Articles | Volume 2, issue 1
https://doi.org/10.5194/jsss-2-51-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/jsss-2-51-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Mobile sensor platforms: categorisation and research applications in precision farming
C. W. Zecha
University of Hohenheim, Institute of Crop Science, Department of Agronomy (340a), Fruwirthstr. 23, 70599 Stuttgart, Germany
J. Link
University of Hohenheim, Institute of Crop Science, Department of Agronomy (340a), Fruwirthstr. 23, 70599 Stuttgart, Germany
W. Claupein
University of Hohenheim, Institute of Crop Science, Department of Agronomy (340a), Fruwirthstr. 23, 70599 Stuttgart, Germany
Related subject area
Measurement systems: Multi-sensor systems
Integration and evaluation of the high-precision MotionCam-3D into a 3D thermography system
Laser-tracker-based reference measurement for geometric calibration of phase-measuring deflectometry with active display registration
In situ analysis of hydration and ionic conductivity of sulfonated poly(ether ether ketone) thin films using an interdigitated electrode array and a nanobalance
Method and experimental investigation of surface heat dissipation measurement using 3D thermography
Determination of the mean base circle radius of gears by optical multi-distance measurements
Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments
Sensor characterization by comparative measurements using a multi-sensor measuring system
DAV3E – a MATLAB toolbox for multivariate sensor data evaluation
Autonomous micro-platform for multisensors with an advanced power management unit (PMU)
Combined distributed Raman and Bragg fiber temperature sensing using incoherent optical frequency domain reflectometry
Simultaneous in situ characterisation of bubble dynamics and a spatially resolved concentration profile: a combined Mach–Zehnder holography and confocal Raman-spectroscopy sensor system
Capacitive gas-phase detection in liquid nitrogen
A research port test bed based on distributed optical sensors and sensor fusion framework for ad hoc situational awareness
Sensor defect detection in multisensor information fusion
Challenges and trends in manufacturing measurement technology – the “Industrie 4.0” concept
Comparing mobile and static assessment of biomass in heterogeneous grassland with a multi-sensor system
Instrumented flow-following sensor particles with magnetic position detection and buoyancy control
Qualification concept for optical multi-scale multi-sensor systems
On the use of electrochemical multi-sensors in biologically charged media
Miguel-David Méndez-Bohórquez, Sebastian Schramm, Robert Schmoll, and Andreas Kroll
J. Sens. Sens. Syst., 13, 123–133, https://doi.org/10.5194/jsss-13-123-2024, https://doi.org/10.5194/jsss-13-123-2024, 2024
Short summary
Short summary
3D thermograms are a good alternative when a single traditional 2D thermal image does not reveal enough information to analyze a complex object. However, the 3D thermography field is still under exploration. This paper shows a comparison of a thermography system operated with two different 3D sensors. The results indicate that the depth sensor with more accurate measurements captures the object geometry better, and therefore the interpretation of the 3D thermograms is improved.
Yann Sperling and Ralf Bernhard Bergmann
J. Sens. Sens. Syst., 13, 1–7, https://doi.org/10.5194/jsss-13-1-2024, https://doi.org/10.5194/jsss-13-1-2024, 2024
Short summary
Short summary
Phase-measuring deflectometry is an optical shape measurement technique for reflective surfaces. The basic idea is that a pattern that is observed through reflection on a curved surface gets distorted and reveals information about its shape. In this work we describe a method to move the pattern to obtain data for quantitative shape determination. The experimental setup is calibrated. With a laser tracker we reveal calibration errors and discuss their influence on the reconstructed shape.
Hendrik Wulfmeier, Niklas Warnecke, Luca Pasquini, Holger Fritze, and Philippe Knauth
J. Sens. Sens. Syst., 11, 51–59, https://doi.org/10.5194/jsss-11-51-2022, https://doi.org/10.5194/jsss-11-51-2022, 2022
Short summary
Short summary
A newly developed experimental setup to characterize thin polymeric films during dehydration and hydration is presented. The great advantage of this measurement device and technique is that it monitors the mass change and conductivity of the films in situ and simultaneously at virtually identical conditions. The feasibility of the technique is demonstrated by characterizing ionomer thin films. A mass resolution of ±7.9 ng is achieved. The precision of relative humidity (RH) control is ±0.15 %.
Robert Schmoll, Sebastian Schramm, Tom Breitenstein, and Andreas Kroll
J. Sens. Sens. Syst., 11, 41–49, https://doi.org/10.5194/jsss-11-41-2022, https://doi.org/10.5194/jsss-11-41-2022, 2022
Short summary
Short summary
The method of non-contact temperature measurement in conjunction with a 3D sensor described in this paper can be used to determine the heat loss of technical devices and industrial plants. This measurement tool thus helps to optimize the energy efficiency of these devices and plants.
Marc Pillarz, Axel von Freyberg, and Andreas Fischer
J. Sens. Sens. Syst., 9, 273–282, https://doi.org/10.5194/jsss-9-273-2020, https://doi.org/10.5194/jsss-9-273-2020, 2020
Short summary
Short summary
The necessary reliability of wind turbine gearboxes increases the requirements for large gear measurements. However, standard measuring methods reach their limits for large gears with diameters > 1 m. Therefore a scalable optical gear measurement approach is presented. At first, simulation and experimental results prove the principle applicability of the measuring approach for small gear measurements. Geometric parameters of gears can be determined with a single-digit micrometer uncertainty.
Marcin Uradzinski and Hang Guo
J. Sens. Sens. Syst., 9, 7–13, https://doi.org/10.5194/jsss-9-7-2020, https://doi.org/10.5194/jsss-9-7-2020, 2020
Sebastian Hagemeier, Markus Schake, and Peter Lehmann
J. Sens. Sens. Syst., 8, 111–121, https://doi.org/10.5194/jsss-8-111-2019, https://doi.org/10.5194/jsss-8-111-2019, 2019
Short summary
Short summary
In this contribution a multi-sensor measuring system is presented. With this measurement system comparative measurements using five different surface measurement sensors are performed under identical conditions in a single set-up. The presented measurement results show different transfer behaviour of each sensor and indicate unique advantages for tactile and optical sensors. Comparative measuring enables the investigation of measurement deviations and helps to improve appropriate techniques.
Manuel Bastuck, Tobias Baur, and Andreas Schütze
J. Sens. Sens. Syst., 7, 489–506, https://doi.org/10.5194/jsss-7-489-2018, https://doi.org/10.5194/jsss-7-489-2018, 2018
Short summary
Short summary
Predictions about systems too complex for physical modeling can be made nowadays with data-based models. Our software DAV³E is an easy way to extract relevant features from cyclic raw data, a process often neglected in other software packages, based on mathematical methods, incomplete physical models, or human intuition. Its graphical user interface further provides methods to fuse data from many sensors, to teach a model the prediction of new data, and to check the model’s performance.
