Cra-w - use of mobile applications in agriculture

1Walloon Agricultural Research Centrewww.cra.wallonie.be

Use of mobile applications in agriculture :

exemple of precision farming concepts and applications

Walloon Agricultural Research Centrewww.cra.wallonie.be

Liège, 02/07/2015MyEorganics stakeholder workshop

Viviane Planchon, Jean-Pierre Goffart, Bruno Huyghebaert, Georges Sinnaeve

Combined GNSS and Copernicus data to secure

environmental agricultural systems


Walloon agricultural Research Centre

Regional public body

• To carry out fundamental and applied agricultural researches

• To provide services using expertise and equipment related to research results





420 including

150 scientists

300 ha experimental

fields, orchards,

greenhouses, laboratories…

3 sites Gembloux, Libramont, Mussy-la-

ville

150 research

projects at regional, national

and european

level

Cellule transversale de Recherches en agriculture biologiquewww.cra.wallonie.be

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Strategic Plan for the Development of Organic Agriculture (PSDAB) in Wallonia 2020

Carlo Di Antonio, June 2013

PSDAB 2.1. GOVERNANCE MEASURES•Action 2 : to ensure the implementation and monitoring of the operational organic farming scheme•Action 3 : to establish, within the CRA-W, an ‘ORGANIC’ research team and to develop a coherent research plan with the needs of the sector

Since the beginning of 2014, creation of a transversal research group on organic farming financed by the Walloon Region based on the :


Cellule transversale de Recherches en agriculture biologiquewww.cra.wallonie.be

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Cellule transversale de Recherche en agriculture biologique

CtRab


EO in agriculture

• Agriculture is one of the key areas for societal benefit of space

applications in Belgium and EU• The new generation of European EO sensors (S1, S2, PROBA-V) is

providing both high spatial and temporal resolution matching agriculture monitoring requirements

• Agriculture is one of the key areas for societal benefit of space

applications in Belgium and EU• The new generation of European EO sensors (S1, S2, PROBA-V) is

providing both high spatial and temporal resolution matching agriculture monitoring requirements

• Belgium, an ideal test bed for agriculture EO : parcel delineation, complexity, pilot centers

• CRA-W is a member of

• Belgium, an ideal test bed for agriculture EO : parcel delineation, complexity, pilot centers

• CRA-W is a member of


USA Belgium

Clear definition of the target, the goal

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Plan oDefinition and concepts of Precision FarmingoGeoreferenced data acquisitionoData treatment, calibration and validationoData interpretation

Development of Decision Support Systems :

CRA-W projects


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Precision farming (PF)

A way to improve crops management

• based on a better intra-field knowledge• considering spatial and temporal variability (soil,

crops, etc.)• to improve farm performances on several points

▫ technic: productivity and agronomical performances▫ economic: profitability of exploitations▫ environment: minimizing impacts of agro-activities

• for conventional and organic farming …


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Precision farming in three steps


Spatio – Temporal

Variability within a field in

Soils and Plants

3 – CONTROLHow?

2 – UNDERSTANDINGWhy ?

1 – KNOWLEDGEWhere ? When ?

How much?

Identification of FactorsLimiting Crop Yields.« Agronomist Role »

Need for GeoreferencedData Acquisition

▫ Variable Rate Application‘VRA’ (Spatial Continuity)

▫ Localised Management‘MZ’ (Management Zones)


How to identify the intra-field variability ?

• Detailed soil maps (in Wallonia, very accurate soil

maps : a soil sample each 75 m)• Yield and product quality maps (multi-years)• Grid soil samples• Near sensing of soil and crop• Remote sensing (ground, aerial or spatial) of soil and

crop

Data common basis and analysis tools : GPS and GIS technologies for data geo-referencing Geo-statistical methods for data interpolation Meta-data management methodsWalloon Agricultural Research Centre

www.cra.wallonie.be

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From Precision farming …


Cultural operations performed accurately using the :

•geolocalisation GPS RTK• constellation of geolocalisation satellites (GNSS, GLONAS, Galileo, etc.)



Georeferenced Data Acquisition

Source: Müeller-elektronik

Source: Yara

Source: Claas

META-DATA(Generic and geo-

localized)

Source: Agrometius



Use machineries as “Generic” Data provider:• Tractor (CANbus): effort sensor, slippage,

consumption…• Harvester: yield monitoring, quality monitoring

(protein, humidity…)• Other « embedded » sensors: Radiometers, NIR-

sensors, Veris, fluorimeters…

Walloon Agricultural Research Centrewww.cra.wallonie.be Source: Valtra



Precise guidance for farm machinery• GNSS technologies for tractor guidance• VRA technologies and machinery management


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… to the precision farming v2.0 or the “smart farming”

With databases growing rich by remote sensing :

•Spatial remote sensingconstellation of EO satellites : Copernicus, ESA – S2 23/06/2015

•Airborne remote sensingUAV’sair flights (APEX in Gembloux 01/07/2015)

•Near sensing or field sensors in the field- sensors embedded on farm machinery :

tractors, harvesters, fertilizer, sprayer(NSENSOR, Greenseeker, Multiplex, etc.)-hand-held ground-based(CROPSCAN, etc.) -near sensing (chlorophyllometer)



