2,124 research outputs found

    Architecture and Applications of IoT Devices in Socially Relevant Fields

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    Number of IoT enabled devices are being tried and introduced every year and there is a healthy competition among researched and businesses to capitalize the space created by IoT, as these devices have a great market potential. Depending on the type of task involved and sensitive nature of data that the device handles, various IoT architectures, communication protocols and components are chosen and their performance is evaluated. This paper reviews such IoT enabled devices based on their architecture, communication protocols and functions in few key socially relevant fields like health care, farming, firefighting, women/individual safety/call for help/harm alert, home surveillance and mapping as these fields involve majority of the general public. It can be seen, to one's amazement, that already significant number of devices are being reported on these fields and their performance is promising. This paper also outlines the challenges involved in each of these fields that require solutions to make these devices reliableComment: 1

    Machinery and Techniques for Cattle Husbandry

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    Die Digitalisierung hat als Megatrend auch die Milchviehhaltung ergriffen. Für die Betriebe prägt sie sich in der Automatisierung, der Sensorik und dem Datenmanagement aus. Bei der Automatisierung ist die Melktechnik Vorreiter und bietet inzwischen für alle Betriebstypen und -größen verschiedene Konzepte an. Bei Fütterungs- und Reinigungstechnik ist eine ähnliche Entwicklung zu erwarten. Im Bereich der Sensorik gibt es eine Vielzahl von Anbietern mit verschiedensten Systemen. Hier wird sich zeigen, was sich langfristig etablieren wird. Das Datenmanagement ist in der Milchviehhaltung noch entwicklungsfähig. Besonders über System- und Firmengrenzen hinweg gibt es hier kaum Angebote. Die Umsetzung der Systeme in der Praxis wird dabei von den stark schwankenden Milchpreisen nach dem Auslaufen der Quote und den unstrukturierten Anforderungen der Gesellschaft an die Milchviehhaltung beeinflusst.Digitization has also taken dairy farming as a megatrend. For farms, it is characterized by automation, sensor technology and data management. Milking technology is a pioneer in automation and now offers different concepts for all types and sizes of farms. In feeding and cleaning technology, a similar development is expected. In the field of sensors, there are a large number of providers with a wide range of systems. Here will show what will establish long term. Data management is still viable in dairy farming. There are hardly any offers, especially over system and company borders. The implementation of the systems in practice is influenced by the strongly fluctuating milk prices after the quota has expired and the unstructured demands of society on dairy cattle husbandry

    National Conference on COMPUTING 4.0 EMPOWERING THE NEXT GENERATION OF TECHNOLOGY (Era of Computing 4.0 and its impact on technology and intelligent systems)

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    As we enter the era of Computing 4.0, the landscape of technology and intelligent systems is rapidly evolving, with groundbreaking advancements in artificial intelligence, machine learning, data science, and beyond. The theme of this conference revolves around exploring and shaping the future of these intelligent systems that will revolutionize industries and transform the way we live, work, and interact with technology. Conference Topics Quantum Computing and Quantum Information Edge Computing and Fog Computing Artificial Intelligence and Machine Learning in Computing 4.0 Internet of Things (IOT) and Smart Cities Block chain and Distributed Ledger Technologies Cybersecurity and Privacy in the Computing 4.0 Era High-Performance Computing and Parallel Processing Augmented Reality (AR) and Virtual Reality (VR) Applications Cognitive Computing and Natural Language Processing Neuromorphic Computing and Brain-Inspired Architectures Autonomous Systems and Robotics Big Data Analytics and Data Science in Computing 4.0https://www.interscience.in/conf_proc_volumes/1088/thumbnail.jp

    \u3ci\u3eCharacterizing Feedlot Feed using Depth Cameras and Imaging Technology\u3c/i\u3e