Pierre Bellier, Philippe Laurent, Serguei Stoukatch, François Dupont, Laura Joris, and Michael Kraft
J. Sens. Sens. Syst., 7, 299–308, https://doi.org/10.5194/jsss-7-299-2018, https://doi.org/10.5194/jsss-7-299-2018, 2018
Short summary
Short summary
An original platform embedding multiple sensors and an energy harvesting unit is described. It is versatile and requires little or no maintenance. Multiple platforms can be connected to a hub device in a wireless sensor network. Emphasis was put on the reduction of power consumption and on the energy harvesting unit. With the addition of a small solar panel the system can be fully autonomous indoors. Characterization of power consumption and a test in real-world operation are presented.
Max Koeppel, Stefan Werzinger, Thomas Ringel, Peter Bechtold, Torsten Thiel, Rainer Engelbrecht, Thomas Bosselmann, and Bernhard Schmauss
J. Sens. Sens. Syst., 7, 91–100, https://doi.org/10.5194/jsss-7-91-2018, https://doi.org/10.5194/jsss-7-91-2018, 2018
Short summary
Short summary
Optical temperature sensors offer unique features which make them indispensable for key industries such as the energy sector. However, commercially available systems are designed to perform either distributed or hot spot temperature measurements. We have combined two measurement concepts to overcome this limitation, which allow distributed temperature measurements to be performed simultaneously with read-outs of optical hot spot temperature sensors at distinct positions along a fiber.
Jajnabalkya Guhathakurta, Daniela Schurr, Günter Rinke, Roland Dittmeyer, and Sven Simon
J. Sens. Sens. Syst., 6, 223–236, https://doi.org/10.5194/jsss-6-223-2017, https://doi.org/10.5194/jsss-6-223-2017, 2017
Christoph Kandlbinder, Alice Fischerauer, Mario Mösch, Tobias Helling, Gerhard Fischerauer, and Martin Siegl
J. Sens. Sens. Syst., 6, 135–143, https://doi.org/10.5194/jsss-6-135-2017, https://doi.org/10.5194/jsss-6-135-2017, 2017
Short summary
Short summary
In this work we present a cost- and energy-efficient measurement system for the spatial detection of gas phases in liquid fluids with a low permittivity value. We showed that we can simulate the system and its environment and use the calculated results to interpret the results originating from measurements of the electrical capacitance between different electrodes. The proposed system can be modified for, e.g., observation of fluid behaviour in cryogenic tanks for reigniteable space propulsion.
Nick Rüssmeier, Axel Hahn, Daniela Nicklas, and Oliver Zielinski
J. Sens. Sens. Syst., 6, 37–52, https://doi.org/10.5194/jsss-6-37-2017, https://doi.org/10.5194/jsss-6-37-2017, 2017
Short summary
Short summary
Maritime study sites utilized as a physical experimental test bed for sensor data fusion, communication technology and data stream analysis tools can provide substantial frameworks for design and development of e-navigation technologies. Increasing safety by observation and monitoring of the maritime environment with new technologies meets forward-looking needs to facilitate situational awareness. The study highlights research potentials and foundations achieved by distributed optical sensors.
Jan-Friedrich Ehlenbröker, Uwe Mönks, and Volker Lohweg
J. Sens. Sens. Syst., 5, 337–353, https://doi.org/10.5194/jsss-5-337-2016, https://doi.org/10.5194/jsss-5-337-2016, 2016
Short summary
Short summary
This paper presents a novel method for the detection of sensor defects. Here, the consistency between measurements of sensor groups are utilized for this method. The sensor groups are pre-determined by the structure of an existing sensor fusion algorithm, which is in turn used to determine the health of a monitored system (e.g. a machine). Defect detection results of the presented method for different test cases and the method's capability to detect a number of typical sensor defects are shown.
Dietrich Imkamp, Jürgen Berthold, Michael Heizmann, Karin Kniel, Eberhard Manske, Martin Peterek, Robert Schmitt, Jochen Seidler, and Klaus-Dieter Sommer
J. Sens. Sens. Syst., 5, 325–335, https://doi.org/10.5194/jsss-5-325-2016, https://doi.org/10.5194/jsss-5-325-2016, 2016
Short summary
Short summary
Strategic considerations and publications dealing with the future of industrial production are significantly influenced these days by the concept of "Industrie 4.0". For this reason the field of measurement technology for industrial production must also tackle this concept when thinking about future trends and challenges in metrology.
Hanieh Safari, Thomas Fricke, Björn Reddersen, Thomas Möckel, and Michael Wachendorf
J. Sens. Sens. Syst., 5, 301–312, https://doi.org/10.5194/jsss-5-301-2016, https://doi.org/10.5194/jsss-5-301-2016, 2016
Short summary
Short summary
This study aimed to explore the potential of a multi-sensor system for assessment of biomass in pastures under different grazing intensities. Prediction accuracy with a mobile application of sensors was always lower than when sensors were applied statically. However accuracy of biomass prediction improved with increasing grazing intensity. Although the limitations associated with the system especially in very lenient pastures, the finding opens up a perspective for future grazing management.
Sebastian Felix Reinecke and Uwe Hampel
J. Sens. Sens. Syst., 5, 213–220, https://doi.org/10.5194/jsss-5-213-2016, https://doi.org/10.5194/jsss-5-213-2016, 2016
Short summary
Short summary
Sensor particles with buoyancy control and position detection are presented, which are used for flow tracking in large vessels, such as biogas digesters and waste water tanks. They were tested in the realistic flows of a biogas digester. The buoyancy control allows taring for good flow tracing by the sensor particles, and it lets them float to the surface after data acquisition for easy recovery. The fluid mixing was estimated from detected passages of sensor particles at a magnetic coil.
A. Loderer and T. Hausotte
J. Sens. Sens. Syst., 5, 1–8, https://doi.org/10.5194/jsss-5-1-2016, https://doi.org/10.5194/jsss-5-1-2016, 2016
Short summary
Short summary
This article describes a new qualification concept for dimensional measurements on optical measuring systems, using the example of a prototypical multi-scale multi-sensor fringe projection system for production-related inspections of sheet-bulk metal-formed parts. A new concept is developed for determining the orientations and positions of the sensors' measuring ranges in a common coordinate system. The principle element of the concept is a newly developed flexible reference artefact.
S. Sachse, A. Bockisch, U. Enseleit, F. Gerlach, K. Ahlborn, T. Kuhnke, U. Rother, E. Kielhorn, P. Neubauer, S. Junne, and W. Vonau
J. Sens. Sens. Syst., 4, 295–303, https://doi.org/10.5194/jsss-4-295-2015, https://doi.org/10.5194/jsss-4-295-2015, 2015
Cited articles
Acevo-Herrera, R., Aguasca, A., Bosch-Lluis, X., Camps, A., Martínez-Fernández, J., Sánchez-Martín, N., and Pérez-Gutiérrez, C.: Design and First Results of an UAV-Borne L-Band Radiometer for Multiple Monitoring Purposes, Remote Sens., 2, 1662–1679, https://doi.org/10.3390/rs2071662, 2010.
