UAV-based remote sensing as a monitoring tool for smallholder cropping systems

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Background/history, Project objectives, Community of Practice / Trainin

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UAV-based remote sensing as a monitoring tool for smallholder cropping systems

  1. 1. UAV-based remote sensing as a monitoring tool for smallholder cropping systems Adolfo Posadas2, Dieudonné Harahagazwe2, Arnold Bett1, Elijah Cheruiyot1,2 University of Nairobi International Potato Center (CIP) UAV Seminar, Geography Department Kenyatta University, 8 March 2016
  2. 2.  2 Introduction  Background/history  Project objectives  Community of Practice / Training Adolfo
  3. 3. Light-plant interaction Credit: Centre for Remote Imaging, Sensing and Processing (CRISP) Application examples:  Characterization of crops – stress detection  Infection  Water deficiency  Crop discrimination  Soil characterization 3 Remote Sensing in Agriculture Why UAV?  Low-cost  Detailed information  No clouds effect
  4. 4. 2002 Balloons: • Hot air. • Hellium. 2008 Model planes: • Combustion. • Electric. 2009 Helicopter: •Combustion. •Electric. 2012 Multirotor: • Quadcopter. • Octocopter. ARSP at CIP - History 4
  5. 5. The aim of this “proof of concept” project is to develop and validate a low-cost UAV-based remote sensing tool for crop area determination (ARSIS) using sweet potato as a pilot crop Specific objectives include: • Acquire and test digital commercial, multi- and hyper- spectral cameras, design, acquire, assemble, and test micro-sensors • Implement methods to remove noise and enhance relevant information from extremely high resolution imageries obtained by Drone • Introduce Multi scaling algorithms, based on non-linear approaches, capable of measuring at one scale while probabilistically making inference at other spatial scales • An out scaling plan that describes a path forward for the validated UAV-ARSIS, as a logical next step of the “Proof of Concept” project. 5 Project Objectives
  6. 6. Key Highlights: – Costs, accessibility, and user-friendliness – Involving local institution at different stages is a must – Stepwise – From simple to complex tools – Complementarity with satellite imageries – Multiple crops – Yield assessment? – Is it feasible to discriminate varieties? 6 32 Participants: – National, Regional and International institutions – 5 CGIAR Centers (CIP, ICRAF, CIAT, IITA and ILRI), and ICIPE Community of Practice Inception Workshop – October 2014, Nairobi
  7. 7.   Unmanned Aerial Vehicle (UAV) – platform  Sensors  Support system – power supply, communication, data storage Arnold 7 Hardware
  8. 8. 8 Commercial UAVs UAV Platforms Octocopter assembled in Nairobi Estimated cost: $ 5000 Quadcopter assembled in Nairobi Estimated cost: $ 4000 MikroKopter’s Electronic System $740
  9. 9. 9 Ardupilot’s Electronic System $250 Quadcopter Open Source UAV Platforms Quadcopter Open Source designed and assembled locally based on Ardupilot Estimated cost: $ 500
  10. 10. 10 Reducing Camera Cost While Improving Image Quality NDVI with TETRACAM ADC Micro NDVI with CIP Built CameraRGB Original Image – Canon EOS Sensors
  11. 11. 11 Active Sensors Under Construction Sensors Fluorescence sensor Photochemical Reflectance Index (PRI) sensor under construction
  12. 12. 12 Locally Assembled Multispectral Acquisition System Acquisition Multispectral System Assembled 1. Power Supply System 2. Mini PC (pcDuino) 3. Cameras CCD (Red & NIR) 4. LiPo Battery Holder 1 2 3 4 Data Acquisition System
  13. 13. UAV assembled in collaboration with ICRAF and UoN 13 UAV Assembly and Testing UAV used in the first field mission in Mwanza – Tanzania in April 2015
  14. 14. 14 Capacity to Repair Broken Down UAVs - Demonstrated UAV Repair and Maintenance FixedCrashed Octocopter Fixed3D-printed Quadcopter crashed
