scholarly journals Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

2020 ◽  
Vol 10 (17) ◽  
pp. 6132
Author(s):  
Tomáš Řezník ◽  
Petr Kubíček ◽  
Lukáš Herman ◽  
Tomáš Pavelka ◽  
Šimon Leitgeb ◽  
...  
Keyword(s):  
Big Data ◽  
Spatial Pattern ◽  
Precision Agriculture ◽  
Lessons Learned ◽  
Positioning System ◽  
Proof Of Concept ◽  
Farm Machinery ◽  
Yield Values ◽  
2D And 3D

Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations.

scholarly journals Towards the Development and Verification of a 3D-Based Advanced Optimized Farm Machinery Trajectory Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2980
Author(s):  
Tomáš Řezník ◽  
Lukáš Herman ◽  
Martina Klocová ◽  
Filip Leitner ◽  
Tomáš Pavelka ◽  
...  
Keyword(s):  
Economic Efficiency ◽  
Precision Agriculture ◽  
Soil Compaction ◽  
Unified Modeling Language ◽  
The Czech Republic ◽  
Farm Machinery ◽  
Unified Modeling ◽  
Environmental Burden ◽  
On Line ◽  
2D And 3D

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostěnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.

Telecommunications- and Information Technology–Inspired Analyses: Review of an Intelligent Transportation Systems Experience

2017 ◽  
Vol 2658 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Yupo Chan
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Transportation Systems ◽  
Lessons Learned ◽  
Connected Vehicles ◽  
Ongoing Research ◽  
Real Time Information ◽  
Time Information

This paper reviews both the author’s experience with managing highway network traffic on a real-time basis and the ongoing research into harnessing the potential of telecommunications and information technology (IT). On the basis of the lessons learned, this paper speculates about how telecommunications and IT capabilities can respond to current and future developments in traffic management. Issues arising from disruptive telecommunications technologies include the ready availability of real-time information, the crowdsourcing of information, the challenges of big data, and the need for information quality. Issues arising from transportation technologies include autonomous vehicles and connected vehicles and new taxi-like car- and bikesharing. Illustrations are drawn from the following core functions of a traffic management center: ( a) detecting and resolving an incident (possibly through crowdsourcing), ( b) monitoring and forecasting traffic (possibly through connected vehicles serving as sensors), ( c) advising motorists about routing alternatives (possibly through real-time information), and ( d) configuring traffic control strategies and tactics (possibly though big data). The conclusion drawn is that agility is the key to success in an ever-evolving technological scene. The solid guiding principle remains innovative and rigorous analytical procedures that build on the state of the art in the field, including both hard and soft technologies. The biggest modeling and simulation challenge remains the unknown, including such rapidly emerging trends as the Internet of things and the smart city.

scholarly journals Monitoring cattle behavior and pasture use with GPS and GIS

2000 ◽  
Vol 80 (3) ◽  
pp. 405-413 ◽  
Author(s):  
L.W. Turner ◽  
M.C. Udal ◽  
B. T. Larson ◽  
S.A. Shearer
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Global Positioning System ◽  
Precision Agriculture ◽  
Geographic Information ◽  
Spatial And Temporal Variability ◽  
Positioning System ◽  
Gps Technology ◽  
Global Positioning ◽  
Livestock Behavior

Precision agriculture is already being used commercially to improve variability management in row crop agriculture. In the same way, understanding how spatial and temporal variability of animal, forage, soil and landscape features affect grazing behavior and forage utilization provides potential to modify pasture management, improve efficiency of utilization, and maximize profits. Recent advances in global positioning system (GPS) technology have allowed the development of lightweight GPS collar receivers suitable for monitoring animal position at 5-min intervals. The GPS data can be imported into a geographic information system (GIS) to assess animal behavior characteristics and pasture utilization. This paper describes application and use of GPS technology on intensively managed beef cattle, and implications for livestock behavior and management research on pasture. Key words: Livestock behavior, electronics, grazing, forage, global positioning system, geographic information system

PICASSO: The Golden CubeSat

2021 ◽  
Author(s):  
Noel Baker ◽  
Michel Anciaux ◽  
Philippe Demoulin ◽  
Didier Fussen ◽  
Didier Pieroux ◽  
...  
Keyword(s):  
Electron Temperature ◽  
Plasma Density ◽  
Space Science ◽  
Lessons Learned ◽  
Vertical Profiles ◽  
Proof Of Concept ◽  
Scientific Instruments ◽  
The Earth ◽  
Spacecraft Potential

<p>Led by the Belgian Institute for Space Aeronomy, the ESA-backed mission PICASSO (PICo-Satellite for Atmospheric and Space Science Observations) successfully launched its gold-plated satellite on an Arianespace Vega rocket in September 2020. PICASSO is a 3U CubeSat mission in collaboration with VTT Technical Research Center of Finland Ltd, AAC Clyde Space Ltd. (UK), and the CSL (Centre Spatial de Liège), Belgium. The commissioning of the two onboard scientific instruments is currently ongoing; once they are operational, PICASSO will be capable of providing scientific measurements of the Earth’s atmosphere. VISION, proposed by BISA and developed by VTT, will retrieve vertical profiles of ozone and temperature by observing the Earth's atmospheric limb during orbital Sun occultation; and SLP, developed by BISA, will measure in situ plasma density and electron temperature together with the spacecraft potential.</p><p>Serving as a groundbreaking proof-of-concept, the PICASSO mission has taught valuable lessons about the advantages of CubeSat technology as well as its many complexities and challenges. These lessons learned, along with preliminary measurements from the two instruments, will be presented and discussed.</p>

