Slides from the 3rd Technical Meeting of the Slope Project

1. Project SLOPE
WP 4 – Multi-sensor model-based quality
control of mountain forest production

2. 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 (optimization)
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

3. Work Package 4: work to be done T4.1
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:

4. T4.1: Data mining and model integration of stand
quality indicators from on-field survey
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
 draft: October 2014
accepted: July 2015
 OCtober 2015
the resources planned: 9 M/M
the resources utilized:
PROBLEMS: Not reported
 






5. Work Package 4: work to be done T4.2
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:

6. T4.2: Evaluation of NIRS as a tool for determination
of log/biomass quality index
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
the resources planned: 13 M/M
the resources utilized:
PROBLEMS: Delay in access to sensor (sensor arrived Oct 2015, software Dec 2015)
SOLUTIONS: intensify efforts, working meetings with BOKU and COMPOLAB
 


draft: Dec 2015





 draft: October 2014
accepted: July 2015
 




7. Work Package 4: work to be done T4.3
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:

8. T4.3: Evaluation of hyperspectral imaging for the
determination of log/biomass quality index
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
the resources planned: 17 M/M
the resources utilized:
PROBLEMS: Delay in access to sensor (sensor arrived Oct 2015)
SOLUTIONS: intensify efforts, working meetings with BOKU and COMPOLAB




Jan 2016
 draft: May 2014
accepted: July 2015







9. Work Package 4: work to be done T4.4
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:

10. T4.4: Data mining and model integration of
log/biomass quality indicators from stress-wave
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
the resources planned: 5.5 M/M
the resources utilized:
PROBLEMS: delay with access to sensors (arrived January 2016), change of Task Leader
SOLUTIONS: intensify efforts, change in staff involved



draft: Jan 2016


 draft: December 2014
accepted: July 2015
 






11. Work Package 4: work to be done T4.5
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:

12. T4.5: Evaluation of cutting process (CP) for the
determination of log/biomass CP quality index
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
the resources planned: 6.0 M/M
the resources utilized:
PROBLEMS: delay with access to sensors (arrived January 2016)
SOLUTIONS: intensify work, close collaboration with COMPOLAB

 
draft: Jan 2016



 draft: January 2014
accepted: July 2015



13. Work Package 4: work to be done T4.6
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:

14. T4.6: Implementation of the log/biomass grading
system
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
the resources planned: 8.0 M/M
the resources utilized:
PROBLEMS: Delay related to other tasks – difficulties with implementation
SOLUTIONS: LAB scanner + prototype software developed in lab + algorithms ready


31.06.2016

 draft: October 2014
accepted: July 2015









15. fulfillment of the project work plan:
related deliverables (M25)
WP4 M17
task
delive
rable
title
type of
deliverable
lead
particip
ant
due date
foreseen or actual
delivery date
comment
T4.1
D4.2 on field survay data for tree characterization report TRE 31.10.2014 31.10.2014 accepted
D4.7
estimation of log/biomass quality by external tree shape
analysis
software tool TRE 31.05.2015 18.12.2015
Waiting for final
approval
T4.2
D4.3 establisghing NIR measurement protocol report CNR 31.10.2014 31.10.2014 accepted
D4.8 estimation of log/biomass quality by NIR software tool CNR 30.09.2015 March 2016
T4.3
D4.4 establisghing hyperspectral imaging measurement protocol report BOK 30.11.2014 05.05.2015 accepted
D4.9 estimation of log/biomass quality by hyperspectral imaging software tool BOK 31.10.2015 April 2016
T4.4
D4.5 establishing acoustic-based measurement protocol report CNR 31.12.2014 05.05.2015 accepted
D4.10 estimation of log/biomass quality by acoustic methods software tool CNR 31.11.2015 May 2016
T4.5
D4.6 establisghing cutting power measurement protocol report CNR 31.01.2015 31.01.2015 accepted
D4.11 estimation of log/biomass quality by cutting power analysis software tool CNR 30.12.2015 April 2016
T4.6
D4.1 existing grading rules for log/biomass report CNR 31.10.2014 31.10.2014 accepted
D4.12
implementatio and callibration of prediction models for
log/biomass quality classes
software tool CNR 31.06.2016 June 2016 NO

16. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Planning actions for all activities and deliverables to be executed
in M25-30:
Finalize + close: D04.8, D04.9, D04.10, D04.11
Deliver + finalize + close: D04.12
Initiate + deliver: -
Assemble sensors + control system
Install sensors in the processor head
Continue field tests with portable instruments
Calibrate system in the lab (“model tree”)
Collaborate with WP3 (and others) in hardware development

17. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Risks and mitigating actions:
Significant delay related to DoW amandment:
• the purchase and delivery of sensors delayed set-up of the system
in the lab (laboratory scanner) as well as on the processor head;
intensify efforts for all involved partners, direct collaboration and
working group meetings, involve additional staff for
developments, testing and implementation
Technologies provided will not be appreciated by “conservative”
forest users; demonstrate financial (and other) SLOPE advantages
Limited reliability of some sensors when implemented on the forest
machinery; careful planning, collaboration with SLOPE (+outside)
engineers

18. Sensors and electronics (WP3 & WP4) in progress
MicroNIR
Hamamatsu
C11708
Hamamatsu
C12666
Accelerometers
time of flight
Mechanical excitator
Accelerometers
free vibration LDS correction
Laser Displacement Sensor
AE sensor + amplifier
Tensionmeters 1/4 bridge
Dynamic load cell
Hydraulic pressure sensor
Hydraulic flow sensor
Absolute encoders
Hamamatsu
C11351
NI 9234
NI 9223
NI 9235/NI 9236
NI 9220
Port #8
CompactDaq
SENSORS
Port #7
Port #6
Port #5
Port #4
Port #3
Port #2
Port #1
LAN port #2
Industrial PC
LAN port #1
Port #6
Port #5
Video output + USB port #4
USB port #3
USB port #2
USB port #1
NI 9403 (Digital I/O)
Custom line scan camera
Port #8
CRio (real time?)
MACHINE CONTROL
Port #7
Port #6
Port #5
Port #4
Port #3
Port #2
Port #1
SEA 9744 (GSM + GPS)
Joystic(s)
RFID reader
Hydraulic actuators
???
???
???
???
LAN port #5
LAN port #4
LAN port #3
Touch screen
T4.2+T4.3T4.4T4.5T4.5T4.4WP3
WP3WP3
NI 9220
Temperatures of oil and air

19. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Thank you! – Grazie!

20. 4.2 Deliverables status
Deliverable D.4.03 Establishing NIR measurement protocol
evaluating the usability of NIR spectroscopy for characterization of bio-resources
along the harvesting chain, providing guidelines for proper collection and analysis
of NIR spectra.
Delivery Date M10, October 2014 - accepted
Estimated person Month = 5
Deliverable D.4.08 Estimation of log/biomass quality by NIR
Set of chemometric models for characterization of different “quality indicators” by
means of NIR and definition of “NIR quality index”
Delivery Date M21, September 2015 – draft presenting protocol validation
uploaded in dropbox, the deliverable are the models currently improved
Estimated Man/Month = 8

21. Detailed procedure related to measurement of NIR
along the whole harvesting scenario
Forest modeling
NIR quality index #1 will be related directly to the
health status, stress status and to the productivity
capabilities of the tree(s) foreseen for harvest –
images from FLYBY to be analyzed
Tree marking
Direct measurement of the NIR spectra by means
of portable instruments will be performed in
parallel to the tree marking operation (NIR quality
index #2) – first trials done in December
Cutting of tree
testing the possibility of collecting sample of wood
in a form of the triangular slice being a part of the
chock cut-out from the bottom of the log (NIR
quality index #3) – first trials done in December
(optional)
prepare samples #1
measurement of infrared
spectra (wet state)
prepare samples #2
condition samples
chemometric models for wet
wood and/or in field
chemometric models for
dry/conditioned wood (lab)
measurement of infrared
spectra
collect sample #1:
chip of axe
collect sample #2:
core ~30mm deep
collect sample #3:
chips after drilling core
collect sample #4:
triangular slices
measurement NIR profile
or hyperspectral image
measurement profile
of infrared spectra
consider approach: max
slope, pith position, WSEN
compute NIR
quality index#2
compute NIR
quality index#3
compute NIR
quality index#4
measurement profile
of infrared spectra
consider approach:
pith position, defects
compute NIR
quality index#5
tree marking
cutting tree
processor head
pile of logs
expert system & data base
refresh sample surface
measurement of infrared
spectra (dry state)
compute dry wood
NIR quality index#6compute the log quality
class (optimize cross-cut)
estimated tree quality
forest models
update the forest database
compare results of wet and
dry woods
combine all available char-
acteristics of the log
lab
Calibration transfer
f(MC, surface_quality)
3D tree
quality index
hyperspectral
HI quality index
stress wave
SW quality index
cutting force
CF quality index
compute NIR
quality index#1

22. Detailed procedure related to measurement of NIR
along the whole harvesting scenario
Processor head
NIR sensors will be integrated with the processor head (NIR quality index #4). The first
trials are foreseen for mid January on the lab scanner during measurement of the model
tree
Pile of logs
The cross section of logs stored in piles is easily accessible for direct measurement. Such
measurements will be repeated periodically in order to monitor the quality depreciation
and to determine the most optimal scanning frequency. The result of measuring NIR
spectra of logs stored in piles will be NIR quality index #5 – first trials done on 60 logs
Laboratory
Samples collected in the forest will be measured instantaneously after arrival in the
laboratory (at the wet state and with rough surface) by using the bench equipment
(NIR quality index #6). Campaign done by BOKU with FT instrument, recently parallel
measurement at IVALSA with MicroNIR

23. Current activity
Software developmnet and measurement campaign

24. First models – defects detection
knots
compression wood
fungi
compression wood
wood
resin pocket
knots
resin pocket
fungi
Defects detection with lab equipment
(data source: BOKU, PCA models: CNR)
Defects detection with MicroNIR
● - knots ▲- stain ▼- wood
(model tree) ■ bark ♦ resin.

