innovation topics · 8/5/2018  · goals • ability to integrate a large variety of use cases •...

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TEMP-0010-DOT-F - Verhaert PresentationCONFIDENTIAL

INNOVATION TOPICS

Alexander FrimoutVerhaert Innovation ConsultantAlexander.Frimout@verhaert.com

08.05.2018

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PROJECT TIMELINE

NEED DEFINITION

MARKET CONSULTATION

FINAL REPORT

MARKET SCOUTING

PUBLIC TENDER

Today + input survey

June 2018 Est. Sept. 2018

Survey: https://bit.ly/2rnG3qC

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HIGH POTENTIALS IDENTIFIED WITHIN THE DEPARTMENT

4 CATEGORIES:

I. FIELD OPERATIONS

II. CLASSIFICATION

III. YIELD/LOSS ESTIMATIONS

IV. GOV. APP DEVELOPMENT

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WHAT IS NOT IN THESE HIGH POTENTIALS

• Opportunities that are not sufficiently innovative• No need for an innovation project• E.g. Using remote sensing for checking the crop diversity requirements • Can still be interesting for the department please contact directly

• Opportunities that offer insufficient value for the department• Not the first focus for the department• E.g. Controls on changes in ecological attention areas• Can still be interesting in the future

• Opportunities where the potential for remote sensing data is too low• Not part of the scope of this project (some exceptions)• E.g. Checking for Thistles (zero-tolerance policy in Flanders)• Non-satellite solutions can still be desirable

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PIO ENVISAGES MID-TERM INNOVATIONS, BUT…

Certain innovations can be classified in other financing channels

(Non-innovative) Commercial

tenders

SME feasibilitystudy &

innovationprojects

Sprint / R&DProject

Fundamental research, H2020 projects

VA

LUE

FOR

DEP

T.

LV

LEVEL OF INNOVATION

Ready, off-the-shelfsolutions

Innovative partnerships

Very challenging, breakthroughresearch requiredProgram for Innovation

procurementMain focus CAPSAT

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WILL ASK YOUR INPUT ON THE HIGH POTENTIALS

• Is it innovative? Why (not)?

• What are potential showstoppers?

• Where lies the complexity?

• What would be required to make it work?

• Is there any commercial value?

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COMMON GOALS ACROSS HIGH POTENTIALS

• Minimal dependence on weather conditions• We seek a step change in accuracy, but are realistic that

99% is probably not feasible• Algorithms must be compliant with current traffic light

assessment model• This will allow the department to:

do equalized high level checks for all of Flanders;warn farmers when they may be in violation;dispatch field controllers to plots which are not compliant;dictate future policy based on these insights.

+ HELPING THE FARMER do better farm management & achieve better results is an important goal for the department!

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HIGH POTENTIALS

1. Field Operations2. Classification3. Yield/Loss Prediction4. Governmental App Development

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I. FIELD OPERATIONS

1. Registration of the moment of planting/seedinga. Planting/Seeding detectionb. Registration using alternatives to remote sensing datac. Registration using a model based approachd. Controller optimization program

2. Registration of the moment of ploughinga. Ploughing detectionb. Direction of ploughing

3. Registration of the moment of harvestinga. Harvesting detectionb. Mowing of grasslands

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1. REGISTRATION OF THE MOMENT OF PLANTING/SEEDING

WHYSeveral seeding regulations need to be measured21/4 (seeding deadline cracked grasslands)

1/3-31/8 (no seeding allowed in wasteland)

GOALS• At least for top 10 crops in Belgium

• Fast evaluation upon image acquisition (<1 day)

1.A PLANTING/ SEEDING DETECTION

WHYPotential crop-independent alternative to detection (register field operations)

GOALS• Access to reliable public data

sources

• No privacy conflicts

1.B REGISTRATION USING ALTERNATIVES TO REMOTE SENSING DATA

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1. REGISTRATION OF THE MOMENT OF PLANTING/SEEDING

WHYBackwards estimation based on greenup time

Less desirable than detection: large delay means it is too late for controller visits and/or corrective measures

GOALS• At least for top 10 crops in Belgium

• Very high certainty to compensate for time delay (>99% certain of violations)

1.C REGISTRATION USING A MODEL BASED APPROACH

WHYIn-field inspections guided based on remote sensing markers

GOALS• Optimize inspections based on traffic

light evaluations

• Consider future image acquisition (& expected weather conditions)

• Take routing logistics into account

1.D CONTROLLER OPTIMIZATION

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2. REGISTRATION OF THE MOMENT OF PLOUGHING

WHYNeed to check that certain plots are ploughed before allowed dates

GOALS• High reliability (>95%) of ploughed/not

ploughed evaluation as evaluation dates draw near

• Fast evaluation upon image acquisition (<1 day)

2.A PLOUGHING DETECTION

WHYImportant on sloped surfaces to limit runoff

GOALS• High reliability (>95%) of detected

direction

• Link plot slope to ploughing direction

• May become more important in long term

2.B DIRECTION OF PLOUGHING

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3. REGISTRATION OF THE MOMENT OF HARVESTING

WHYIndication for next field operations & time crop residues stay on field

GOALS• Detection independent of crop type

• High reliability (>95%) of harvest

3.A HARVESTING DETECTION

WHYTo indicate if the grasslands are maintained & help with loss estimation in case of disasters

GOALS• High reliability (>95%) indication of

most recent time of mowing

3.B MOWING OF GRASSLANDS

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HIGH POTENTIALS

1. Field Operations2. Classification3. Yield/Loss Prediction4. Governmental App Development

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II. CLASSIFICATION

4. Greencoversa. Checking the presence of greencoversb. Identifying greencover typesc. Assessing greencover impact

5. Fabaceaea. Checking the presence of fabaceaeb. Identifying fabaceae typesc. Assessing fabaceae impact

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Perennial ryegrass (Lolium perenne)

