Big Data: Discovering the Value! of an Underutilized Asset! No-Till Conference! Cincinnati, Ohio! Jan. 16, 2015!
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1 !! Big Data: Discovering the Value! of an Underutilized Asset! No-Till Conference! Cincinnati, Ohio! Jan. 16, 2015! S.A. Shearer Food, Agricultural and Biological Engineering
2 Presentation Ground Rules! I will talk about the future no guarantees that I m right (or wrong)!! No intent to endorse one product or technology over another.! I will connect observations (mine and others) to suggest how agriculture will change.! I ve borrowed heavily from materials available via the internet, and have tried my best to credit the respective individuals.! Scarlet and Gray are the primary colors of the presentation -- observation of who pays my salary.!
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4 AgState Task Force! digital agriculture family of activities related to farming that includes precision agriculture, prescription agriculture and enterprise agriculture; and depends on the collection, use, coordination, and analysis of data from a multiplicity of sources with the goal of optimizing productivity, profitability and sustainability of farming operations.!
5
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7 Technologies that will shape the future of agriculture!
8 Google Glass?!
9 Topcon Positioning Systems IP-S2 HD!
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11 Google Car!
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13 Market forces that will shape the future of agriculture!
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15 Reducing Fertilizer Use in Agriculture. Walmart is requiring suppliers who use commodity grains, such as corn, wheat and soy in their products, to develop a fertilizer optimization plan that outlines clear goals to improve performance based on Index research. Through this program, the company and its suppliers have the potential to reduce fertilizer use on 14 million acres of farmland in the U.S. by 2020.!
16 Current precision ag technologies!
17 What is Big Data?! NSF recently referred to Big Data as large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, , video, click streams, and/or all other digital sources.!
18 What is the value of Big Data! if we don t produce" actionable information?!
19 What s driving planter development?!
20 Precision Planting Sensing/Control!
21 Kinze Multi-Hybrid!
22 CleanSeed CX-6 Smart Seeder!
23 Controlling Canopy Architecture" Torres, Vossenkemper, Raun and Taylor (Oklahoma State University)!
24 OSU/Beck s Field Investigations!
25 OSU/Beck s Field Investigations!
26 Telematics offerings!
27 Fuel Use Rate Distributions! Frequency! 6000! 5000! 4000! 3000! 2000! 1000! 0! Planting! 0! 2! 4! 6! 8! 10! 12! 14! 16! 18! 20! 22! 24! 26! 28! 30! 32! 34! 36! 38! 40! 42! 44! Fuel Use Rate (L/h)! Frequency! 4500! 4000! 3500! 3000! 2500! 2000! 1500! 1000! 500! 0! NH 3 Application! 0! 3! 6! 9! 12! 15! 18! 21! 24! 27! 30! 33! 36! 39! 42! 45! 48! 51! 54! 57! 60! 63! 66! 69! 72! 75! 78!! Fuel Use Rate (L/h)!
28 How big is too big?!
29 Spray Application Accuracy! (Fulton, et al.)! 29
30 Trends in ballasted GVW?!
31 Now 685 Hp (80,000 lb.)?!
32 Combine Classes?! Class%X% %565%hp% Class%XI% %629%hp% Class%XII% %693%hp% Class%XIII% %757%hp%
33 Machine!!! Compaction Penalty! Trafficked Area! (%)!! Yield Reduction Prediction! (200 bu/ac No-Till Corn Base)! Normal! Wet! Trafficked Yield (bu/ ac)! Field Avg. Yield (bu/ ac)! Trafficked Yield (bu/ ac)! Field Avg. Yield! (bu/ac)! Grain Cart! (Balzer 2000)! 14.0! 175! 196! 148! 193! Planter, 36 Row! (Case IH 1260)! 6.7! 190! 199! 171! 198! Class IX Combine, 16 Row! (Deere S690)! 17.1! 176! 196! 150! 192! Self-Propelled Sprayer! (RoGator RG1300)! 4.5! 198! 200! 182! 199! Manure Application! (Houle 48-8D)! 44.7! 189! 195! 168! 186!
34 Data driven decisions the future of digital agriculture!
![Machine Data Generation! As-Applied Files (.shp)! Spraying [0.3 MB/ac]! NH 3 application [4.3 MB/ac]! Planting [5.](/docs-images/74/71308792/images/35-0.jpg "5 MB/ac]! Yield Data [4.3 MB/ac]! Prescription Files [0.01 MB/ac]! Soil/Fertility Data [0.6 MB/ac]! Total [0.")
35 Machine Data Generation! As-Applied Files (.shp)! Spraying [0.3 MB/ac]! NH 3 application [4.3 MB/ac]! Planting [5.5 MB/ac]! Yield Data [4.3 MB/ac]! Prescription Files [0.01 MB/ac]! Soil/Fertility Data [0.6 MB/ac]! Total [0.5 KB/plant]!
36 Remote Sensing Data Generation! 24 bits per pixel! 2.5 cm/pixel! 17.2 MB/ac of image data!
37 FAA Grants UAS Exemptions"! Companies receive commercial exemptions for aerial surveying, construction site monitoring and oil rig flare stack inspections (Dec. 10, 2014).! Trimble Navigation Limited! VDOS Global, LLC! Clayco, Inc.! Woolpert, Inc.!
