innovation topics · 8/5/2018 · goals • ability to integrate a large variety of use cases •...
TRANSCRIPT
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TEMP-0010-DOT-F - Verhaert PresentationCONFIDENTIAL
INNOVATION TOPICS
Alexander FrimoutVerhaert Innovation [email protected]
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