Drone sensing A new perspective on cereal disease management? David Whattoff - Agricultural Development Manager
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1 Drone sensing A new perspective on cereal disease management? David Whattoff - Agricultural Development Manager
2 SOYL Precision Ag National coverage Provide application plans to 1,000,000 ha per year PA services based on Nutrient grid sampling EC soil surveys Nitrogen management UAV imagery Field scale R & D program
3 Precision Agriculture The right amount, in the right place, at the right time, in the right way Decision making Input optimisation Yield improvement Record keeping Sustainably Traceability
4 Overview of current sensor technology Dr Anne-Katrin Mahlein,
5 SOYLsight UAV Geo Copter X-8000
6 Replicated plot analysis 24MP Colour (RGB) Narrow band multispectral Combined RGB & MS Analysis
7 Field scale assessment
8 Example data Plant counts: Lettuce
9 Example data Plant counts: Potatoes
10 Example data Canopy heights: PGR cereals trial Units in metres Height at canopy (G2) height at bare ground (G1)
11 Precision crop protection Commercial drones
12 Precision crop protection - Commercial sensors
13 Precision crop protection Data analysis
14 Precision crop protection - Commercial sensors
15 Precision agriculture approach Variable PGR NDVI High Med Low
16 PGR dose rate Dose rate calculation Max PGR app Max limit of NDVI (95% of max) (NDVI max) Average app Average NDVI Min PGR app Lower limit of NDVI (NDVI cut-off) NDVI A linear PGR ramp to inform application rate
17 Precision agriculture approach Variable PGR
18 Precision Crop Protection The challenges of disease monitoring & management
19 Canopy sensing limitations -Speciation Dr Anne-Katrin Mahlein,
20 Characteristic spectral signatures Dr Anne-Katrin Mahlein,
21 Vegetation Index A definition A Vegetation Index (VI) is a spectral transformation of two or more bands designed to enhance the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations Huete, A.; Didan K., Miura T., Rodriquez E. P., Gao X. & Ferreria L. G. (2000). "Overview of the radiometric and biophysical performance of the MODIS vegetation indices". Remote Sensing of Environment. 83:
22 Disease monitoring Vegetation Indexes
23 Disease monitoring Trials
24 Disease monitoring Vegetation Indexes KWS Kielder KWS Siskin KWS Kielder Pre-T0 T0 T1 T1.5 T2 T3 GS26 BBCH 30- BBCH 31- (Leaf 2 BBCH BBCH %) rd March 2nd April (leaf 3 50%) 3rd May 9th May 22nd May 9th June untreated untreated untreated untreat untreat untrea ed ed ted untreated untreated untreated untreat untreat untrea ed ed ted - - Strand Piper Skywa Prosar y 1.0 o KWS Siskin - - Strand Piper Skywa y 1.0 Prosar o 0.8 REP3 REP2 REP Siskin Kielder Siskin Kielder KWS 5 Kielder KWS 6 Siskin KWS 7 Kielder KWS 8 Siskin KWS 9 Kielder 10 KWS Siskin 11 KWS Kielder 12 KWS Siskin Ceriax Timpani Piper 1.0 Ceriax Timpani Piper 1.0 Ceriax Timpani Piper 1.0 Ceriax Timpani Piper 1.0 Timpani 2.0 Ceriax Piper 1.0 Piper 1.0 Timpani 2.0 Ceriax Piper 1.0 Piper 1.0 Ceriax Strand Toledo 0.3 Timpani 2.0 Piper Ceriax Strand Toledo 0.3 Timpani 2.0 Piper Skywa y 1.0 Skywa y 1.0 Skywa y 1.25 Skywa y 1.25 Skywa y1.25 Skywa y1.25 Skywa y 1.25 Skywa y 1.25 Prosa ro 0.6 Prosa ro 0.6 Prosar o 0.8 Prosar o 0.8 Prosar o 0.8 Prosar o 0.8 Prosar o 0.8 Prosar o 0.8
25 Disease monitoring NDVI 23/05/16 Septoria sp. Septoria sp. Septoria sp. Yellow rust Yellow rust Yellow rust Yellow rust YIELD LEAF3 P LEAF4 P LEAF5 P LEAF2 P LEAF3 P LEAF4 P LEAF5 P LEAF4 C RENDVI t/ha PESSEV PESSEV PESSEV PESSEV PESSEV PESSEV PESSEV green area 09/08/201 6 % % % % % % % INDEX 23/05/201 23/05/201 23/05/201 23/05/201 23/05/201 23/05/201 23/05/201 23/05/ KWS Kielder KWS Siskin KWS Kielder KWS Siskin KWS Kielder KWS Siskin TCARI 7 KWS Kielder KWS Siskin KWS Kielder KWS Siskin KWS Kielder KWS Siskin
26 Disease monitoring NDVI 13/06/16 YIELD LEAF1 P LEAF2 P LEAF3 P PLANT C t/ha PESSEV PESSEV PESSEV green area RENDVI % % % INDEX 09/08/ /06/ /06/ /06/ /06/ KWS Kielder KWS Siskin KWS Kielder KWS Siskin KWS Kielder KWS Siskin KWS Kielder TCARI 8 KWS Siskin KWS Kielder KWS Siskin KWS Kielder KWS Siskin
27 Image Timing AHDB Cereals & Oilseed
28 Additional factors affecting canopy sensing Different crops and row spacing Temporal & season changes Ambient light conditions Soil nutrition imbalances Weed patches Disease Water-logging
29 Disease Management Big data analytics
30 Summary Drones are just the platform, it is the sensor that is key. Effective results will only be achievable through correctly interpreted data. Disease monitoring is possible. Disease management will be possible.
31 Thank you David Whattoff - Agricultural Development Manager
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