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IGTF-ASPRS 2015 Hyperspectral Imagery Advancing Agriculture

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Presentation on hyperspectral imagery uses in production agriculture.

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IGTF-ASPRS 2015 Hyperspectral Imagery Advancing Agriculture

  1. 1. Advanced Reconnaissance Corp. Trends –Hyperspectral Imagery Advancing Production Agriculture
  2. 2. Contact Information Nate J. Taylor, Director Commercial Business Development, Marketing & Sales • 15 years project management/product development experience • Chevron • John Deere • ZedX • GeoVantage ntaylor@advrecon.com +1 (814) 441-1867 http://advrecon.com Facebook, Vimeo @natejtaylor @advrec 2
  3. 3. Ag Technology Evolution 1980 1990 1995 2000 2010 Today Technology Traditional Equipment Pusedo GIS Yield Monitors Real GIS Application Control Machine Display Machine Guidance Implement Guidance Crop Models Tracking Machine Steering GPS Positioning Lightbar Guidance Remote Sensing Sensing Technologies GPS Augmentation Telemetry Hitch Technologies Automation Display Technologies Video/Vision Technologies Positioning Systems Autonomous Operation Swarms Data analysis Self Calibrating Equipment Rule Based Decision 3
  4. 4. Shortening the Cycle Remote Sensing 2015 2018 AgVu 4
  5. 5. Advanced Reconnaissance Corp. at a Glance Agriculture Sustainability Military ARC Produces and Operates the Most Advanced—Yet Least Complex-- Hyperspectral Imaging Systems in the World --- For Rapid Information to Users Crops of Interest IED Indicators Crop Health / Maturity Stressed Vegetation 5
  6. 6. ARC’S TECHNOLOGY • Visible / near infrared & thermal • 151 spectral bands • Uses full spectrum of information 6
  7. 7. Introduction to AgVu Applicability to Quantifying Crop Stress • Crop Stress has traditionally been difficult to automatically quantify for remote sensing. Why?  Many things effect multispectral signatures similarly  Plant signatures can be different at different locations  Plant signatures change over time (as vegetation matures)  Disease is initially subtle in the plant signature • AgVu  Hyperspectral imaging based content  Has 50x the information content as NDVI or multispectral  False colors separate information from different diseases, pests, etc 7
  8. 8. 1 0.45 to 0.52 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.40 0.47 0.53 0.59 0.64 0.71 0.78 0.99 1.15 1.30 1.45 1.62 1.87 2.17 longitud de onda (micrones) reflectancia Visible Reflected infrared Near infrared Short wave infrared Leaf pigments Leaf cell structure Water content Region of Spectrum Wavelength (microns) Reflectance 2 0.52 to 0.60 3 0.63 to 0.69 5 1.55 to 1.75 7 2.08 to 2.35 TODAY’S MULTISPECTRAL IMAGINGTODAY’S MULTISPECTRAL IMAGING Image collectionImage collection 4 0.79 to 0.90 8
  9. 9. • Current analysis based on only 4 bands • Data is normalized • Variability Limited • Ratio of 2 bands to create NDVI • Relative Index Band 3 Band 1 Band 2 Band 4 TODAY’S MULTISPECTRAL IMAGINGTODAY’S MULTISPECTRAL IMAGING Image analysisImage analysis 9
  10. 10. Detecting “The Shift” Hundreds of narrow contiguous spectral bands Data •Yield •Multispectral •As Applied Information •Hyperspectral Knowledge •Agronomic •Quality Decisions •Variety Selection •Storage •Many more The Knowledge Gap Barrier ARC’s analysis breaks “the agriculture knowledge gap barrier” Hyperspectral Imagery 10
  11. 11. Hyperspectral Imaging in Real-World PlantType(s) Real-World Agronomics i 1 i 2 i 3 i 4 Cause Real Signature Differences i 5 AgVu Decodes the Signature into Stable Information Levels i 6 i 7 Difficult to Separate using the Signature 11
  12. 12. The ARC Difference The Information Layers are Accessed By “Flipping Different Switches” in the Software Layer 2 Shows Crop Health Layer 1 Shows Crop Type Layer 3 Shows Crop Cultivars 12
  13. 13. MORE INFORMATION, BETTER DECISIONSMORE INFORMATION, BETTER DECISIONS 13 4 bands: What do you see?4 bands: What do you see?
  14. 14. 7 bands: What do you see?7 bands: What do you see? 14 MORE INFORMATION, BETTER DECISIONSMORE INFORMATION, BETTER DECISIONS
  15. 15. 15 151 bands: What do you see?151 bands: What do you see? MORE INFORMATION, BETTER DECISIONS
  16. 16. Decisions are only as good as the amount of information available True Color NDVI AgVu 50X as much information means subtle issues are evident earlier MORE INFORMATION, BETTER DECISIONS 16
  17. 17. Soybeans What Hybrid is Planted? Crop Varieties Crop Growth Inhibitors Waterway - grass Farmstead Terraces Grass Waterway Farmstead Line Oldfenceline Corn - 2 Hybrids Alternate 16 rows Corn - different hybrid Beans RR STS Conventional Corn Soybeans Different Varieties Var I Var II Var III Var IV Var 3 Var 2 Var 3 Var 4 Corn - different hybrid CRP - (10 year reserve) grass with areas mowed (dark) for thistle control 30 in row soybeans Corn Roundup Ready STS Conventional Var 2 Var 1 Var 1 Var 2 Var 4 MORE INFORMATION, BETTER DECISIONS 17
  18. 18. ThermalTrue Color AgVu Two spotted spider mite infestation It provides crop information to growers, earlier and more timely than any other way ARC’s AgVu™ Shows Early Signs of Insects in Strawberry Fields MORE INFORMATION, BETTER DECISIONS 18
  19. 19. Around 10am, Day 1 Around 12pm, Day 2 REPEATABLE ACCURACY 19
  20. 20. Wrap Up • ARC alone has the capability to automatically process hyperspectral data  Rapid turn around of actionable information creating institutional knowledge  Enables low cost identification of a variety of crop stresses • ARC’s AgVu service enables decision agriculture by bridging the “knowledge gap barrier.”  Agronomists and agriculturalists can leverage their knowledge • ARC’s algorithm can detect soil minerals, pest/disease infestation, crop health and more  Specific and quantifiable information directly from imagery 20
  21. 21. Backup
  22. 22. Detecting “The Shift”

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