2. 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 value
6. 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…
3. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Wood might not be perfect…
4. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Wood from mountains might be priceless…
5. 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 preharvest inventory procedures in the mountain areas
• to provide data to refine stand growth and yield models for
long-term silvicultural management
7. 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)
8. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Partners’ role and contributions:
Will be explained in presentations of tasks…
9. 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
10. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Task 2.3
4.1.
Tasks 3.1
4.2-4.3
Tasks 3.2
4.4
Tasks 3.3
Tasks 3.4
4.2-4.3-4.4-4.5-4.6
on-field forest survey
Mark tree
Confirm route of cable crane
Tree felling
Cable crane
Truck
Processor
de-brunch, cut to length, measures, mark
GPS
PC/PAD
RFID TAG
RFID reader
RFID reader
Portable NIR
Hyperspectral
Accellerometers
Oscilloscope
NIR QI
H QI
SW QI
GPS
PC/PAD
3D scanner
3D vision
RFID TAG
(if cross cut)
ID backup
Techno carriage
GPRS
RFID reader
WIFI
Skyline launcher
Load sensor
Intelligent chookers
GPS
PC/PAD
Data logger
Black box access
Control system
M/M interface
ID backup
Weight, time
Database
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
Load cell for cutting force
Cutting feed sensor
Feed force sensor
Diameter digital caliper
Length
RFID reader
RFID TAG
PC control comp.
GPRS/WIFI
Hyperspectral
NIR scanner
Kinect ® (or similar 3D vision)
Microphone/accellerometer
Data logger
Black box access
Code Printer
Control system
M/M interface
ID backup
Database
NIR QI + H QI + SW QI + CF QI
Quality class
Tasks 3.5
GPS
GPRS
RFID antenna
BUSCAN
Load cell
Logistic Software
11. 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
