Digitial Precision Agriculture and Location Intelligence Levente Klein IBM TJ Watson Research Center

PAIRS Analytics: Precision irrigation  Project: First of a Kind  Pain Points o Yield o Variability o Quality o Efficiency  Solution o Satellite images are analyzed to derive vegetation index o Predictive weather models o Crop models o Evapo-transpiration model o Controllable smart irrigation system (Digital Drip-line Technology)

Spatial Variability is a major limit to crop yields, water efficiency and uniformity Perennial crop (vineyard) : Large intra-field variability (measured during harvest) “limits” total crop yield Changes by 2x within less of 20 meters If the low performing parts of field can be improved to the “current” average yield will increase by >50 % are possible Harvester with yield measurement and GPS 10 acre yield map 3

 Novel drip line/solenoid line infrastructure  Multiple irrigation per day (instead of one time block) to maintain soil moisture  Fertilizer can be delivered differentially through the same double drip line Double drip line with control electronics. Control electronics box and node Solar panels and central control area. Digital Drip line Technology (2DT) 4

Increased Yield due to variable rate irrigation Yield (t/ha) Control area Yield enhancing area Year to year change Control area Yield enhancing area Year to year change Mean % % Coefficient of Variation % % Cabernet Sauvignon across 63 vine rows Grid 15x15 meters (first and last zones larger) matched satellite cell size 140 independent irrigation zones

Water use efficiency Total irrigation hours Water use efficiency In the VRI area 20% less water used than in the control area. Water use efficiency (lg of grapes per gallon of water) is 12% improved in the VRI area.

Benefits of Precision Irrigation Increased crop yield: Irrigation water savings Water savings: Resolution min 4 t/acre, max 14 t/acre, and avg 7.5 t/acre Crop yield increase and return of investment (ROI) for a double drip line system can be as low as 2 years for an area instrumented at 30 m spatial resolution.. Viable technology to deliver the optimum amount of water while preserving or increasing yield.

Platform requirements for Irrigation prediction Massive but “organized” data store -Paralell processing/Hadoop/Hbase -Common “format” - Spatially & temporally joined and linked - Global - Peta Bytes of Data Queries: E.g. Find all areas with soil type A and surface temperatures between 35 and 65 between May and April in 2014 Multiple temporal and spatial - resolutions - formats (xml, raster, vector) - projections - reference systems IBM Research Analytics - Image Recognition / Processing - Irrigation Forecsating/Evapo-transpiration -Photo synthetically Solar Radiation Forecasting 8 What is required for regional/continental scale irrigation forecasting?

PAIRS Analytics: Highly accurate weather and climate forecasting Operational weather forecasts for > 1600 sites across the US using a multi-model machine-learnt blending approach. Over 30 % improved MAE (mean absolute error) accuracy over NOAA, AccuWeather etc. System continuously learns and improves based on target metrics Forecast data accessible via a data service 9

Conclusion 10 Data driven precision agriculture can enable crop yield increase making it possible to produce same amount of crop on 25% less land surface. Physics based irrigation models coupled with an automated irrigation system can precisely deliver the optimum amount of water. For large scale decision support system Physical Big Data approaches built on Hadoop/HBase evapo-transpiration modeling and automation is developed.