Adamchuk, V. I., Hummel, J., Morgan, M., and Upadhyaya, S.: On-the-go soil sensors for precision agriculture, Comput. Electron. Agric., 44, 71–91, https://doi.org/10.1016/j.compag.2004.03.002, 2004.
Adamchuk, V. I., Viscarra Rossel, R. A., Sudduth, K. A., and Schulze Lammers, P.: Sensor Fusion for Precision Agriculture, in: Sensor Fusion – Foundation and Applications, edited by: Thomas, C., InTech, chap. 2, 27–40, 2010.
AeroVironment Inc.: Nano Hummingbird, http://www.avinc.com/resources/press_release/aerovironment_develops_worlds_first_fully_operational_life-size_hummingbird, last access: 7 May 2013.
Agogino, A., Goebel, K., and Alag, S.: Intelligent Sensor Validation And Sensor Fusion For Reliability And Safety Enhancement In Vehicle Control, Tech. rep., University of California at Berkley, Berkley, CA, USA, 56 pp., 1995.
Al-Abbas, A., Swain, P., and Baumgardner, M.: Relating Organic Matter and Clay Content to the Multispectral Radiance of Soils, Soil Sci., 114, 477–485, https://doi.org/10.1097/00010694-197212000-00011, 1972.
Andújar, D., Escolà, A., Dorado, J., and Fernández-Quintanilla, C.: Weed discrimination using ultrasonic sensors, Weed Res., 51, 543–547, https://doi.org/10.1111/j.1365-3180.2011.00876.x, 2011.
Andújar, D., Weis, M., and Gerhards, R.: An Ultrasonic System for Weed Detection in Cereal Crops, Sensors, 12, 17343–17357, https://doi.org/10.3390/s121217343, 2012.
Appel, J. and Nielsen, K.: Applied Multi-Agent Systems Principles to Cooperating Robots in Tomorrow's Agricultural Environment, Ph.D. thesis, University of Southern Denmark, Odense, Denmark, 238 pp., 2005.
Auernhammer, H.: Precision farming – the environmental challenge, Comput. Electron. Agric., 30, 31–43, https://doi.org/10.1016/S0168-1699(00)00153-8, 2001.
Balch, T.: TeamBots\up{TM 2.0 Overview}, http://www.teambots.org/, last access: 7 May 2013.
Bäni, D.: Fawn Rescue – Use of a UAV with thermal camera for detection of wild animals, Ph.D. thesis, ETH Zurich, Switzerland, 2011.
Barber, K. S. and Martin, C. E.: Autonomy as Decision-Making Control, in: Intelligent Agents VII Agent Theories Architectures and Languages, edited by: Castelfranchi, C. and Lespérance, Y., vol. 1986 of Lecture Notes in Comp. Sci.\/, Springer Berlin/Heidelberg, 267–271, https://doi.org/10.1007/3-540-44631-1_25, 2001.
Barrientos, A., Colorado, J., del Cerro, J., Martinez, A., Rossi, C., Sanz, D., and Valente, J.: Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots, J. Field Robot., 28, 667–689, https://doi.org/10.1002/rob.20403, 2011.
Bäumker, M., Przybilla, H.-J., and Zurhorst, A.: Enhancement of the Navigation Data Quality of an Autonomous Flying UAV for Use in Photogrammetry, in: Proceedings of the 3rd International Conference on Machine Control & Guidance, Stuttgart, Germany, 27–29 March 2012, 281–289, 2012.
Bechar, A. and Edan, Y.: Human-robot collaboration for improved target recognition of agricultural robots, Ind. Robot: An Int. J., 30, 432–436, https://doi.org/10.1108/01439910310492194, 2003.
Bernardes, T., Moreira, M. A., Adami, M., Giarolla, A., and Rudorff, B. F. T.: Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery, Remote Sens., 4, 2492–2509, https://doi.org/10.3390/rs4092492, 2012.
Blackmore, S.: UniBots – Agricultural Robotics Portal, http://www.unibots.com/Agricultural_Robotics_Portal.htm, last access: 7 May 2013.
Blackmore, S. and Griepentrog, H. W.: A future view of Precision Farming, in: Proceedings of the PreAgro Precision Agriculture Conference, Bonn, Germany, 13–15 March 2002, 131–145, 2002.
Blackmore, S., Stout, B., Maohua, W., and Runov, B.: Robotic agriculture – the future of agricultural mechanisation?, in: Proceedings of the 5th European Conference on Precision Agriculture, Uppsala, Sweden, 9–12 June 2005, 621–628, 2005.
Bláha, M., Eisenbeiss, H., Grimm, D., and Limpach, P.: Direct Georeferencing of UAVs, ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-1/C22, 131–136, https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-131-2011, 2012.
Blanco, J.-L., Moreno, F.-A., and Gonzalez, J.: A collection of outdoor robotic datasets with centimeter-accuracy ground truth, Auton. Robots, 27, 327–351, https://doi.org/10.1007/s10514-009-9138-7, 2009.
BMJ: § 16 LuftVO (German Air Traffic Regulations) Erlaubnisbedürftige Nutzung des Luftraums, Regulation, BMJ (German Federal Ministry of Justice), Berlin, Germany, 1 pp., http://www.gesetze-im-internet.de/luftvo/_16.html, 2012.
Bossé, E., Roy, J., and Grenier, D.: Data fusion concepts applied to a suite of dissimilar sensors, in: Proceedings of the Canadian Conference on Electrical and Computer Engineering, Calgary, AB, Canada, 26–29 May 1996, 692–695, https://doi.org/10.1109/CCECE.1996.548247, 1996.
Bravo, C., Moshou, D., Oberti, R., West, J., McCartney, A., Bodria, L., and Ramon, H.: Foliar Disease Detection in the Field Using Optical Sensor Fusion, Agr. Eng. Int., 6, 14 pp., 2004.
Bry, A., Bachrach, A., and Roy, N.: State estimation for aggressive flight in GPS-denied environments using onboard sensing, in: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), St. Paul, MN, USA, 14–18 May, 8 pp., https://doi.org/10.1109/ICRA.2012.6225295, 2012.
Bürkle, A., Segor, F., and Kollmann, M.: Towards Autonomous Micro UAV Swarms, J. Intell. Robotic Syst., 61, 339–353, https://doi.org/10.1007/s10846-010-9492-x, 2011.