Italian/Westerwolds ryegrass (Lolium multiflorum)

Fodder radish (Raphanus sativus subsp. oleiferus)

White mustard (Sinapis alba)

Turnip rape (Brassica rapa)

Lopsided oat (Avena strigosa)

4. GREENCOVERS

WHYObligation to maintain greencovers for a set period of time (8 weeks)

GOALS• High reliability (>95%) to distinguish

from wintercrops, late summer crops, vegetables & grasslands

• Give indication of time on field (>90% certainty of presence ≥ 8 weeks)

4.A CHECKING THE PRESENCE OF GREENCOVERS

WHYThere are requirements for different greencover mixes

GOALS• Distinction between most used

greencover plants & mixes

• Lower accuracy allowed, possibility to validate using financial data (currently not in possession of the department)

4.B IDENTIFYING GREENCOVER TYPES

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4. GREENCOVERS

WHYMeasuring the effect can lead to better future policies

GOALS• Measurable indication of level of

nitrogen fixation

• Impact on soil erosion

• Impact on biodiversity (?)

4.C ASSESSING GREENCOVER IMPACT

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Clover-grass mixtures

Fodder peas

Broad beans (Vicia faba)

Alfalfa (Medicago sativa)

Common vetch (Vicia sativa)

Lupin (Lupinus spp.)

5. FABACEAE

WHYSeparate subsidies are given for Fabaceae

GOALS• Ability to distinguish between a

grassland which has Fabaceae present and regular grasslands with as high an accuracy as possible (>80%)

5.A CHECKING THE PRESENCE OF FABACEAE

WHYSize of subsidy is dependent on type of Fabaceae

GOALS• Distinguish most used Fabaceae plants

& mixes: mostly grass-clover mixes

• Lower accuracy allowed, possibility to validate using financial data (currently not in possession of the department)

5.B IDENTIFYING FABACEAE TYPES

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5. FABACEAE

WHYMeasuring the effect can lead to better future policies

GOALS• Indication of level of nitrogen

fixation

5.C ASSESSING FABACEAE IMPACT

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HIGH POTENTIALS

1. Field Operations2. Classification3. Yield/Loss Prediction4. Governmental App Development

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III. YIELD/LOSS ESTIMATION

6. Yield estimationa. Yield estimation for top 10 cropsb. Plant-independent performance indication

7. Loss estimationa. Affected area & severity estimationb. Quality loss estimationc. Economic loss estimation

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6. YIELD ESTIMATION

WHYProvide valuable advise for farmer

GOALS• Decent accuracy (<10% error rate) yield

estimation for the Flanders region for top 10 crops

• Ability to detect disease outbreaks

• Feedback interface for farmers with high ease of use

6.A YIELD ESTIMATION FOR TOP 10 CROPS

WHYGive high-level feedback to farmers on all crop types

GOALS• High accuracy indicators not mandatory

• Allow comparisons. F.e. plot performance compared to previous years, comparing yields of similar plots…

6.B CROP-INDEPENDENT PERFORMANCE INDICATION

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7. LOSS ESTIMATION

WHYObjective determination in case of disasters is challenging

GOALS• Ability to determine affected area in case

of disasters: hail, storm, frost, drought, flood

• Accurate biomass loss estimations for top 10 crops (<10% error margin)

7.A AFFECTED AREA & SEVERITY ESTIMATION

WHYAbility to objectively determine if harvest is still good for sale and to what degree quality has been affected by the disaster

GOALS• Any objective indication of quality loss

• Alternatives to remote sensing data?

7.B QUALITY LOSS ESTIMATION

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7. LOSS ESTIMATION

WHYObjective estimation of financial loss experienced by farmer

GOALS• Integration market prices, quantity

& quality loss, …

7.C ECONOMIC LOSS ESTIMATION

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HIGH POTENTIALS

1. Field Operations2. Classification3. Yield/Loss Prediction4. Governmental App Development

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IV. GOVERNMENTAL APP DEVELOPMENT

8. App developmenta. Multi-purpose image registration appb. Augmented reality farmer assistancec. Image recognition

!No direct connection to remote sensing dataApp can be used for wide variety of cases which cannot be

done with remote sensing data in the near future. The department is looking for a solution in the short term

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8. GOVERNMENTAL APP DEVELOPMENT

WHYAvoid the need for controller to travel to the site

GOALS• Ability to integrate a large variety of use

cases• Automatic rejection if quality is insufficient

(resolution, lighting)• Tamper-proof registration of picture

metadata (location, orientation, timestamp)

8.A MULTI-PURPOSE IMAGE REGISTRATION APP

WHYGuide farmer to correct location/ orientation to record better images

GOALS• Automatic interaction with backend

system of the department to identify landscape elements

8.B AUGMENTED REALITY FARMER ASSISTANCE

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8. GOVERNMENTAL APP DEVELOPMENT

WHYAutomate a number of checks and remove need for human intervention

GOALS• Top candidates for image

recognition still to be identified

8.C IMAGE RECOGNITION

HIGH

VALUE FOR DEPT. LV

INNOVATION LEVEL

HIGH

Questions?

Talk to one of us today!

Ruben Fontaine

Head of Declaration

Unit

Sebastiaan

Philips

Expert on GSAA,

Greening,

Agricultural

disastersPieter Roggemans

ICT Technical expert

Remote Sensing

Tim Baeten

Expert on GIS, LPIS,

subsidiarity

Tine Van Eylen

Expert on Direct

Payments

Sanne Habets

Expert on Greening –

crop diversification

and EFA

Timo Ghysels

Project management

CAPSAT

Pillar II – Rural

Development