340 research outputs found

    Earth Observation Open Science and Innovation

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    geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

    Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

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    Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies

    Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

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    Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

    Current status of and future opportunities for digital agriculture in Australia

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    In Australia, digital agriculture is considered immature and its adoption ad hoc, despite a relatively advanced technology innovation sector. In this review, we focus on the technical, governance and social factors of digital adoption that have created a disconnect between technology development and the end user community (farmers and their advisors). Using examples that reflect both successes and barriers in Australian agriculture, we first explore the current enabling technologies and processes, and then we highlight some of the key socio-technical factors that explain why digital agriculture is immature and ad hoc. Pronounced issues include fragmentation of the innovation system (and digital tools), and a lack of enabling legislation and policy to support technology deployment. To overcome such issues and increase adoption, clear value propositions for change are necessary. These value propositions are influenced by the perceptions and aspirations of individuals, the delivery of digitally-enabled processes and the supporting legislative, policy and educational structures, better use/conversion of data generated through technology applications to knowledge for supporting decision making, and the suitability of the technology. Agronomists and early adopter farmers will play a significant role in closing the technology-end user gap, and will need support and training from technology service providers, government bodies and peer-networks. Ultimately, practice change will only be achieved through mutual understanding, ownership and trust. This will occur when farmers and their advisors are an integral part of the entire digital innovation system

    Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

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    As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

    Utilization of Internet of Things and wireless sensor networks for sustainable smallholder agriculture

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    Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science

    The Digital Transformation and Disruptive Technologies: Challenges and Solutions for the Electricity Sector in African Markets

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    The rise of disruptive technologies is profoundly transforming systems of production and management across sectors and industries, but primarily in wealthy countries. This paper considers how disruptive technologies could help improve power sector reform and development in African markets. In particular, it explores the role that might be played by the Internet of Things, cloud computing, and advanced analytics. After reviewing current trends in disruptive technologies, the paper illustrates the application of key elements with use cases in the areas of power infrastructure planning, power sector operations, and off-grid electrification. Finally, the paper looks at context-specific challenges to the widespread implementation of disruptive technologies. While disruptive technologies offer innovative ways of tackling some of the main challenges of traditional approaches to power sector development, their widespread adoption hinges on a concerted effort across public and private players to lend support to key aspects such as improved broadband connectivity, a vibrant startup scene and surrounding technology ecosystem, or simply the right to Internet access

    The digitization of agricultural industry – a systematic literature review on agriculture 4.0

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    Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world's population, agri-food demands are growing — posing the need to switch from traditional agricultural methods to smart agriculture practices, also known as agriculture 4.0. To fully benefit from the potential of agriculture 4.0, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of agriculture 4.0 by investigating the emerging trends of digital technologies in the agricultural industry. For this purpose, a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses is conducted to analyse the scientific literature related to crop farming published in the last decade. After applying the protocol, 148 papers were selected and the extent of digital technologies adoption in agriculture was examined in the context of service type, technology readiness level, and farm type. The results have shown that digital technologies such as autonomous robotic systems, internet of things, and machine learning are significantly explored and open-air farms are frequently considered in research studies (69%), contrary to indoor farms (31%). Moreover, it is observed that most use cases are still in the prototypical phase. Finally, potential roadblocks to the digitization of the agriculture sector were identified and classified at technical and socio-economic levels. This comprehensive review results in providing useful information on the current status of digital technologies in agriculture along with prospective future opportunities

    Internet of Things (IoT) in Agriculture Industries

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    The IoT applications in the agriculture industry are intertwined in many ways, creating a hybrid construct of traditional businesses with digital connectivity implementations. Strategic issues and principles on the assimilation of IoT solutions are prevail through time: investment costs, data interconnectivity, management and analysis, as well as data security. The agriculture industry is expected to be always alerted for the changes that may apply, with all the contemporary challenges and future directions responding to the situations, so it can reap the optimum use of the IoT connectivity
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