Busemeyer, L., Klose, R., Linz, A., Thiel, M., Tilneac, M., Wunder, E., and Ruckelshausen, A.: Agro-sensor systems for outdoor plant phenotyping platforms in low and high density crop field plots, in: Proceedings of the 68th International Conference on Agricultural Engineering, Braunschweig, Germany, 27–28 October 2010, 213–218, 2010.
CAA: Basic principles of Unmanned Aircraft, Tech. rep., Civil Aviation Authority, London, UK, 1 pp., http://www.caa.co.uk/, 2009.
CAA: CAP 722 Unmanned Aircraft System Operations in UK Airspace – Guidance, UK Civil Aviation Authority, Gatwick, West Sussex, UK, 5 Edn., 2012.
Cartade, P., Lenain, R., Thuilot, B., and Berducat, M.: Formation Control Algorithm for a Fleet of Mobile Robots, in: Proceedings of the 3rd International Conference on Machine Control & Guidance, Stuttgart, Germany, 27–29 March 2012, 91–100, 2012.
Cepeda, J. S., Chaimowicz, L., and Soto, R.: Exploring Microsoft Robotics Studio as a Mechanism for Service-Oriented Robotics, in: Latin American Robotics Symposium and Intelligent Robotics Meeting, IEEE, 7–12, https://doi.org/10.1109/LARS.2010.18, 2010.
Compton, M., Neuhaus, H., Taylor, K., and Parashar, A.: Semantic Sensor Network Ontology, https://marinemetadata.org/community/teams/ontdevices/ontdevrel, last access: 7 May 2013.
Corwin, D. L. and Lesch, S. M.: Application of Soil Electrical Conductivity to Precision Agriculture, Agron. J., 95, 455–471, 2003.
Côté, C., Brosseau, Y., Létourneau, D., Raïevsky, C., and Michaud, F.: Robotic Software Integration Using MARIE, Int. J. Adv. Robotic Syst., 3, 55–60, 2006.
Crowley, J.: Navigation for an intelligent mobile robot, IEEE J. Robotics Autom., 1, 31–41, https://doi.org/10.1109/JRA.1985.1087002, 1985.
Dabas, M., Brisard, A., Tabbagh, J., and Boigontier, D.: Use of a new sub-metric multi-depth soil imaging system (MuCEP), in: Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MN, USA, 16–19 July 2000, 13 pp., 2000.
Dasarathy, B.: Sensor fusion potential exploitation-innovative architectures and illustrative applications, Proceedings of the IEEE, 85, 24–38, https://doi.org/10.1109/5.554206, 1997.
Dasarathy, B. V.: Information Fusion – what, where, why, when, and how?, Inform. Fusion, 2, 75–76, https://doi.org/10.1016/S1566-2535(01)00032-X, 2001.
de Croon, G., de Weerdt, E., de Wagter, C., Remes, B., and Ruijsink, R.: The appearance variation cue for obstacle avoidance, IEEE Trans. Robotics, 28, 529–534, 2012.
d'Oleire Oltmanns, S., Marzolff, I., Peter, K., and Ries, J.: Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco, Remote Sens., 4, 3390–3416, https://doi.org/10.3390/rs4113390, 2012.
Doyle, L., Vellekoop, M. J., and Mlodzianowski, W.: Optifert, http://www.optifert.eu/, last access: 7 May 2013.
Dunford, R., Michel, K., Gagnage, M., Piégay, H., and Trémelo, M.-L.: Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest, Int. J. Remote Sens., 30, 4915–4935, https://doi.org/10.1080/01431160903023025, 2009.
Durrant-Whyte, H. and Bailey, T.: Simultaneous localization and mapping: part I, IEEE Robotics Autom. Mag., 13, 99–110, https://doi.org/10.1109/MRA.2006.1638022, 2006.
Durrant-Whyte, H. F.: Sensor Models and Multisensor Integration, Int. J. Robot. Res., 7, 97–113, https://doi.org/10.1177/027836498800700608, 1988.
Eisenbeiss, H., Kunz, M., and Ingensand, H. (Eds.): Proceedings of the International Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g 2011), International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38-1/C22, ETH Zurich, Zurich, Switzerland, 2011.
Elmenreich, W.: An Introduction to Sensor Fusion, Tech. rep., Vienna University of Technology, Department of Computer Engineering, Vienna, Austria, 28 pp., 2002.
Encyclopaedia Britannica: Automation, http://www.britannica.com/EBchecked/topic/44912/automation, last access: 7 May 2013.
Engelberger, J. F.: Historical Perspective and Role in Automation, in: Handbook of Industrial Robotics, edited by: Nof, S. Y., chap. 1, 3–10, John Wiley & Sons, Inc., New York, NY, USA, 2nd Edn., 1999.
Evans, M., Anderson, J., and Crysdale, G.: Achieving flexible autonomy in multiagent systems using constraints, Appl. Artif. Intell., 6, 103–126, https://doi.org/10.1080/08839519208949944, 1992.
Everitt, J., Escobar, D., Cavazos, I., Noriega, J., and Davis, M.: A three-camera multispectral digital video imaging system, Remote Sens. Environ., 54, 333–337, https://doi.org/10.1016/0034-4257(95)00169-7, 1995.
FAA: Unmanned Aircraft (UAS), http://www.faa.gov/about/initiatives/uas/uas_faq/, last access: 7 May 2013.
Fender, F., Hanneken, M., Stroth, S. I. D., Linz, A., and Ruckelshausen, A.: Sensor Fusion Meets GPS: Individual Plant Detection, in: Proceedings of the XVI. CIGR World Congress & AgEng Bonn 2006 & 64th VDI-MEG International Conference Agricultural Engineering, Bonn, Germany, 3–7 September 2006, 279–280, 2006.
Fischer, D.: Mechanical weed control in organic sugar beets, Ph.D. thesis, University of Hohenheim, Stuttgart, Germany, 174 pp., 2012.
Fraisse, C. W., Sudduth, K. A., and Kitchen, N. R.: Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity, T. ASAE, 44, 155–166, 2001.
Franke, J., Mewes, T., and Menz, G.: Airborne hyperspectral imaging for the detection of powdery mildew in wheat, in: Proceedings of SPIE 7086, Imaging Spectrometry XIII, 7086, 708609:1–708609:10, https://doi.org/10.1117/12.795040, 2008.
Frost, A., McMaster, A., Saunders, K., and Lee, S.: The development of a remotely operated vehicle (ROV) for aquaculture, Aquacult. Eng., 15, 461–483, https://doi.org/10.1016/S0144-8609(96)01004-7, 1996.
García-Pérez, L., García-Alegre, M., Ribeiro, A., and Guinea, D.: An agent of behaviour architecture for unmanned control of a farming vehicle, Comput. Electron. Agric., 60, 39–48, https://doi.org/10.1016/j.compag.2007.06.004, 2008.
