From Precision Agriculture to Smart Farming. Corné Kempenaar Capigi conference Connecting the future, April 2, 2014

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Merry French
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1 From Precision Agriculture to Smart Farming Corné Kempenaar Capigi conference Connecting the future, April 2, 2014

2 Content Introduction to the topic of today Some precision agriculture results Crop, soil and yield monitoring Variable rate application at grid level Individual plant treatment (the next level) Conclusions and challenges Discussion

3 Our challenge: More with less Food Demand Where we want to be Where we are now Ecological Footprint

4 What is precision agriculture? Precision agriculture (PA) is a farming management concept based on observing, measuring and responding to variability in crops / livestock Spatial and temporal variation Detection, decision making and implementation Sensors, big data, DSS, GNSS, implements, ICT, smart phone, FMS, Apps, Robotics,...

5 Precision agriculture according to Univ. Wisconsin

6 Precision Agriculture 1.0 Controlled traffic farming

7 Precision agriculture 2.0, the next step

8 Scales in precision agriculture Grid In practice Plant On station research Leaf On institute research Disease On institute and university research

9 National satellite data portal Beschikbare satellietbeelden binnen NSD vanaf Maart 2012 Sateliet Data Bands/polarisatie Spatiale Resolutie Temporele Resolutie Formosat Panchromatisch Multispectraal Black/white Blue, Green, Red, NIR 2 meter 8 meter Every 10 days DMC Multispectraal Green, Red, NIR 22 meter Ca. 3x per week Radarsat Radar HH+HV polarisatie VV+VH polarisatie 25 meter Every 24 days Without: Geometric correction Mosaicing Cloud detection and filtering Atmosferic correction End products/indices

10 DMC versus Formosat Noordoostpolder DMC (22 m) NDVI 4-Sep-2012 Formosat (2/8 m) NDVI 1-Sep-2012

11 Geo-databank of an arable crop rotation

13 Satellite images of winter wheat field

14 50x winter wheat NDVI curves in Flevoland NDVI groeicurve is een unieke kwantificering van de gewasontwikkeling per perceel Ism teeltadviseurs verdere analyse van groeicurves Wat is de optimale groeicurve? Wat is de relatie met opbrengst?

15 Potato production and storage

16 Biomass sensors Cropscan Yara N Sensor (ALS and passive) v sensed area sensed area Terra (Aster sensor), Worldview-2 and TerraSAR-X satellites

17 Minimum effective dose (l/ha) Decision support model Reglone Reflection parameter CropScan

18 Minimum effective dose (l/ha) Grid treatment: sensor op cabine of tractor, DSS, VRA on the go, as applied map Reglone 90 sensed area 90 sensed area v Reflection parameter CropScan Acknowledgement: Yara, Homburg Holland, PPO

Use VRA in practice: Leipzig, 2009 Yara N-Sensor plus

Reglone, normal, 1x Ware potatoes BBCH GS 91, 7

days after treatment Fixed rate: 2,5 L Reglone/ha @

19 Use VRA in practice: Leipzig, 2009 Yara N-Sensor plus conventional sprayer MLHD PHK dosing algorithm Reglone, normal, 1x Ware potatoes BBCH GS 91, 7 august, sunny, hot (30 C) Pictures were taken four days after treatment Fixed rate: 2,5 L 300 L water per ha Variabele rate: av. 1,5 L 200 L water per ha

20 Grid treatment: based on satellite image, DSS and task map (2011) Acknowledgement: TerraSphere, NSO en Mts Zondag

21 or sensing from UAS, DSS en task map (2012) Acknowledge ment: TerraSphere, Vd Borne

22 Formosat image used for potato haulm killing (2013)

23 Task map potato haulm killing (2013) with Akkerweb

24 As-applied map Yara N-Sensor

25 Other precision agriculture applications in development Variable seeding/planting density VRA fertilizer application Soil ph management VRA irrigation Selective harvesting Nematode management Disease management Weed management...

26 wikipedia.org fieldcopter.eu

27 Veg. indices versus N-content biomass

28 Model for Nitrogen application in potato

29 Screendump of the N-advise model

30 Fuel use monitoring -> input soil management (2013) 30

31 Printscreens making of task map clay content -> VRA soil herbicide weed control)

32 Weed detection with near sensing camera s a b 1.46 m c d 0.01 m

34 Conclusions and recommendations Smart farming requires: Accurate data soil, climate, crop and livestock conditions Data infrastructure to analyse and apply the (big) data DSS that translate spatial and temporal variation in actions Implements for site or animal specific actions Integration of technology at farm and chain level Bottom up development Cooperation between suppliers and end-users Yield potential map concept will boost PA Autonomous platforms in crops will change level of precision in PA

35 Towards smart farming tech. (P.A. 2.0)

Farm Maps Farm Maps your field in view

Farm Maps Farm Maps your field in view your field in view Each field carries an enormous amount of information that growers could use to choose a tailor-made approach. This will result in optimum performance of a field, both in the short term