38 Woolpert, Dayton, OH! High Resolution Imagery!
39 Stand Counts via Remote Sensing!
40 Herbicide Carryover!
41 N Application Errors!
42 Compaction Study!
43 Compaction Study! 43
44 Compaction Study! 44
45 Hybrid Differences!
46 Lightweight Hyperspectral Cameras! Headwall Photonics Micro-Hyperspec (1.8 lb.)! nm (324 bands)! Rikola Ltd 300 g Hyperspectral Camera! nm (10 nm resolution)!
47 Hyperspectral Reflectance - Corn! 0.5 Mean of all Plants for each Corn Hybrid 0.5 Mean of all Plants for each Corn Hybrid 0.45 P1 Mean P2 Mean P3 Mean 0.45 P1 Mean P2 Mean P3 Mean P4 Mean P4 Mean 0.4 P5 Mean P6 Mean 0.4 P5 Mean P6 Mean P7 Mean P7 Mean 0.35 P8 Mean P9 Mean 0.35 P8 Mean P9 Mean Reflectance ! P10 Mean P11 Mean P12 Mean P13 Mean P14 Mean P15 Mean P16 Mean Reflectance P10 Mean P11 Mean P12 Mean P13 Mean P14 Mean P15 Mean P16 Mean Wavelength (nm) 1 st Round of Hyperspectral Measurements! Wavelength (nm) 2 nd Round of Hyperspectral Measurements! 0.5 Mean of all Plants for each Corn Hybrid 0.5 Mean of all Plants for each Corn Hybrid 0.45 P1 Mean P2 Mean P3 Mean 0.45 P1 Mean P2 Mean P3 Mean P4 Mean P4 Mean 0.4 P5 Mean P6 Mean 0.4 P5 Mean P6 Mean P7 Mean P7 Mean 0.35 P8 Mean P9 Mean 0.35 P8 Mean P9 Mean P10 Mean P10 Mean 0.3 P11 Mean P12 Mean 0.3 P11 Mean P12 Mean Reflectance 0.25 P13 Mean P14 Mean P15 Mean P16 Mean Reflectance 0.25 P13 Mean P14 Mean P15 Mean P16 Mean Wavelength (nm) 3 rd Round of Hyperspectral Measurements! Wavelength (nm) 4 th Round of Hyperspectral Measurements!
48 Hyperspectral Reflectance - Corn! Mean of all Tassels for each Corn Hybrid Mean of all Silk for each Corn Hybrid ! P1 Mean P2 Mean P3 Mean P4 Mean P5 Mean P6 Mean P7 Mean P8 Mean P9 Mean P10 Mean P11 Mean P12 Mean P13 Mean P14 Mean P15 Mean P16 Mean P1 Mean P2 Mean P3 Mean P4 Mean P5 Mean P6 Mean P7 Mean P8 Mean P9 Mean P10 Mean P11 Mean P12 Mean P13 Mean P14 Mean P15 Mean P16 Mean Reflectance Reflectance Wavelength (nm) Wavelength (nm) Hyperspectral Measurements of Tassels! Hyperspectral Measurements of Silk!
49 In-Field Phenotyping!
50 Continuing trend toward automation.!
51 Current Products! Deere MachineSync! AGCO GuideConnect!
52 Autonomous Tractor vs. Baxter?! products/baxter/!
53 Future of Agriculture?!
54 Tractor of the future! Technical obsolescence and mechanical life will coincide machine life of 6-7 cropping seasons.! Fully (or supervised) autonomous tractors will be hp and under 8,000 lb. GVW.! Tractors will be reconfigurable for ground clearance and track width.! Tractors will utilize spark ignition engine multi-fuel (gasoline, ethanol and CNG) and reduced emissions.! Automation (sensor-rich environment) will accelerate data generation.!
55 Agricultural Data Warehouses
56 Existing Data Warehouses! Monsanto/Climate Corp. - FieldScripts! John Deere MyJohnDeere! DuPont s - Pioneer Field360! Cargill s - NextField DataRx! Beck s - FARMserver! FarmersEdge PrecisionEdge!
57 Producer Concerns over Data! Privacy and security! Limited access to their data contained in proprietary message (CAN) formats! Loss of control once data is uploaded to the cloud uncertain of how data will be used! Aggregators of producer data will use it to control markets! Unlikely to share in value of data! Government access!
58 Basic Scenarios! Manual Transfer Wireless Cloud-Based Platform
59 Emerging Scenario! Ag Tech! Producer-Centric Ag Data Warehouse (Third Party) API! Standardization Cleaning Certification Anonymization Aggregation API!
60 ADW Value to Producers! Producers will be in control of their data! Ag Data Warehouse (ADW) will become the trusted, third party manager of data! Scalable serves small and large farmers alike! APIs will provide access to proprietary CAN messages! Summary data will be made available for benchmarking! More opportunities to realize value! Accelerated data aggregation! FOIA not applicable!
61 ADW Value to Agri-Business! Acceleration of data aggregation! Certification process will improve overall data quality! Ability to share development costs reduced cost of providing data services to customers! Improved services for clients! Reduced liability for data security! Reduced compliance costs!
62 Questions?!
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