25. Scheduled activity
activity responsible status schedule
Determination of measurement conditions of MicroNIR CNR On going
Measurement discs from BOKU CNR On going
Calibration transfer BOKU February/March 2016
Measurement trees in field CNR On going
Data base of spectra for QI CNR/BOKU On going
Report from in field measurement CNR done
Chemometric models in PLS toolbox CNR/BOKU February 2016
Installation of MicroNIR on processor head COMPOLAB March 2016
Implementation of the software in the system CNR May 2016
Report of NIR traceability CNR March 2016
Use existing models for prediction of calorific value CNR February/March 2016

26. Project SLOPE
WP4: Multi-sensor model-based quality control of
mountain forest production
T4.3– Evaluation of hyperspectral imaging (HI) for the
determination of log/biomass “HI quality index”
Cork, January 19th-21st, 2016
Andreas Zitek, Katharina Böhm, Jakub Sandak, Anna Sandak,
Barbara Hinterstoisser
BOKU & CNR
Technical Meeting, Cork
19.01.2016

27. Mid-term Review
2/Jul/15
Task 4.3 – Output
D4.04 Establishing hyperspectral measurement protocol
• Methodology, laboratory setup and field transfer
D4.09 Estimation of log quality by hyperspectral imaging
• Labscale investigations ((visible)/near infrared hyperspectral cameras)
• Validation by NIR measurements
• Application of chemometric approaches for data evaluation and
multivariate image analysis
• Identification of most relevant spectral information
• Development of transfer options to (harsh) field conditions
• Development of the “HI quality index” for quality grading
• Technological implementation on prototype

28. Fulfillment of the project work plan:
related deliverables (M25)
WP4
task
delive
rable
title
type of
deliverable
lead
particip
ant
due date
foreseen or actual
delivery date
comment
T4.1
D4.2 on field survay data for tree characterization report TRE 31.10.2014 31.10.2014 accepted
D4.7
estimation of log/biomass quality by external tree shape
analysis
software tool TRE 31.05.2015 18.12.2015
Waiting for final
approval
T4.2
D4.3 establisghing NIR measurement protocol report CNR 31.10.2014 31.10.2014 accepted
D4.8 estimation of log/biomass quality by NIR software tool CNR 30.09.2015 March 2016
T4.3
D4.4
Establishing hyperspectral imaging measurement
protocol
report BOK 30.11.2014 05.05.2015 accepted
D4.9
Estimation of log/biomass quality by hyperspectral
imaging
software tool BOK 31.10.2015 April 2016
T4.4
D4.5 establishing acoustic-based measurement protocol report CNR 31.12.2014 05.05.2015 accepted
D4.10 estimation of log/biomass quality by acoustic methods software tool CNR 31.11.2015 May 2016
T4.5
D4.6 establisghing cutting power measurement protocol report CNR 31.01.2015 31.01.2015 accepted
D4.11 estimation of log/biomass quality by cutting power analysis software tool CNR 30.12.2015 April 2016
T4.6
D4.1 existing grading rules for log/biomass report CNR 31.10.2014 31.10.2014 accepted
D4.12
implementatio and callibration of prediction models for
log/biomass quality classes
software tool CNR 31.06.2016 June 2016 NO
PROBLEMS: Delay in access to sensors, that produce the data to develop
the model and implement the system (=D 4.09)
SOLUTIONS: intensify efforts, working meetings with CNR, sharing and
transfer of samples measured at BOKU with NIR and HSI to CNR for
MicroNIR and Hamamatsu measurements, meeting in February at BOKU
to produce models, implement system and finalize D4.09 in April 2016

29. Task 4.3 – Field transfer options
Implementation of the hyperspectral imaging in the field:
• Hyperspectral imaging using new technologies
 Optimal accuracy and spatial resolution
 Rigidity of sensors (not suitable for harsh conditions)
 Relatively high cost
• Mono/multi spectral imaging the log cross-section
 Optimal spatial resolution
 Reasonable cost
 Poor spectral accuracy
 Challenges with implementation
• Several simple spectrometers installed on the scanning bar &
measuring the log cross-section
 Optimal spectral accuracy and sufficient spatial resolution
 Reasonable cost
 Difficulties with implementation
Mid-term Review
2/Jul/15
T3.4 Intelligent processor head