Use of remote sensing methodology:• Satellite imagery (MultiSpec., HyperSp.):Landsat, Spot, Proba-V, Sentinel, Deimos, Rapid-Eye,

RadarSat, …

• UAV’s imagery (collaboration with VITO):eBee, Octocopter + cameras (RGB, MS, Red-Edge,

hyperspectral)



Georeferenced Data AcquisitionUse of Satellite imagery to assess Crop nitrogen status and tofurther develop Decision Support Systems (DSS) for Nfertilisation management


SPOT 5August 5 2009

SPOT 5August 5 2009

ROISPOT 5July 25 2008

ROI

SPOT 5July 25 2008

New satellites constellation is well adapted for agriculture monitoring at field scale: SENTINEL 1, 2, 3 (ESA)

Launched 2013 to 2016

Good image quality with clear sky conditions - Clouds cover is a limitation for visible and NIR wavelengths, but not for Radar ones

SPOT 5August 5 2009

ROI



High potentialities of satellite imagery to discriminate between differences in crop nitrogen status within a field


SPOT 5August 5 2009

SPOT 5August 5 2009

ROISPOT 5July 25 2008

100% N

70% N

0 N

Significant differences in the reflectance signal (digital number)

between increasing N rate zones



Use of UAV’s and embedded cameras to assess the variability of the crop during the growing season, and also support satellite image calibration…

SPOT 5August 5 2009

ROISPOT 5July 25 2008Potato crop evolution bare soil / Starting or decaying crop full developing crop

Source: iPot Project



…. or to follow up crop growth and phenology and help discrimination for within field zonation

SPOT 5August 5 2009

ROISPOT 5July 25 2008 Source:

iPot Project



Use of hand-held ground-based technology to calibrate/validate remotely sensed data

• Chlorophyll meter (SPAD 502)• Radiometer (Cropscan)• Fluorimeter (Dualex, Multiplex)• Hemispherical pictures for LAI assessment

Together with invasive plant sampling for analytical assessment while required




Index SPAD

Index NBI

Index CHL

Index FLAV

Indices NBI-R or NBI-G

Indices SFR-R or SFR-G Indices G et NormG

Index FLV Indices R et NormRIndices NIR et NormNIR

Estimation of leaf [chlorophylle]

Chlorophyll meter SPAD/HNT

Transmittance

Radiometer Cropscan

Réflectance

Fluorimeter Dualex

Transmittance – Chlorophyll fluorescence

Fluorimeter Multiplex

Chlorophyll fluorescence

Estimation of leaf [chlorophylle]and [flavonoids]

Estimation of leaf [flavonoids]

Indices NDVI et GNDVI

Indices SAVI et GSAVI…

Index SPAD/FLV

Index SPAD/FLAV

Aerial or spatial remotely sensed data calibration

Other are ASD radiometer for chlorophyll or hemispherical pictures for LAI assessment


Data acquisition from sensors The constraints for relevant data• Clear definition of the target, the goal• The right sensor and configuration• Check quality of the raw signal• Sensor calibration• Spatial and Temporal scale• Only one chance!!!

and ALSO :• Storage of the raw and averaged data

▫ 1 tera byte /day for satellite S2▫ backups



Data acquisitionSpatial and temporal scale


Spatial resolution: 10 to 60 m (pixel size)

Temporal resolution: 4 to 6 days (return period)

Image size: 290*290 km (Sentinel, ESA)2 pictures to cover Belgium ! Free of charge! Spatial resolution : 60 pictures/sec, 2 x 3

m width per passage, f(speed, work width) Temporal resolution: few hoursImage size: 1 field

Spatial resolution : 2-9 cmTemporal resolution: few hImage size: few fields

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Compare, correlate and validate the generic data with other pertinent and known agronomical data

Data treatment, calibration and validation

Source: Müeller-elektronik

META-DATA(Generic and

geo-localized)

Collection and analysis

of the generic data

Agronomical interpretatio

n

Source: Geo-pro Correlation between Generic and reference data :

Slippage ≈ Soil humidity

Seeding densityConcept of the VISA project


Data interpretation

Use the information gathered for• Interpretation (WHY?) • Variability control (HOW?)

Relevant SKILLS are :• Agronomical background (agronomist role)• Experts multidisciplinary approach (soil, crop,

machinery, statistics, economy, …)

CRA-W



Development of Decision Support System

Exploitation of the integrated information to generate and develop agronomical strategy

Exemple of levels of approach at CRA-W :

• Soil tillage (e.g. variation of speed or depth)• Seeding (e.g. variation in sowing density)• Fertilizer application (e.g. variation in N rate)




Holistic approach, considering all the parameters and using a multidisciplinary experts board

Application maps could be transferred to the machinery (VRA or MZ application)

Applications could be used by the farmer, etc. through web-tools or mobile application


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o VISA o BELCAMo iPoto REQUACARTO