An Introduction to Computational Social Science for Organizational Communication

2018 ◽  
pp. 184-200
Author(s):  
Andrew N. Pilny ◽  
Marshall Scott Poole
Keyword(s):  
Big Data ◽  
Social Science ◽  
Organizational Communication ◽  
Computational Social Science ◽  
Lessons Learned ◽  
Audit Culture ◽  
Research Designs ◽  
Data Driven Approach ◽  
Interaction Modeling ◽  
The Relationship

The exponential growth of “Big Data” has given rise to a field known as computational social science (CSS). The authors view CSS as the interdisciplinary investigation of society that takes advantage of the massive amount of data generated by individuals in a way that allows for abductive research designs. Moreover, CSS complicates the relationship between data and theory by opening the door for a more data-driven approach to social science. This chapter will demonstrate the utility of a CSS approach using examples from dynamic interaction modeling, machine learning, and network analysis to investigate organizational communication (OC). The chapter concludes by suggesting that lessons learned from OC's history can help deal with addressing several current issues related to CSS, including an audit culture, data collection ethics, transparency, and Big Data hubris.

A Distributed and Scalable Solution for Applying Semantic Techniques to Big Data

Big Data ◽  
2016 ◽  
pp. 1091-1109 ◽  
Author(s):  
Alba Amato ◽  
Salvatore Venticinque ◽  
Beniamino Di Martino
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Software Architecture ◽  
Application Domain ◽  
Proof Of Concept ◽  
Functional Requirements ◽  
Original Formulation ◽  
Digital Revolution ◽  
Big Data Technologies ◽  
Semantic Techniques

The digital revolution changes the way culture and places could be lived. It allows users to interact with the environment creating an immense availability of data, which can be used to better understand the behavior of visitors, as well as to learn about their thoughts on what the visit creates excitement or disappointment. In this context, Big Data becomes immensely important, making possible to turn this amount of data in information, knowledge, and, ultimately, wisdom. This paper aims at modeling and designing a scalable solution that integrates semantic techniques with Cloud and Big Data technologies to deliver context aware services in the application domain of the cultural heritage. The authors started from a baseline framework that originally was not conceived to scale when huge workloads, related to big data, must be processed. They provide an original formulation of the problem and an original software architecture that fulfills both functional and not-functional requirements. The authors present the technological stack and the implementation of a proof of concept.

A Big Data Test-bed for Analyzing Data Generated by an Air Pollution Sensor Network

2016 ◽  
Vol 13 (4) ◽  
pp. 19-35 ◽  
Author(s):  
Lídice García Ríos ◽  
José Alberto Incera Diéguez
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Sensor Network ◽  
Proof Of Concept ◽  
Test Bed ◽  
Pollution Levels ◽  
Wide Range ◽  
Military Applications

Sensor networks have perceived an extraordinary growth in the last few years. From niche industrial and military applications, they are currently deployed in a wide range of settings as sensors are becoming smaller, cheaper and easier to use. Sensor networks are a key player in the so-called Internet of Things, generating exponentially increasing amounts of data. Nonetheless, there are very few documented works that tackle the challenges related with the collection, manipulation and exploitation of the data generated by these networks. This paper presents a proposal for integrating Big Data tools (in rest and in motion) for gathering, storage and analysis of data generated by a sensor network that monitors air pollution levels in a city. The authors provide a proof of concept that combines Hadoop and Storm for data processing, storage and analysis, and Arduino-based kits for constructing their sensor prototypes.

scholarly journals A Security Monitoring Framework for Mobile Devices

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1197 ◽  
Author(s):  
António Lima ◽  
Luis Rosa ◽  
Tiago Cruz ◽  
Paulo Simões
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Lessons Learned ◽  
Bring Your Own Device ◽  
Reference Architecture ◽  
Proof Of Concept ◽  
Security Monitoring ◽  
Detection Systems ◽  
Management Policies ◽  
Computing Platforms

Quite often, organizations are confronted with the burden of managing mobile device assets, requiring control over installed applications, security, usage profiles or customization options. From this perspective, the emergence of the Bring Your Own Device (BYOD) trend has aggravated the situation, making it difficult to achieve an adequate balance between corporate regulations, freedom of usage and device heterogeneity. Moreover, device and information protection on mobile ecosystems are quite different from securing other device assets such as laptops or desktops, due to their specific characteristics and limitations—quite often, the resource overhead associated with specific security mechanisms is more important for mobile devices than conventional computing platforms, as the former frequently have comparatively less computing capabilities and more strict power management policies. This paper presents an intrusion and anomaly detection framework specifically designed for managed mobile device ecosystems, that is able to integrate into mobile device and management frameworks for complementing conventional intrusion detection systems. In addition to presenting the reference architecture for the proposed framework, several implementation aspects are also analyzed, based on the lessons learned from developing a proof-of-concept prototype that was used for validation purposes.

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