Geipel, J., Knoth, C., Elsässer, O., and Prinz, T.: DGPS- and INS-Based Orthophotogrammetry on Micro UAV Platforms for Precision Farming Services, in: Proceedings of the Geoinformatics 2011 Conference, Münster, Germany, 15–17 June 2011, 174–179, 2011.
Gerhards, R. and Oebel, H.: Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying, Weed Res., 46, 185–193, https://doi.org/10.1111/j.1365-3180.2006.00504.x, 2006.
Goodwin, B. K. and Mishra, A. K.: Farming Efficiency and the Determinants of Multiple Job Holding by Farm Operators, Am. J. Agr. Econ., 86, 722–729, https://doi.org/10.1111/j.0002-9092.2004.00614.x, 2004.
Gottlieb, E., Harrigan, R., McDonald, M., Oppel, F., and Xavier, P.: The Umbra Simulation Framework, Tech. rep., Intelligent Systems and Robotics Center Sandia National Laboratories, Albuquerque, NM, USA, 17 pp., https://doi.org/10.2172/782709, 2001.
Graeff, S., Link, J., and Claupein, W.: Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements, Cent. Eur. J. Biol., 1, 275–288, https://doi.org/10.2478/s11535-006-0020-8, 2006.
Griepentrog, H. W., Ruckelshausen, A., Jørgensen, R. N., Lund, I., Oerke, E.-C., Gerhards, R., Menz, G., and Sikora, R. A.: Autonomous Systems for Plant Protection, in: Precision Crop Protection – the Challenge and Use of Heterogeneity, Oerke, E.-C., Gerhards, R., Menz, G., and Sikora, R. A. (Eds.), Springer Science+Business Media B.V., Heidelberg, 323–334, https://doi.org/10.1007/978-90-481-9277-9, 2010.
Gröll, K., Graeff, S., and Claupein, W.: Use of Vegetation indices to detect plant diseases, in: Proceedings of the 27th GIL Annual Conference, 340, Stuttgart, Germany, 5–7 March 2007, 91–94, 2007.
Grossmann, P.: Multisensor Data Fusion, GEC J. Technol., 15, 27–37, 1998.
Gutjahr, C. and Gerhards, R.: Decision Rules for Site-Specific Weed Management, in: Precision Crop Protection – the Challenge and Use of Heterogeneity, Oerke, E.-C., Gerhards, R., Menz, G., and Sikora, R. A. (Eds.), Springer Science+Business Media B.V., Heidelberg, Germany, 223–239, https://doi.org/10.1007/978-90-481-9277-9_14, 2010.
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., and Dextraze, L.: Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture, Remote Sens. Environ., 81, 416–426, https://doi.org/10.1016/S0034-4257(02)00018-4, 2002.
Hague, T., Marchant, J., and Tillett, N.: Ground based sensing systems for autonomous agricultural vehicles, Comput. Electron. Agric., 25, 11–28, https://doi.org/10.1016/S0168-1699(99)00053-8, 2000.
Hayes-Roth, B.: A blackboard architecture for control, Artif. Intell., 26, 251–321, https://doi.org/10.1016/0004-3702(85)90063-3, 1985.
He, B., Zhang, H., Li, C., Zhang, S., Liang, Y., and Yan, T.: Autonomous navigation for autonomous underwater vehicles based on information filters and active sensing., Sensors, 11, 10958–10980, https://doi.org/10.3390/s111110958, 2011.
Herbst, R. (Eds.): UAS-UL Proceedings, in: Proceedings of the 1st Conference on Autonomously Flying Systems for Sustainable Agriculture (UAS-UL), HU Berlin, Berlin, Germany, 2012.
Hoge, F. E., Swift, R. N., and Yungel, J. K.: Active-passive airborne ocean color measurement. 2: Applications, Appl. Optics, 25, 48–57, https://doi.org/10.1364/AO.25.000048, 1986.
Hunt Jr., E. R., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S., and McCarty, G. W.: Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring, Remote Sens., 2, 290–305, https://doi.org/10.3390/rs2010290, 2010.
ICAO: ICAO Cir 328, Unmanned Aircraft Systems (UAS), International Civil Aviation Organization, Montréal, Quebec, Canada, 2011.
IDGA: Unmanned aerial system, http://www.idga.org/glossary/unmanned-aerial-system/, last access: 7 May 2013.
Jensen, K., Nielsen, S. H., Larsen, M., Bøgild, A., Green, O., and Jørgensen, R. N.: FroboMind – proposing a conceptual architecture for field robots, in: Proceedings of the International Conference of Agricultural Engineering (CIGR-AgEng), 5th Automation Technology for Off-road Equipment Conference (ATOE), Valencia, Spain, 8–12 July 2012, 163–168, 2012.
Jia, Z., Balasuriya, A., and Challa, S.: Autonomous Vehicles Navigation with Visual Target Tracking: Technical Approaches, Algorithms, 1, 153–182, https://doi.org/10.3390/a1020153, 2008.
Jonjak, A. K.: Analysis of Site-Specific Adjustment Applied to On-The-Go Soil Sensing Data for Agronomic Use, Biol. Syst. Eng. – Dissertations, Theses, and Student Research, 2011.
Jørgensen, R. N., Sørensen, C. G., Maagaard, J., Havn, I., Jensen, K., Søgaard, H. T., and Sørensen, L. B.: HortiBot: A System Design of a Robotic Tool Carrier for High-tech Plant Nursing, Agri. Eng. Int.: the CIGR Ejournal, 9, 13 pp., 2007.
Kam, H., Schmittmann, O., and Schulze Lammers, P.: Cropping System for Mechanical Intra-/Inter Row Weeding, in: Proceedings of the 67th International Conference on Agricultural Engineering, 135–140, Hanover, Germany, 6–7 November 2009.
Karaboga, D. and Akay, B.: A survey: algorithms simulating bee swarm intelligence, Artif. Intell. Review, 31, 61–85, https://doi.org/10.1007/s10462-009-9127-4, 2009.
Keller, M., Zecha, C., Weis, M., Link, J., Gerhards, R., and Claupein, W.: Competence centre SenGIS – exploring methods for georeferenced multi-sensor data acquisition, storage, handling and analysis, in: Proceedings of the 8th European Conference on Precision Agriculture (ECPA), Prague, Czech Republic, 11–14 July 2011, 491–500, 2011.
Khosla, R.: Precision agriculture: challenges and opportunities in a flat world, in: Proceedings of the 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane, Australia, 1–6 August 2010, 26–28, 2010.
Kittmann, K., Breeman, J., and Schmollgruber, P.: The NACRE Innovative Evaluation Platform and its Navigation & Control Strategies, in: Proceedings of the SAE 2011 AeroTech Congress & Exhibition, Toulouse, France, 18–21 October 2011.
Knappenberger, T. and Köller, K.: Spatial assessment of the correlation of seeding depth with emergence and yield of corn, Precis. Agric., 13, 163–180, https://doi.org/10.1007/s11119-011-9235-4, 2011.