Development of Decision Support SystemCRA-W projects

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•Multidisciplinary project funded by CRA-w (Moerman found)•Medium-term trials (4 years) in the experimental fields of CRA-W and at a farmer•Collaboration with other scientific organisms : Arvalis (F), UCL, ULg, VITO•Collaboration with the private sector :Cofabel, SCAM, Yara, Agrometius, Agri3000

VISA project

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VISA – data collection

Soil characterization

Harvest characterization

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VISA – agronomical interpretation

Data holistic interpretation by experts board


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VISA – Modulation and transfert to agricultural machinery

Modulation of 3 levers:• tillage• seedling• fertilizer and soil amendment inputs

Modulation map

Need of an application to gathered data and generate modulation

maps

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BELgian Collaborative Agriculture Monitoring at parcel level for sustainable cropping systems

Partnership and collaborative system• BELCAM objective : speed up the information loop between users,

developers and information providers to improve the transfer of training through an interactive platform gathering all geolocated data

• Information chain: satellites, researchers developing methods, pilot centers and farmers

« Farmsourcing »

Development of a tool

Expected resultsBELCAM will develop processing methods and models thanks to a collaborative strategy with pilot centers to deliver 6 EO target products to professionals along 3 seasons:

1. Annual Nitrogen balance-sheet forecast at field level, based on crops rotation, farming practices (i.e. crop residues, green manure, tillage…) and field conditions (slope, soil texture)

2. Annual field zoning, to help adjusting farming practices according to field heterogeneity compiled from previous years

3. Potato/winter wheat Nitrogen status at the parcel level, deciding whether an additional N application is required or not, based on the Chlorophyll a,b retrieval and the plant N estimate

4. Overall crop status (LAI, fAPAR, biomass) for potato/winter/maize along the season and aggregated at the district level

5. Major anomalies, pest, N, water stress or disease damages assessment, aggregated at the district level from SAR and optical time series analysis

6. Yield estimate from the newly developed model aggregated at the district level

“Industrial Potato monitoring for the Belgian

potato sector”



iPot project objective


To set up of a web-based information platform for the Belgian potato sector (processors, traders, researchers) to allow on line follow up of the potato parcels in order to control and improve potato quality and volume

The joint use of satellite and airborne remote sensing data, meteorological data and crop growth models will provide an efficient and unbiased monitoring of potato growth and development on the whole growing area

To provide information on potato growth and development at field level and yield estimates and forecasts to the Belgian potato sector by means of a

web-based geo-information platform



iPot project objective


Expected outcome (at parcel level):•Crop yield estimates •Early crop yield prediction (i.e. at harvest) including uncertainty range

Yield estimation and prediction



iPot project (Belgapom, VITO, CRA-w, ULg) – Belspo 2014-2017


Variability:-Within field-Between fields

Index time profile

Combined with weather info

Ranking of fields

iPot webtool


Development of Decision Support SystemREQUACARTO


REQUASUD webtool

(REQUASUD, CRA-W, ULg)

Sampling soil service



REQUACARTO


REQUASUD webtool

Systematic geolocation and unique identifier for each geo-

analysis :•to increase the accuracy

of the estimate•to make the monitoring of the fertility of agricultural land in

Wallonia (REQUASOL)




In other agronomic fields :

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• Quality of the milk of individual cows at the milking room

• On-line determination of the milk quality, in order to improve

the quality of the milk the feeding the genetic the health and fertility of the herd

Quality of the milk at the milking parlour

Livestock Precision Farming : Dairy sector

MilkiNIR project

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Precision Farming : Pig breeding

Gestal FM Project

Ingestion monitoring, optimisation of performances




In other agronomic fields :

-assessment of GHG emission (ENERGES project) and NRJ comsumption (GEOCAN project)-IPM (Integrated Pest Management)-varietal assessment and breeding trials-…




With a shift from a data-poor environment to a data-rich environment in agriculture,

ICT success is calling for developping new collaborative pathway for operational EO

ICT developments with mobile applications will be essential for agriculture; application usable for the farmers, etc.

Conclusion




The challenge is to combine all the possible applications into ONE usable application for the

user

Conclusion

55Gestal FM Project

Ingestion monitoring, optimisation of performances

Livestock Precision Farming : Pig breeding


Thank you for your attention


CONTACTS

Production and Sectors Department (D2)Crops production systems - U5Jean-Pierre Goffart [email protected] nutrition and Sustainability - U6Eric Froidmont [email protected] Animal breeeding, Quality production and Welfare - U7José Wavreille [email protected] machines and Facilites - U8Bruno Huyghebaert [email protected]

Agriculture and Natural Environment Department (D3)Soil fertility and Water protection - U9Christian Roisin [email protected] Farming systems, Territories and Information technologies - U11Viviane Planchon [email protected]

56Walloon Agricultural Research Centre

www.cra.wallonie.be


CONTACTS

Valorisation of Agricultural Products Department (D4)Agricultural product technology - U14Georges Sinnaeve [email protected] and Feed quality - U15Vincent Baeten [email protected]

CtRabJulie Vandamme [email protected]

57Walloon Agricultural Research Centre

www.cra.wallonie.be

Cra-w - use of mobile applications in agriculture

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