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UAV-Based High-Resolution Remote Sensing as an Innovative Monitoring Tool for Effective Crop Management
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UAV-Based High-Resolution Remote Sensing as an Innovative Monitoring Tool for Effective Crop Management

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Held at the AAB Conference on Crop Resource Use Efficiency and Field Phenotyping in Grantham, UK on 25-26 March 2013.

Held at the AAB Conference on Crop Resource Use Efficiency and Field Phenotyping in Grantham, UK on 25-26 March 2013.

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  • 1. UAV-Based High-Resolution Remote Sensing asan Innovative Monitoring Tool for Effective Crop Management Christian Knoth, Torsten Prinz Institute for Geoinformatics, University of Muenster Crop Resource Use Efficiency and Field Phenotyping Grantham, Lincs, 2013-03-26 http://ifgicopter.uni-muenster.de
  • 2. Overview I. Introduction II. Quadrotor Sensor Platforms III. Infrared Imaging IV. Monitoring of Bog Ecosystems V. Precision Agriculture VI. Perspectives for Field Phenotyping2 http://ifgicopter.uni-muenster.de
  • 3. Introduction The ifgicopter project: Aim: using multicopters as platforms for gathering all kinds of sensor data • high flexibility • VTOL (hovering) • little operating costs • variable spatial and temporal resolution3 http://ifgicopter.uni-muenster.de
  • 4. Introduction Fields of work: • flight planning software • communication framework • accurate positioning via enhanced differential GPS (DGPS) • creation of (infrared) remote sensing products • analysis of climate phenomena • …4 http://ifgicopter.uni-muenster.de
  • 5. Quadrotor Sensor Platforms “Mikrokopter” “Microdrones” (building kit) (ready-to-use product)  carries e.g. IXUS400 or  carries e.g. IXUS 100IS (NIR only, natural colour) (VIS-NIR) autonomous navigation via GPS and flight planning software5 http://ifgicopter.uni-muenster.de
  • 6. Infrared Imaging Sensor Technique: • modified digital compact camera • hot mirror removed • captures light between 400 and 1100 nm wavelength • natural colour, NIR or CIR images (external filter)6 http://ifgicopter.uni-muenster.de
  • 7. Infrared Imaging Outcomes:  colour infrared  near infrared • bog under restoration  RGB true colour  CIR composite (NIR/B) • turnip field7 http://ifgicopter.uni-muenster.de
  • 8. Monitoring of Bog Ecosystems object-based classification ● waterlogged bare peat ● birch trees (Betula pubescens) ● cotton grass (Eriophorum vaginatum) ● sphagnum moss (Sphagnum spec.) ● result8 http://ifgicopter.uni-muenster.de
  • 9. Precision AgricultureNitrogen Management • CIR Image m 25 509 http://ifgicopter.uni-muenster.de
  • 10. Precision AgricultureNitrogen Management • CIR Image • classified GNDVI m 25 5010 http://ifgicopter.uni-muenster.de
  • 11. Precision AgricultureNitrogen Management • CIR Image • classified GNDVI • application map m 25 5011 http://ifgicopter.uni-muenster.de
  • 12. Precision Agriculture Weed Detection m 25 50 • CIR Image • high resolution image12 http://ifgicopter.uni-muenster.de
  • 13. Precision Agriculture Weed Detection m 25 50 • CIR Image • high resolution image • crop row detection13 http://ifgicopter.uni-muenster.de
  • 14. Precision Agriculture Weed Detection m 25 50 • CIR Image • high resolution image • crop row detection • weed detection14 http://ifgicopter.uni-muenster.de
  • 15. Precision Agriculture Weed Detection m 25 50 • CIR Image • high resolution image • crop row detection • weed detection • application map15 http://ifgicopter.uni-muenster.de
  • 16. Precision Agriculture Multicopter UAV „Hexe“ (University of Hohenheim) • point spectrometer MMS1 (tec5) + webcam • temperature and humidity sensors • data transfer via W-LAN © Jakob Geipel/Institute for Crop Sciences/Agronomy, University of Hohenheim16 http://ifgicopter.uni-muenster.de
  • 17. Precision Agriculture Multicopter UAV „Hexe“ (University of Hohenheim) • point spectrometer MMS1 (tec5) + webcam • temperature and humidity sensors • data transfer via W-LAN © Jakob Geipel/Institute for Crop Sciences/Agronomy, University of Hohenheim17 http://ifgicopter.uni-muenster.de
  • 18. Precision Agriculture Fixed-wing UAV „E-Trainer“ (University of Hohenheim)  Spectrometer readings © Johanna Link-Dolezal/Institute for Crop Sciences/Agronomy, University of Hohenheim18 http://ifgicopter.uni-muenster.de
  • 19. Perspectives for Field Phenotypingpotential of using multicopters ?  mean NIR/Blue ratio (OBIA) ● non-invasive ● variable flight level and ground resolution ● VTOL capability ● piloting easy to learn ● reliable ● customizable  sensors  flight equipment  interfaces19 http://ifgicopter.uni-muenster.de
  • 20. Perspectives for Field Phenotyping Lightweight Sensors ? ● multispectral sensors www.tetracam.com ● 3D cameras www.digitalkamera.de ● hyperspectral imaging sensors ● thermal imaging sensors www.headwallphotonics.com ● … www.thermoteknix.com20 http://ifgicopter.uni-muenster.de
  • 21. Perspectives for Field Phenotyping Automation of data handling and processing ? data acquisition analysis of phenotypic traits subsequent crop management21 http://ifgicopter.uni-muenster.de
  • 22. Perspectives for Field Phenotyping Automation of data handling and processing ?  might look like this: “Sensor Platform Framework”1 ● communication with multi- sensor equipped UAVs ● synchronizes and couples data from different sensors (e.g. sensor measurement + position information) ● provides interface for additional output plugins 1Rieke (2010): Entwicklung eines Frameworks zur Anbindung von Multi-Sensor-Plattformen an das Sensor Web https://wiki.52north.org/bin/view/SensorWeb/SensorPlatformFramework22 http://ifgicopter.uni-muenster.de
  • 23. Perspectives for Field Phenotyping Automation of data handling and processing ?  might look like this: “Sensor Platform Framework”1 ● communication with multi- sensor equipped UAVs ● synchronizes and couples data from different sensors (e.g. sensor measurement + position information) ● provides interface for additional output plugins 1Rieke (2010): Entwicklung eines Frameworks zur Anbindung von Multi-Sensor-Plattformen an das Sensor Web https://wiki.52north.org/bin/view/SensorWeb/SensorPlatformFramework23 http://ifgicopter.uni-muenster.de
  • 24. Perspectives for Field Phenotyping Automation of data handling and processing ?  might look like this: Framework for automatic processing1 ● automatic processing (biomass mapping / weed detection) and integration of results into subsequent applications ● web-based  easy access and interoperability ● extensible 1Drerup (2012):An automatic web-based framework to integrate UAV-based data into precision farming applications24 http://ifgicopter.uni-muenster.de
  • 25. Conclusion • promising means of data acquisition also for field phenotyping • flight duration and payload is constantly being enhanced • increasing number of applicable sensors and data processing/management techniques • application development needed25 http://ifgicopter.uni-muenster.de
  • 26. Thank you for your kind attention! Questions? http://ifgicopter.uni-muenster.de26