Konolige, K., Myers, K., Ruspini, E., and Saffiotti, A.: The Saphira architecture: a design for autonomy, J. Exp. Theor. Artif. Intell., 9, 215–235, https://doi.org/10.1080/095281397147095, 1997.
Kushleyev, A., Mellinger, D., and Kumar, V.: Towards A Swarm of Agile Micro Quadrotors, in: Proceedings of the Conference for Robotics Science and Systems VIII, Sydney, Australia, 9–13 July 2012, 8 pp., 2012.
la Cour-Harbo, A.: Publications, http://vbn.aau.dk/en/persons/pp_b77b18f5-cfd8-413a-99d6-722226da105c/publications.html, last access: 7 May 2013.
Laliberte, A. S. and Rango, A.: Incorporation of texture, intensity, hue, and saturation for rangeland monitoring with unmanned aircraft imagery, in: The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences, GEOBIA 2008, Vol. XXXVIII-4/, Calgary, Alberta, Canada, 5–8 August 2008.
Lamb, D.: The use of qualitative airborne multispectral imaging for managing agricultural crops – a case study in south-eastern Australia, Aust. J. Exp. Agr., 40, 725–738, 2000.
Langley, R. B.: Innovation: the GPS error budget, GPS World, 8, 51–56, 1997.
Lebourgeois, V., Bégué, A., Labbé, S., Mallavan, B., Prévot, L., and Roux, B.: Can Commercial Digital Cameras Be Used as Multispectral Sensors? A Crop Monitoring Test, Sensors, 8, 7300–7322, https://doi.org/10.3390/s8117300, 2008.
Lee, W., Slaughter, D. C., and Giles, D.: Robotic Weed Control System for Tomatoes, Precis. Agric., 1, 95–113, 1999.
Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbé, S., and Baret, F.: Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots, Sensors, 8, 3557–3585, https://doi.org/10.3390/s8053557, 2008.
Lindström, M., Orebäck, A., and Christensen, H. I.: BERRA: a research architecture for service robots, in: Proceedings of the 2000 IEEE International Conference on Robotics and Automation, IEEE, San Francisco, CA, USA, 24–28 April 2000, 3278–3283, https://doi.org/10.1109/ROBOT.2000.845168, 2000.
Link, J., Zillmann, E., Graeff, S., Batchelor, W., and Claupein, W.: Method for generating site-specific prescription maps via offline technique, in: Mitteilungen der Gesellschaft für Pflanzenbauwissenschaften, Vol. 19, 224–225, Schmidt & Klaunig KG, Kiel, Germany, Bonn, Germany, 2007.
Link-Dolezal, J., Reidelstürz, P., Graeff, S., and Claupein, W.: Use of a UAV for acquisition of multispectral data in winter wheat, in: Precision Agriculture Reloaded – Informationsgestützte Landwirtschaft, 105–108, Gesellschaft für Informatik (GI), Stuttgart, Germany, 2010.
Link-Dolezal, J., Kittmann, K., Senner, D., and Claupein, W.: Testing a Mini UAS to collect geo-referenced data for agricultural purposes, in: Proceedings of the 3rd International Conference on Machine Control & Guidance, Stuttgart, Germany, 27–29 March 2012, 224–234, 2012.
López-Granados, F.: Weed detection for site-specific weed management: mapping and real-time approaches, Weed Res., 51, 1–11, https://doi.org/10.1111/j.1365-3180.2010.00829.x, 2011.
Luo, R., Yih, C., and Su, K.: Multisensor fusion and integration: : Approaches, applications, and future research directions, IEEE Sens. J., 2, 107–119, 2002.
Maidl, F.-X., Huber, G., and Schächtl, J.: Strategies for site specific nitrogen fertilisation on winter wheat, in: Proceedings of the 7th International Conference on Precision Agriculture, Minneapolis, MN, USA, 25–28 July 2004, 1938–1948, 2004.
Makarenko, A., Brooks, A., and Kaupp, T.: Orca: Components for Robotics, in: Proceedings of the International Conference on Intelligent Robots and Systems (IROS), Workshop on Robotic Standardization, Beijing, China, 9–15 October 2006, 5 pp., 2006.
Marrazzo, W. N., Heinemann, P. H., Crassweller, R. E., and LeBlanc, E.: Electronic nose chemical sensor feasibility study for the differentiation of apple cultivars, Trans. ASAE, 48, 1995–2002, 2005.
Martinon, V., Fadailli, E. M., Evain, S., and Zecha, C. W.: Multiplex: An innovative optical sensor for diagnosis, mapping and management of nitrogen on wheat, in: Proceedings of the 8th European Conference on Precision Agriculture (ECPA), Prague, Czech Republic, 11–14 July 2011, 547-561, 2011.
Maurer, M.: Carolo-Cup, http://www.carolo-cup.de/, last access: 7 May 2013.
McBratney, A., Whelan, B., Ancev, T., and Bouma, J.: Future Directions of Precision Agriculture, Precis. Agric., 6, 7–23, https://doi.org/10.1007/s11119-005-0681-8, 2005.
Merriam-Webster Inc.: Platform, http://www.merriam-webster.com/dictionary/platform, last access: 7 May 2013.
Merz, T. and Chapman, S.: Autonomous unmanned helicopter system for remote sensing missions in unknown environments, in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g 2011), Vol. 38-1/C22, 6 pp., Zurich, Switzerland, 14–16 September 2011.
Millonas, M.: Swarms, phase transitions, and collective intelligence, in: Proceedings of the 3rd Conference on Artificial Life, Santa Fe, NM, USA, 15–19 June 1992, 30 pp., 1992.
Montemerlo, M., Roy, N., and Thrun, S.: Perspectives on standardization in mobile robot programming: the carnegie mellon navigation (carmen) toolkit, in: Proceedings of the International Conference on Intelligent Robots and Systems (IROS), IEEE, Las Vegas, NV, USA, 3, 2436–2441, https://doi.org/10.1109/IROS.2003.1249235, 2003.
Moore, M. M. and Lu, B.: Autonomous Vehicles for Personal Transport: A Technology Assessment, Social Science Research Network, 13 pp., https://doi.org/10.2139/ssrn.1865047, 2011.
Moran, M., Inoue, Y., and Barnes, E.: Opportunities and limitations for image-based remote sensing in precision crop management, Remote Sens. Environ., 61, 319–346, https://doi.org/10.1016/S0034-4257(97)00045-X, 1997.
μDrones: Micro drone autonomous navigation for environment sensing, http://www.ist-microdrones.org/, last access: 7 May 2013.
Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F., and MacDonald, L.: Spatial Variability of Measured Soil Properties across Site-Specific Management Zones, Soil Sci. Soc. Am. J., 69, 1572–1579, https://doi.org/10.2136/sssaj2005.0062, 2005.
