Embed Size (px)
Citation preview
Work Package 4: Multi-sensor model-based quality
control of mountain forest production
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Some thoughts after the first day of kick-off meeting:
1. Complements for all partners for fascinating presentations, unique know-how and enthusiasm.
2. The forest in mountains is peculiar, and very different than such of flat lands!!!
3. Trees in mountains are (mostly) BIG…4. Big/old tree may be or superior quality, or “fuel wood”5. Trees from mountains might be of really high value6. We do support with our heart “PROPER LOG FOR PROPER USE”7. The quality of wood/log/tree is an issue!!!!!8. But, the quality of wood is not only external dimentions, taper
and diameter…
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Wood might not be perfect…
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Wood from mountains might be priceless…
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
The goals of this WP are:• to develop an automated and real-time grading system for the forest production, in order to improve log/biomass segregation and to help develop a more efficient supply chain of mountain forest products• to design software solutions for continuous update the pre-harvest inventory procedures in the mountain areas • to provide data to refine stand growth and yield models for long-term silvicultural management
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Fine-grained timeline:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
MHG 1ITENE 1.1KESLA 1.2GRAPHITECH 1.3CNR 1.4MHG 1.52FLY 2.1COAST 2.2TRE 2.3GRAPHITECH 2.4ITENE 2.5GRE 3CNR 3.1GRE 3.2GRE 3.3KESLA 3.4ITENE 3.5MHG 3.6
4
TRE 4.1
CNR 4.2
BOKU 4.3
CNR 4.4
CNR 4.5
CNR 4.6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
MHG 1ITENE 1.1KESLA 1.2GRAPHITECH 1.3CNR 1.4MHG 1.52FLY 2.1COAST 2.2TRE 2.3GRAPHITECH 2.4ITENE 2.5GRE 3CNR 3.1GRE 3.2GRE 3.3KESLA 3.4ITENE 3.5MHG 3.6
4
TRE 4.1
CNR 4.2
BOKU 4.3
CNR 4.4
CNR 4.5
CNR 4.6
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Interim delivery stages (with dates):
D.4.01 R: Existing grading rules for log/biomass (December 2014)D.4.02 R: On-field survey data for tree characterization (March 2015)D.4.03 R: Establishing NIR measurement protocol (April 2015)D.4.04 R: Establishing hyperspectral imaging measurement protocol (May 2015)D.4.05 R: Establishing acoustic-based measurement protocol (June 2015)D.4.06 R: Establishing cutting power measurement protocol (July 2015)D.4.07 P: Estimation of log/biomass quality by external tree shape analysis (July 2015)D.4.08 P: Estimation of log/biomass quality by NIR (August 2015)D.4.09 P: Estimation of log quality by hyperspectral imaging (September 2015)D.4.10 P: Estimation of log quality by acoustic methods (October 2015)D.4.11 P: Estimation of log quality by cutting power analysis (November 2015)D.4.12 P: Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure (July 2016)
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Partners’ role and contributions:
Will be explained in presentations of tasks…
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Dependences between activities:
•T1.2 (and your comments) vital for proper initiation of work…
•WP4 is strictly related to WP3
•WP4 provides data to WP5
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Task 2.34.1.on-field forest survey
GPSPC/PAD3D scanner
3D vision
Tasks 3.14.2-4.3Mark treeConfirm route of cable crane
GPSPC/PADRFID TAGRFID reader
Tasks 3.24.4
Tree felling
Database
NIR QIH QI
RFID reader
RFID TAG(if cross cut)
Portable NIRHyperspectral
AccellerometersOscilloscope
SW QI
Tasks 3.3
Cable crane
Techno carriageGPRSRFID readerWIFISkyline launcherLoad sensorIntelligent chookersGPSPC/PADData loggerBlack box access
Control systemM/M interface
Tasks 3.44.2-4.3-4.4-4.5-4.6Processorde-brunch, cut to length, measures, mark
Load cell for cutting forceCutting feed sensorFeed force sensorDiameter digital caliperLengthRFID readerRFID TAGPC control comp.GPRS/WIFI
HyperspectralNIR scannerKinect ® (or similar 3D vision)Microphone/accellerometer
Data loggerBlack box accessCode Printer
Control systemM/M interfaceID backupDatabase
NIR QI + H QI + SW QI + CF QI
Tasks 3.5
Truck
RFID tags are only used for identifying trees/logs along the supply chain, not to store information.Material parameters from sensors are stored in the database
GPSGPRSRFID antennaBUSCANLoad cell
Logistic Software
ID backup
ID backup
Weight, time
Quality class
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Risks and mitigating actions:
To keep focus on practical applications and not pure (fascinating for us) research; 2-monts progress reporting, contributions/comments of SLOPE partners
Properly define real user expectations; contribution of the development of WP1, discussions with stake holders, foresters, users of forest resources
Technologies provided will not be appreciated by “conservative” forest users; demonstrate financial (and other) SLOPE advantages
Difficulties with integration of some sensors with forest machinery; careful planning, collaboration with SLOPE engineers
Thank you very much
