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Emerging Robots and Sensing Technologies in Geosciences
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Emerging Robots and Sensing Technologies in Geosciences

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (15 May 2021) | Viewed by 12753

Special Issue Editors

Dr. João Valente

E-Mail Website
Guest Editor
Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands
Interests: field robotics, UAV, remote sensing, multi-robot systems, machine vision and olfactation
Dr. Duarte José Antunes

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, the Netherlands
Interests: optimal control; stochastic control; networked control systems; approximated dynamic programming; precision farming; systems biology
Special Issues, Collections and Topics in MDPI journals
Dr. Rolf Hut

E-Mail Website
Guest Editor
Faculty of Civil Engineering and Geoscience, chair of Water Resources Engineering, Delft University of Technology, Delft, Netherlands
Interests: cross discipline designed sensor systems, maker movement, impact of the arduino on geoscience, geoscientific sensor systems
Dr. Beril Sirmacek

E-Mail Website
Guest Editor
School of Robotics and Mechanics, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Interests: computer vision; remote sensing; artificial intelligence; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are proud to announce this Special Issue addressing the state-of-the-art of robotic systems and related sensors technologies applied to geosciences.

Sustainable agriculture and environment conservation are top priorities for all governments and nations worldwide. Our population is growing fast, and our resources are becoming ever scarcer. Sensor technology will play a key role in providing fast solutions and recovery plans for these challenges. However, data acquisition is still a challenge, mainly when dealing with complex and dynamic environments such as agricultural fields and natural ecosystems. Gathering reliable data in situ, in a fast, cost-effective, and long-lasting manner is often not possible in many contexts without using robotic platforms endowed with different sensors.

This Special Isue will consider for publication all new advances in autonomous remote sensing systems that are developed to sense closely the earth. We want to give room to ideas that are beyond your imagination, that are unconventional, but at the same time bring scientific soundness.

Topics within the domain of this Special Issue include but are not limited to the following:

  • Air, ground, and maritime robotic systems;
  • Novel perception systems, event-triggered sensing;
  • Sensors (of all kinds) and (data-driven) sensor fusion;
  • Open-source software/hardware and mechatronics;
  • Informative path planning and adaptive sampling;
  • Internet of Things;
  • Augmented reality/virtual reality applications;
  • Big data and machine learning;
  • Open-issues and challenges;
  • Precision agriculture, nature conservation, and land degradation assessment;
  • Applications and case studies are very welcome!

Note: priority will be given to open-source tools and methods (e.g., software, datasets, and ros{bags, nodes}[1]) that are released upon the acceptance of the publication.

[1]  http://www.ros.org

Dr. João Valente
Dr. Duarte Antunes
Dr. Rolf Hut
Dr. Beril Sirmaçek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

14 pages, 19777 KiB  
Article
Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops
by David Alejandro Jimenez-Sierra, Edgar Steven Correa, Hernán Darío Benítez-Restrepo, Francisco Carlos Calderon, Ivan Fernando Mondragon and Julian D. Colorado
Sensors 2021, 21(13), 4369; https://doi.org/10.3390/s21134369 - 25 Jun 2021
Cited by 8 | Viewed by 3993
Abstract
Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction [...] Read more.
Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works. Full article
(This article belongs to the Special Issue Emerging Robots and Sensing Technologies in Geosciences)
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13 pages, 3496 KiB  
Article
Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles
by Raphael Mawrence, Sandra Munniks and João Valente
Sensors 2020, 20(24), 7332; https://doi.org/10.3390/s20247332 - 20 Dec 2020
Cited by 17 | Viewed by 4020
Abstract
For years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO2. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the [...] Read more.
For years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO2. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the spatial resolution of the resulting air quality data. Recently, electrochemical sensors and their integration with unmanned aerial vehicles (UAVs) have attempted to fill these gaps through various experiments, none of which have considered the influence of a UAV when calibrating the sensors. Accordingly, this research attempts to improve the reliability of NO2 measurements detected from electrochemical sensors while on board an UAV by introducing rotor speed as part of the calibration model. This is done using a DJI Matrice 100 quadcopter and Alphasense sensors, which are calibrated using regression calculations in different environments. This produces a predictive r-squared up to 0.97. The sensors are then calibrated with rotor speed as an additional variable while on board the UAV and flown in a series of flights to evaluate the performance of the model, which produces a predictive r-squared up to 0.80. This methodological approach can be used to obtain more reliable NO2 measurements in future outdoor experiments that include electrochemical sensor integration with UAV’s. Full article
(This article belongs to the Special Issue Emerging Robots and Sensing Technologies in Geosciences)
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26 pages, 3855 KiB  
Article
Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle
by Yang Yang, Quan Li, Junnan Zhang and Yangmin Xie
Sensors 2020, 20(2), 439; https://doi.org/10.3390/s20020439 - 13 Jan 2020
Cited by 14 | Viewed by 3727
Abstract
Most path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage the measurement accuracy especially [...] Read more.
Most path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage the measurement accuracy especially when turning at high speed. In this study, an adaptive iterative learning algorithm is proposed to optimize the turning parameters, which accounts for the dynamic characteristics of unmanned surface vehicles (USVs). The resulting optimal turning radius and speed are used to generate the path and speed profiles. The simulation results show that the proposed path-smoothing and speed profile design algorithms can largely increase the path-following performance, which potentially can help to improve the measurement accuracy of various activities. Full article
(This article belongs to the Special Issue Emerging Robots and Sensing Technologies in Geosciences)
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