  15. 15. 15 Training Capacity UAV Demonstration at the 6th SPHI Annual Conference - Kigali Training at Ministry of Agriculture - Rwanda
  16. 16.   Spectral data acquisition  Image mosaicking  Texture-aided supervised classification Elijah 16 Software/Processing
  17. 17. 17 SpectraCIP Software USB2000+VIS-NIR-ES Spectrometer Spectral Data Acquisition Commercial Version: SpectraSuite SpectraCIP: Advantages • Open-source • Easy to store data – handy in the field
  18. 18. 18 Open Source Image Stitching Software: ISAM-CIP Mosaic produced Software mosaicks two or more images by identifying identical points in the overlap area. Mosaicking
  19. 19. 19 Texture-based Maximum Likelihood Classification of RapidEye Satellite Image Cropping areas identified from satellite images at 6.5 m resolution Cropping Area Identification at District Level • Additional texture information significantly improves discrimination results • Classified satellite image aids identification of UAV data collection sites
  20. 20. 20 Classification Results and Error Matrix Cropping Area Identification at District Level Error Matrix: • Overall Classification Accuracy = (4202/4964) 84.6495% • Kappa Coefficient = 0.8106 Class Feature Colour Number of sample points Number of feature points Percentage cover Area (Hectares) Unclassified [Black] 32,562,557 27.203 146706.04 Maize [Red] 33 1,244,964 1.040 5609.01 Trees [Sea Green] 36 9,014,849 7.531 40615.14 Shrubs [Green] 42 1,998,269 1.669 9002.92 Road_murram [Magenta] 40 7,631,747 6.376 34383.77 Road_tarmac [Purple] 36 2,209,397 1.846 9954.13 Clouds [Cyan] 66 12,263,029 10.245 55249.36 Tilled_land_soil [Maroon] 42 780,023 0.652 3514.28 Buildings [Sienna] 31 51,869,227 43.332 233689.54 Bananas [Blue] 11 111,303 0.093 501.46 Sweetpotato [Yellow] 12 15,339 0.013 69.11 Reference Classification Class Maize Trees Shrubs Murra m Tarma c Clouds Tilled land Buildin gs Banan as Sweet potato Total Maize 93 0 0 0 0 0 0 0 1 0 94 Trees 0 1304 0 0 0 0 0 0 6 0 1310 Shrubs 2 0 689 0 0 0 0 0 0 1 692 Murram 29 0 0 238 0 0 1 0 0 0 268 Tarmac 0 0 0 0 292 0 0 0 0 0 292 Clouds 0 0 0 0 2 1255 0 9 0 0 1266 Tilled land 0 0 0 0 0 0 89 0 0 0 89 Buildings 15 0 38 299 19 314 5 218 1 3 912 Bananas 6 2 4 0 0 0 0 0 12 0 24 Sweetpot ato 0 0 5 0 0 0 0 0 0 12 17 Total 145 1306 736 537 313 1569 95 227 20 16 4964
  21. 21. 21 Texture-based Classification of UAV Image Better classification results could be obtained with multispectral imageries, but this require special equipment and knowledge Crops identified at farm level from images taken with <5 cm resolution using a regular digital camera Crop Discrimination at Farm Level
  22. 22.   Multifractal analysis  Spatial scaling Adolfo 22 Non-linear Processing
  23. 23. 23 Non-linear Processing Methods 14 Sweetpotato varieties at LZARDI experiment, Mwanza Optical spectra Multifractal spectra Processing: Variety Discrimination
  24. 24. 24 Way Forward • Develop a UAV Regional Hub in Nairobi: – Training on UAV hardware and software development – Training on UAV applications • Engage in advocacy to improve an enabling environment for the use of UAVs for development in the region • Etc.
  25. 25. Videos: • AgroTV–16 – Spanish; • ARSIS Video – English; Blog Posts: • Invasion of the Potato Drones – Lima; • Community of Practice – SSA; • UAV Assembling – SSA; • Crop Discrimination; • Airborne Agriculture Photos: • Testing the Unmanned Aerial Vehicle – Flickr; Join UAV Community on Online Platforms: • UAV4Ag on DGroups; • @UAV4Ag on Twitter. 25 Learn More!
  26. 26. Thank you! Contacts: Roberto Quiroz, PhD R.Quiroz@cgiar.org International Potato Center (CIP) Q/A Contributing authors: Adolfo Posadas Arnold Bett Elijah Cheruiyot Dieudonné Harahagazwe Hildo Loayza Susan Palacios Mario Balcázar Luis Silva Roberto Quiroz

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