17,055 research outputs found
Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges
open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture
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
The Digitalisation of African Agriculture Report 2018-2019
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
An Effective Multi-Cue Positioning System for Agricultural Robotics
The self-localization capability is a crucial component for Unmanned Ground
Vehicles (UGV) in farming applications. Approaches based solely on visual cues
or on low-cost GPS are easily prone to fail in such scenarios. In this paper,
we present a robust and accurate 3D global pose estimation framework, designed
to take full advantage of heterogeneous sensory data. By modeling the pose
estimation problem as a pose graph optimization, our approach simultaneously
mitigates the cumulative drift introduced by motion estimation systems (wheel
odometry, visual odometry, ...), and the noise introduced by raw GPS readings.
Along with a suitable motion model, our system also integrates two additional
types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random
Field assumption. We demonstrate how using these additional cues substantially
reduces the error along the altitude axis and, moreover, how this benefit
spreads to the other components of the state. We report exhaustive experiments
combining several sensor setups, showing accuracy improvements ranging from 37%
to 76% with respect to the exclusive use of a GPS sensor. We show that our
approach provides accurate results even if the GPS unexpectedly changes
positioning mode. The code of our system along with the acquired datasets are
released with this paper.Comment: Accepted for publication in IEEE Robotics and Automation Letters,
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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
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