Nebot, P., Torres-Sospedra, J., and Martínez, R. J.: A new HLA-based distributed control architecture for agricultural teams of robots in hybrid applications with real and simulated devices or environments, Sensors, 11, 4385–4400, https://doi.org/10.3390/s110404385, 2011.
Nielsen, K., Appel, J., and Demazeau, Y.: Applying AI to Cooperating Agricultural Robots, in: Proceedings of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI), Athens, Greece, 7–9 June 2006, 262–270, https://doi.org/10.1007/0-387-34224-9_31, 2006.
Nørremark, M., Griepentrog, H. W., Nielsen, J., and Søgaard, H. T.: Evaluation of an autonomous GPS-based system for intra-row weed control by assessing the tilled area, Precis. Agric., 13, 149–162, https://doi.org/10.1007/s11119-011-9234-5, 2011.
Orebäck, A.: A Component Framework for Autonomous Mobile Robots, Ph.D. thesis, KTH Royal Institute of Technology, Stockholm, Sweden, 143 pp., 2004.
Orebäck, A. and Christensen, H. I.: Evaluation of Architectures for Mobile Robotics, Auton. Robots, 14, 33–49, 2003.
Osterloh, C., Meyer, B., Amory, A., Pionteck, T., and Maehle, E.: MONSUN II – Towards Autonomous Underwater Swarms for Environmental Monitoring, in: International Conference on Intelligent Robots and Systems (IROS2012), Workshop on Robotics for Environmental Monitoring, Vilamoura, Algarve, Portugal, 7–12 October 2012, 6 pp., 2012.
Øvergaard, S. I., Isaksson, T., Kvaal, K., and Korsaeth, A.: Comparisons of two hand-held, multispectral field radiometers and a hyperspectral airborne imager in terms of predicting spring wheat grain yield and quality by means of powered partial least squares regression, J. NIR Spectrosc., 18, 247–261, https://doi.org/10.1255/jnirs.892, 2010.
Pedersen, S. M., Fountas, S., Have, H., and Blackmore, B. S.: Agricultural robots – system analysis and economic feasibility, Precis. Agric., 7, 295–308, https://doi.org/10.1007/s11119-006-9014-9, 2006.
Pe{ñ}a-Barragán, J. M., Kelly, M., de Castro, A. I., and Lopez-Granados, F.: Discrimination of crop rows using object-based approaches in UAV images for early site-specific weed management in maize fields, in: Proceedings of the 1st International Conference on Robotics and Associated High Technologies and Equipment for Agriculture, Pisa, Italy, 19–21 September 2012, 249–254, 2012.
Pfeifer, R. and Scheier, C.: Understanding Intelligence, 3rd Edn., MIT Press, Cambridge, MA, USA, 720 pp., 2001.
Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., and Upchurch, D. R.: Blended remote sensing tools for house management, Photogramm. Eng. Rem. S., 69, 647–664, 2007.
Princeton University: Platform, http://wordnetweb.princeton.edu/perl/webwn?s=Platform, last access: 7 May 2013.
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., and Ng, A.: ROS: an open-source Robot Operating System, in: Proceedings of the International Conference on Robotics and Automation (ICRA), Workshop on Open Source Software in Robotics, Kobe, Japan, 12–17 May 2009, 6 pp., 2009.
Rabatel, G., Gorretta, N., and Labbé, S.: Getting simultaneous red and near infrared bands from a single digital camera for plant monitoring applications, in: Proceedings of the International Conference of Agricultural Engineering (CIGR-AgEng), Valencia, Spain, 8–12 July 2012, 6 pp., 2012.
Reid, J. F., Zhang, Q., Noguchi, N., and Dickson, M.: Agricultural automatic guidance research in North America, Comput. Electron. Agric., 25, 155–167, https://doi.org/10.1016/S0168-1699(99)00061-7, 2000.
Reyniers, M., Vrindts, E., and De Baerdemaeker, J.: Fine-scaled optical detection of nitrogen stress in grain crops, Opt. Eng., 43, 3119–3129, https://doi.org/10.1117/1.1811084, 2004.
RHEA: Robot Fleets for Highly Effective Agriculture and Forestry Management, http://www.rhea-project.eu/, last access: 7 May 2013.
Richharia, M.: Satellite Communication Systems: Design Principles, McGraw-Hill Inc., USA, 2nd Edn., 1999.
Rieke, M., Foerster, T., Geipel, J., and Prinz, T.: High-precision positioning and real-time data processing of uav-systems, in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g 2011), Vol. 38-1/C22, 6 pp., Zurich, Switzerland, 14–16 September 2011.
Rothman, P. L. and Denton, R. V.: Fusion or confusion: knowledge or nonsense?, in: Proceedings of SPIE, 1470, 2–12, https://doi.org/10.1117/12.44835, 1991.
Ruckelshausen, A.: New sensor developments, in: Proceedings of the 1st Julius-Kühn-Symposium on Sensory in Crop Production, Quedlinburg, Germany, 21 June 2012, 6, 2012.
Ruckelshausen, A., Klose, R., Linz, A., Marquering, J., Thiel, M., and Tölke, S.: Autonomous robots for weed control, in: Proceedings 23rd German Conference on Weed Biology and Weed Control, Stuttgart-Hohenheim, Germany, 7–9 March 2006, 173–180, 2006.
Sainz-Costa, N., Ribeiro, A., Burgos-Artizzu, X. P., Guijarro, M., and Pajares, G.: Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed., Sensors, 11, 7095–109, https://doi.org/10.3390/s110707095, 2011.
Samson, G., Tremblay, N., Dudelzak, A., Babichenko, S., Dextraze, L., and Wollring, J.: Nutrient stress of corn plants: early detection and discrimination using a compact multiwavelength fluorescent lidar, in: 20th EARSeL Symposium, 4th EARSeL Workshop Lidar Remote Sensing of Land and Sea, 1, 214–223, Dresden, Germany, 16–17 June 2000.
Saust, F., Wille, J. M., Lichte, B., and Maurer, M.: Autonomous Vehicle Guidance on Braunschweig's inner ring road within the Stadtpilot Project, in: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), IEEE, Baden-Baden, Germany, 5–9 June, 169–174, https://doi.org/10.1109/IVS.2011.5940568, 2011.
Schattenberg, J., Lang, T., Becker, M., Batzdorfer, S., Hecker, P., and Andert, F.: Next UAV – Precise relative positioning using low-cost GNSS coupled with INS, optical-flow and cooperative localization methods, in: Proceedings of the 69th International Conference on Agricultural Engineering, Hanover, Germany, 11–12 November 2011, 487–492, 2011.
Schilling, K. and Jungius, C.: Mobile robots for planetary exploration, Control Eng. Pract., 4, 513–524, https://doi.org/10.1016/0967-0661(96)00034-2, 1996.
