cloud backend that analyses and stores data. The history of observed vitals and alarms can be accessed in the web, included in the Pet Universal software suite. The overall architecture follows a stream processing approach, using telemetry protocols to transport data, and Apache Kafka Streams to analyse streams and trigger alarms on potential hazard conditions. The system was fully implemented, although with laboratory sensors to emulate the smart devices to be worn by the animals. We were able to implement a data gathering and processing pipeline and integrate with the existing clinical management information system. The proposed solution can offer a practical way for long-term monitoring and detect abnormal values of temperature and heart rate in hospitalized animals, taking into consideration the characteristics of the monitored individual (species and state).A observação e tratamento de animais hospitalizados continua muito dependente de procedimentos manuais, especialmente no que diz respeito à colheita de sinais vitais (temperatura, frequência cardíaca, frequência respiratória e pressão arterial). Estes procedimentos manuais são dispendiosos em termos de tempo e afetam o bem-estar do animal. Neste projeto, propomos o recurso a tecnologias IoT para monitorizar animais hospitalizados equipados com sensores que medem sinais vitais, com hardware acessível, e envio dos dados para um serviço na cloud que os analisa e armazena. O histórico dos valores e alarmes podem ser acedidos na web e incluídos na plataforma comercial da Pet Universal. A arquitetura geral segue uma abordagem de processamento funcional, usando protocolos de telemetria para transportar dados e Apache Kafka Streams, analisando e lançando alarmes sobre potenciais condições de risco de acordo com a temperatura e pulsação. O sistema foi totalmente implementado, embora com sensores de laboratório para simular os dispositivos a serem usados pelos animais. Conseguimos implementar um circuito de colheita e processamento de dados e integrar com o sistema de gestão clínica já existente. A solução proposta oferece uma forma prática de monitorização continuada e de deteção de valores anormais de temperatura e frequência cardíaca em animais hospitalizados, tomando em conta as características do indivíduo monitorado (espécie e estado).Mestrado em Engenharia Informátic

Internet of Things. Information Processing in an Increasingly Connected World

This open access book constitutes the refereed post-conference proceedings of the First IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2018, held at the 24th IFIP World Computer Congress, WCC 2018, in Poznan, Poland, in September 2018. The 12 full papers presented were carefully reviewed and selected from 24 submissions. Also included in this volume are 4 WCC 2018 plenary contributions, an invited talk and a position paper from the IFIP domain committee on IoT. The papers cover a wide range of topics from a technology to a business perspective and include among others hardware, software and management aspects, process innovation, privacy, power consumption, architecture, applications

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

Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors

Simple Summary Smart farming is a concept of agricultural innovation that combines technological, social, economic and institutional changes. It employs novel practices of technologies and farm management at various levels (specifically with a focus on the system perspective) and scales of agricultural production, helping the industry meet the challenges stemming from immense food production demands, environmental impact mitigation and reductions in the workforce. Precision Livestock Farming (PLF) systems will help the industry meet consumer expectations for more environmentally and welfare-friendly production. However, the overwhelming majority of these new technologies originate from outside the farm sector. The adoption of new technologies is affected by the development, dissemination and application of new methodologies, technologies and regulations at the farm level, as well as quantified business models. Subsequently, the utilization of PLF in the pig and especially the poultry sectors should be advocated (the latter due to the foreseen increase in meat production). Therefore, more significant research efforts than those that currently exist are mainly required in the poultry industry. The investigation of farmers' attitudes and concerns about the acceptance of technological solutions in the livestock sector should be integrally incorporated into any technological development.Abstract More efficient livestock production systems are necessary, considering that only 41% of global meat demand will be met by 2050. Moreover, the COVID-19 pandemic crisis has clearly illustrated the necessity of building sustainable and stable agri-food systems. Precision Livestock Farming (PLF) offers the continuous capacity of agriculture to contribute to overall human and animal welfare by providing sufficient goods and services through the application of technical innovations like digitalization. However, adopting new technologies is a challenging issue for farmers, extension services, agri-business and policymakers. We present a review of operational concepts and technological solutions in the pig and poultry sectors, as reflected in 41 and 16 European projects from the last decade, respectively. The European trend of increasing broiler-meat production, which is soon to outpace pork, stresses the need for more outstanding research efforts in the poultry industry. We further present a review of farmers' attitudes and obstacles to the acceptance of technological solutions in the pig and poultry sectors using examples and lessons learned from recent European projects. Despite the low resonance at the research level, the investigation of farmers' attitudes and concerns regarding the acceptance of technological solutions in the livestock sector should be incorporated into any technological development

Edge AI-IoT Pivot Irrigation, Plant Diseases and Pests Identification

peer reviewedIn a growing population context with less soil and water resources, the irrigated agriculture allows to increase the yield and the production of several crops in order to meet the increase of the food and fibers demands. To be efficient, an irrigation system must correctly evaluate amount of water and moments to which to apply the irrigation doses. Monitoring systems are crucial in areas of the planet where water is scarce and in environmental harsh conditions to ensure an efficient crop growth. Moreover, plant diseases and pests impact the yields of crops, an early detection allows to treat the disease or pest quickly and reduce the impact of these latter. In this paper, we propose an integrated approach which optimize the water use, the supply of fertilizers, the treatment of plant diseases and pests with a center-pivot equiped with camera by means of IoT and AI algorithms

2020 Program and Abstracts for the Celebration of Student Scholarship

Program and Abstracts from the Celebration of Student Scholarship held in the Spring of 2020