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18. *Satellite, Yields and Management - Alex Whitley

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The behind the scenes of today’s satellite imagery technology and what it can do for your farm. Leander Campbell, AAFC Ottawa, Chris Olbach, Corteva Agriscience and Alex Whitley, Taranis

Publié dans : Alimentation
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18. *Satellite, Yields and Management - Alex Whitley

  1. 1. ARTIFICIAL INTELLIGENCE: 
 Weeds Emergence/Plant Population Disease Insects Eastern Ontario Crop Conference
  2. 2. 1. TRADITIONAL SCOUTING X TARANIS AI 2 99 POINTS 55 H 23 MIN 2349 Points 01H 40MIN
  3. 3. How Does it work Emergence RISK DETECTED: 5 BUSHELS PER ACRE! RISK DETECTED: 3 BUSHELS PER ACRE! RISK DETECTED: 12 BUSHELS PER ACRE!
  4. 4. 1. Emergence The combination of 8cm UHR and leaf-level imagery is the most efficient and profitable way to monitor your fields. Ground truth virtually, and easily convert your dataset into the most accurate zones and prescriptions in the industry. Begin by comparing NDVI to the Visible layer.
  5. 5. 1. Emergence Zoom into poor emergence and good emergence areas to further validate the spectrum. Note: the consistent rows and biomass abundance indicates a healthy emergence.
  6. 6. 1. Emergence Zoom into poor emergence and good emergence area to further validate the spectrum. Note: the amount of bare soil and consistency of low biomass indicates a weak emergence.
  7. 7. 1. Emergence Virtual scouting is the most efficient and cost effective way to ground truth. Next step, use 8cm UHR to build zones and prescriptions that are accurate down to the plant level. Emergence VR Zone
  8. 8. 1. Emergence
  9. 9. 1. Emergence We quantify emergence from the earliest of stages all the way until the whirl can no longer be identified singularly.
  10. 10. 1. Emergence Population Heat Maps are produced for each field and can be exported from an entire territory level for meaningful and efficient reporting.
  11. 11. 2. Weeds Even hard-to-see weeds lurking below the canopy are easily spotted by our AI.
  12. 12. 2. Weeds Several types of weeds being automatically detected
  13. 13. 2. Weeds Plant population, weeds, and volunteer corn marked as weeds!
  14. 14. 2. Weeds Plant population, weeds, and volunteer corn marked as weeds!
  15. 15. 2. Weeds Yes, we can quantify flood damage and plant population, but our AI is so advanced, it will identify weeds even when their submerged!
  16. 16. 2. Weeds Regular flights of AI2 will capture weed pressure from the earliest of stages. All weeds tagged in every image will be categorized, searchable, and can be combined for ultra-specific filtering of your entire territory. Note: the images that have been tagged with the specific threat being filtered will appear blue. The images that did not contain the threat being filtered turn white and aren’t selectable. Makes for mistake free, lightning quick, virtual scouting.
  17. 17. Weeds Filter by specific weed or weeds in general. Notice our analytics quantify how many images out of the total imaging event have the specific threat. Understanding quantities and densities of weed species will help determine product and application method. 2.
  18. 18. Disease Identifying diseases is important. Here we can see leaf blight, grey leaf spot, and the ever-present weeds. 3.
  19. 19. Disease Northern leaf blight is trouble, but AI2 continues to identify it at early stages giving you the chance to prescribe a treatment and protect yield. 3.
  20. 20. Disease Finding grey leaf spot is key for more than just treatment. Hybrid choices can be made based on spotting tolerances 3.
  21. 21. Disease Finding grey leaf spot is key for more than just treatment. Hybrid choices can be made based on spotting tolerances 3.
  22. 22. Insects Identify the migration of an insect threat throughout a geography, track back to the source field, and identify the specific insect down to the leaf level. Source field 4.
  23. 23. Insects Ai finds each insects and categorizes it accordingly. Best practice is to spot check different areas in the field, then neighboring fields, then the furthest field with the same determined threat. 4.
  24. 24. Insects Identify, Classify, and Prescribe in seconds. Go to the field armed with the solution (machine ready for application) rather than going to the field to figure out what is wrong. 4.
  25. 25. Insects Identify, Classify, and Prescribe in seconds. Go to the field armed with the solution (machine ready for application) rather than going to the field to figure out what is wrong. 4.
  26. 26. 2. Corn In-Season, General Monitoring Program satellite whole field UHR leaf - level imagery Planting Harvesting Emergence - V2 Stand count / replant V7 - V10 Tissue sampling for micronutrients / Y drops / side dress / validating early season side dressing / agronomic validation / drainage issues V3 - V6 Nutrient deficiencies / weeds / side dress / tissue & nitrate sampling / replant / agronomic validation / drainage issues Maturity Yield estimation / early market planning V11 - R1 VR nitrate sampling / Y drop / insecticide Rx / fungicide Rx / foliar feeding / agronomic validation R6 Monitoring dry-down / monitoring maturity / final agronomic validation / potential insurance claims (wind, hail, freeze, snap) Presentation 1 Deeper dive into specific threat examples in Maize and Cotton
  27. 27. 2. Soybean In-Season, General Monitoring Program satellite whole field UHR leaf - level imageryPresentation 1 Deeper dive into specific threat examples in Maize and Cotton Planting Harvesting VE - V3 Emergence / stand establishment / early insect & weed identification V6 - R1 Micro-nutrient applications / fungicide treatments / insecticides treatments R2 - Maturity Disease pressure / foliar applications / late weed infestation / fungicide V3 - V6 Weed detection / tissue sampling / drainage issues / water management Determine Growth Stage White mold / SDS / brown stem rot / micro - nutrient applications / fungicide & insecticide treatments
  28. 28. ARTIFICIAL INTELLIGENCE: 
 Weeds Emergence/Plant Population Disease Insects Eastern Ontario Crop Conference

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