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    Imaging technology is a growing field that provides solutions in many areas from manufacturing to agriculture. Through previous research, imaging technologies have been studied in livestock farming to monitor the animal’s health and welfare in the production process. However, the feedlot industry is still behind in validating the feasibility to use some of these technologies nor to adopt such technologies to address challenges the industry is facing, such as lack of skilled labor.This work proposes using novel imaging methods to identify feed types and estimate the amount of feed remaining in a typical Midwestern feedlot feed bunk. These methods have promising potential to provide alternative tools to feedlot operations to alleviate labor requirements for tasks like bunk calling, feed sourcing, and feed mixing. This approach, if successful, provides an alternative option that allows existing systems to incorporate these methods into their framework to accurately perform daily tasks.The main contribution of this work is to leverage imaging technologies, specifically, depth imaging and machine learning techniques to build and validate models that can be used in the feedlot production systems in the Midwestern U.S. To date, several studies have explored the use of imaging technologies and machine learning to monitor individual cow intake in dairy production, but there is limited research body comprehensively conducted to explore these technologies for feedlot applications.The proposed methods were used to collect imagery data for eleven common feedlot ingredients and seven diets. Collected images were processed (a) to estimate the weights of residual feed in the bunk, (b) to evaluate the accuracy of depth cameras in estimating residual feed, (c) to characterize the different feed textures, and (d) to classify feed textures using machine learning techniques. Regression models using pixel transformation were developed to correlate image-model-estimated and the scale-measured feed weights, whereas texture analysis techniques and residual neural network model in 10 classes were used to identify the individual ingredients. Methodologies and results are presented in this thesis as a paper format. The major findings indicate that using low-cost depth cameras and machine learning techniques is promising in the development of alternative tools to estimate the amount of residual feed in concrete bunks and identify individual feed ingredients commonly used in commercial feedlots in the U.S. Advisor: Yijie Xion

    The Digitalisation of African Agriculture Report 2018-2019

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    An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

    Georgikon for Agriculture 2019

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    Cybersecurity and the future of agri-food industries

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    Master of AgribusinessDepartment of Agricultural EconomicsMajor Professor Not ListedThe agri-food sector has been undergoing rapid changes in the areas of food production and distribution over the past decades. Over the years, the sector has moved from disconnected, independent and uncoordinated operations to a highly interconnected, dependent and coordinated operations that have enhanced efficiency. The principal cost of this highly efficient system of production is the increased complexity and the exposure to potential risks networked organizations face in the age of the fourth industrial revolution. Increasingly, the physical value of the agri-food sector’s activities has declined even as the intangibles (data, information, insights) have increased in value. As precision agriculture becomes the mainstream and global positioning systems and RFIDs are deployed to enhance traceability and safety, the importance of data protection and security also become exponentially critical to the integrity of the system. That the sector is ahead of the general economy in the adoption of autonomous machines and artificial intelligence implies that the crucial valuation in the sector would be on data generation, organization and analytics, and machine learning. The combined complexity of these systems and processes interacting together create value and at the same time exposes the industry to significant operational risks. For while it was much difficult for cows and grains of corn to be stolen, stealing the data supporting the value embedded in these commodities is becoming increasing easy and riskier. This research is an exploratory excursion into developing an awareness of the scope of the potential risks creeping into the agri-food sector. It raises concern about the nature, typology and structure of these cybersecurity risks, that identifies the skills and capabilities that are needed for the sector to continue producing value to its customers even as it sustains its competitiveness. It focuses attention on building the internal capacities along the agri-food supply chain to ensure that all stakeholders have the appropriate capabilities and capacities to address the impending and emerging challenges. After all, every chain is as strong as its weakest link. Cybersecurity threat has become a very critical challenge facing all businesses. And the agri-food sector is not immune to the threats it presents. Being prepared is a necessary condition for securing the sector’s future

    Drones and Geographical Information Technologies in Agroecology and Organic Farming

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    Although organic farming and agroecology are normally not associated with the use of new technologies, it’s rapid growth, new technologies are being adopted to mitigate environmental impacts of intensive production implemented with external material and energy inputs. GPS, satellite images, GIS, drones, help conventional farming in precision supply of water, pesticides, fertilizers. Prescription maps define the right place and moment for interventions of machinery fleets. Yield goal remains the key objective, integrating a more efficient use or resources toward an economic-environmental sustainability. Technological smart farming allows extractive agriculture entering the sustainability era. Societies that practice agroecology through the development of human-environmental co-evolutionary systems represent a solid model of sustainability. These systems are characterized by high-quality agroecosystems and landscapes, social inclusion, and viable economies. This book explores the challenges posed by the new geographic information technologies in agroecology and organic farming. It discusses the differences among technology-laden conventional farming systems and the role of technologies in strengthening the potential of agroecology. The first part reviews the new tools offered by geographic information technologies to farmers and people. The second part provides case studies of most promising application of technologies in organic farming and agroecology: the diffusion of hyperspectral imagery, the role of positioning systems, the integration of drones with satellite imagery. The third part of the book, explores the role of agroecology using a multiscale approach from the farm to the landscape level. This section explores the potential of Geodesign in promoting alliances between farmers and people, and strengthening food networks, whether through proximity urban farming or asserting land rights in remote areas in the spirit of agroecological transition. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons 4.0 license
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