Schoellig, A. P., Mueller, F. L., and D'Andrea, R.: Optimization-based iterative learning for precise quadrocopter trajectory tracking, Auton. Robots, 33, 103–127, https://doi.org/10.1007/s10514-012-9283-2, 2012.
Schuler, H.: Analysis of expediture-to-benefits-ratio of advanced control strategies for chemical processes, Automatisierungstechnische Praxis, 36, 28–40, 1994.
Schulz, V., Gerlach, G., and Röbenack, K.: Compensation method in sensor technology: a system-based description, J. Sens. Sens. Syst., 1, 5–27, https://doi.org/10.5194/jsss-1-5-2012, 2012.
Seelan, S. K., Laguette, S., Casady, G. M., and Seielstad, G. A.: Remote sensing applications for precision agriculture: A learning community approach, Remote Sens. Environ., 88, 157–169, https://doi.org/10.1016/j.rse.2003.04.007, 2003.
Shiratsuchi, L., Ferguson, R., Adamchuk, V. I., Shanahan, J., and Slater, G.: Integration of ultrasonic and active canopy sensors to estimate the in-season nitrogen content for corn, in: Proceedings of the 39th North Central Extension-Industry Soil Fertility Conference, Des Moines, IA, USA, 18–19 November 2009, 18–19, 2009.
Sibley, K. J., Astatkie, T., Brewster, G., Struik, P. C., Adsett, J. F., and Pruski, K.: Field-scale validation of an automated soil nitrate extraction and measurement system, Precis. Agric., 10, 162–174, https://doi.org/10.1007/s11119-008-9081-1, 2008.
Siméon, T., Leroy, S., and Laumond, J.-P.: Path Coordination for Multiple Mobile Robots: A Resolution-Complete Algorithm, IEEE Trans. Robotics Autom., 18, 42–49, 2002.
Slaughter, D. C., Giles, D., and Downey, D.: Autonomous robotic weed control systems: A review, Comput. Electron. Agric., 61, 63–78, https://doi.org/10.1016/j.compag.2007.05.008, 2008.
Slaughter, D. C., Perez-Ruiz, M., Fathallah, F., Upadhyaya, S., Gliever, C. J., and Miller, B.: GPS-Based Intra-Row Weed Control System: Performance and Labor Savings, in: Proceedings of the International Conference of Agricultural Engineering (CIGR-AgEng), 5th Automation Technology for Off-road Equipment Conference (ATOE), Valencia, Spain, 8–12 July 2012, 135–139, 2012.
Smith, K.: Does off-farm work hinder "smart" farming?, Agricultural Outlook – United States Economics and Statistics Service, 294, 28–30, 2002.
Smithers, T.: Autonomy in robots and other agents., Brain Cognition, 34, 88–106, https://doi.org/10.1006/brcg.1997.0908, 1997.
Søgaard, H. and Olsen, H.: Determination of crop rows by image analysis without segmentation, Comput. Electron. Agric., 38, 141–158, https://doi.org/10.1016/S0168-1699(02)00140-0, 2003.
Stein, A., Hoosbeek, M., and Sterk, G.: Space-Time Statistics for Decision Support to Smart Farming, in: Ciba Foundation Symposium 210 – Precision Agriculture: Spatial and Temporal Variability of Environmental Quality, Lake, J. V. and Bock G. R. and Goode J. A. (Eds.), John Wiley & Sons, Ltd., Chichester, UK, 8, 120–140, https://doi.org/10.1002/9780470515419.ch8, 2007.
Steiner, U., Bürling, K., and Oerke, E.-C.: Sensor Use in Plant Protection, Healthy Plants, 60, 131–141, https://doi.org/10.1007/s10343-008-0194-2, 2008.
Taylor, J. A., McBratney, A., Viscarra Rossel, R. A., Minansy, B., Taylor, H., Whelan, B., and Short, M.: Development of a Multi-Sensor Platform for Proximal Soil Sensing, in: Proceedings of the 18th World Congress of Soil Science, Philadelphia, PA, USA, 9–15 July 2006, 740a, 2006.
TheUAV: The UAV – Unmanned Aerial Vehicle, http://www.theuav.com/, last access: 7 May 2013.
Tillett, N. D. and Hague, T.: Computer-Vision-based Hoe Guidance for Cereals – an Initial Trial, J. Agric. Eng. Res., 74, 225–236, https://doi.org/10.1006/jaer.1999.0458, 1999.
Tobe, F.: The Robot Report, http://www.therobotreport.com/, last access: 7 May 2013.
Tremblay, N., Wang, Z., and Cerovic, Z. G.: Sensing crop nitrogen status with fluorescence indicators. A review, Agron. Sustain. Dev., 32, 451–464, https://doi.org/10.1007/s13593-011-0041-1, 2011.
Tucker, C. J.: Remote sensing of leaf water content in the near infrared, Remote Sens. Environ., 10, 23–32, https://doi.org/10.1016/0034-4257(80)90096-6, 1980.
Tucker, C. J. and Sellers, P. J.: Satellite remote sensing of primary production, Int. J. Remote Sens., 7, 1395–1416, https://doi.org/10.1080/01431168608948944, 1986.
Turner, D., Lucieer, A., and Watson, C.: An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds, Remote Sens., 4, 1392–1410, https://doi.org/10.3390/rs4051392, 2012.
Watts, A. C., Ambrosia, V. G., and Hinkley, E. A.: Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use, Remote Sens., 4, 1671–1692, https://doi.org/10.3390/rs4061671, 2012.
Weis, M.: An image analysis and classification system for automatic weed species identification in different crops for precision weed management, Ph.D. thesis, University of Hohenheim, Stuttgart, Germany, 126 pp., 2010.
Zarco-Tejada, P. J., González-Dugo, V., and Berni, J.: Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera, Remote Sens. Environ., 117, 322–337, https://doi.org/10.1016/j.rse.2011.10.007, 2012.
Zhang, C. and Kovacs, J. M.: The application of small unmanned aerial systems for precision agriculture: a review, Precis. Agric., 13, 693–712, https://doi.org/10.1007/s11119-012-9274-5, 2012.
Zhang, N., Wang, M., and Wang, N.: Precision agriculture – a worldwide overview, Comput. Electron. Agric., 36, 113–132, https://doi.org/10.1016/S0168-1699(02)00096-0, 2002.
Zillmann, E., Graeff, S., Link, J., Batchelor, W. D., and Claupein, W.: Assessment of Cereal Nitrogen Requirements Derived by Optical On-the-Go Sensors on Heterogeneous Soils, Agron. J., 98, 682–690, https://doi.org/10.2134/agronj2005.0253, 2006.
Zimmer, U. R.: Autonomous Underwater Vehicles, http://www.transit-port.net/Lists/AUVs.html, last access: 7 May 2013.