30. Task 4.3 HSI – general setups
• Whiskbroom imaging : During whiskbroom imaging the sample is scanned pixel
per pixel in the x–y–spatial direction in a sequential manner.
• Staring (staredown) imaging: Staring imaging is done by a two-dimensional
camera capturing the spectral information in each pixel x-, y-plane at once.
• Implementation of the Pushbroom imaging idea: as a line scanning system with
multiple sensors acquiring the information for a reduced set of pixels in the line
at once – subsequent interpolation planned and possible.
Project meeting
19-22/jan/2015
From: BOLDRINI, B.,
KESSLER, W., REBNER, K.
& KESSLER, R. W., 2012.
Hyperspectral imaging: a
review of best practice,
performance and pitfalls
for inline and online
applications. Journal of
Near Infrared
Spectroscopy, 20 (5):
438-508.

31. Task 4.3 HSI – general setups
Software #1: simulation of the NIR sensor results on the scanning bar.
It is possible to simulate the timing of scan by changing the integration time.
Cycle time of software (including integration of signal and acquisition of data by USB +
display on monitor.) = 0.25 s (4 Hz)
Project meeting
19-22/jan/2015

32. Task 4.3 HSI – general setups
Software #2: simulation of the hyperspectral sensor results on the scanning bar (only single
wavelength). Image on the left is an input showing all points measured with hyperspectral
system. Image on the right is reconstructed image by using simple interpolation (part of the
code is shown, used for reconstruction only).
Project meeting
19-22/jan/2015

33. settings of scanning density – rotation (degree)
settings of scanning density – pixels on the scan bar
size of the probe / measured area (pixels, ROI)
resolution of interpolated image

34. LabView code for reconstruction
raw data from scanner 2D interpolation

35. Δ radius = 10 Δ angle = 2 probe_size = 10
Δ reconstruction = 20

36. Δ radius = 50 Δ angle = 2 probe_size = 50
Δ reconstruction = 20

37. Δ radius = 50 Δ angle = 0.5 probe_size = 10
Δ reconstruction = 20

38. Δ radius = 50 Δ angle = 2 probe_size = 10
Δ reconstruction = 20

39. Δ radius = 20 Δ angle = 1 probe_size = 10
Δ reconstruction = 10 ( computation time…)

40. Task 4.3 Model development
Collection of
training
samples with
different
deficits
Measurements
with NIR and HSI
Laboratory
equipment
Detection of
most significant
wavelength
regions for
deficits
First models, lab
equipment
Measurements with
NIR and HSI with
sensors that will be
on Processor Head
MicroNIR
Hamamatsu
Model development and export
with PLS model exporter
Models can be directly used for
data from scanning bar and the
Labview software installed on PC
incl. preprocessing and statistical
methods
Models sensor arm equipment
Workflow
Lab (scientific basis,
calibration transfer)
Calibration & field
transfer

41. Task 4.3 Sensor wavelength range
comparison
Visible & near infrared range (VNIR)
400 nm
• Visible wavelength range ~ 390 - 700 nm
• Near IR wavelength range ~ 700 nm - 2500 μm
2500 nm
FT NIR (lab) 800 – 2400 nm
Hyperspectral (lab)
900 – 1700 nm
MicroNir (sensor)
900 – 1700 nm
Hamamatsu
C12666MA
340 – 780 nm
Hamamatsu
C11708MA
640 – 1050 nm
Range covered by sensors on processor head
340 – 1700 nm

42. Mid-term Review
2/Jul/15
Task 4.3 – 25 samples (spruce, Picea
abies) with defects
resin pockets
eccentric pith + compression wood + rot eccentric pith + rot + knot
shakes, checks, splitsknots
Measured with NIR
and hyperspectral imaging at
BOKU, and MicroNIR and
Hamamatsu at CNR

43. NIR-Spectroscopic measurements –
BOKU - laboratory
• 14 out of 25 samples wood discs were measured using a FT-NIR
with a fibre optic probe at BOKU
Meeting
19/Jan/2016

44. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
• Principal component analysis for wood and resin/ resin pockets
wood
resin pocket
Wavelength 800-1400 nm and 1st derivative + vector normalized
resin

45. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
• Principal component analysis for wood and fungi
woodfungi
Wavelength 800-1400 nm and 1st derivative + vector normalized

46. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
Principal component analysis for wood, bark and fungi
bark
wood
fungi
Wavelength 800-1400 nm and 1st derivative + vector normalized

47. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
Principal component analysis for wood, bark, compression wood
and fungi
bark
wood
fungi
compression wood
Wavelength 800-1400 nm and 1st derivative + vector normalized

48. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
Principal component analysis for wood, bark, compression wood
and fungi
bark
wood
fungi
compression wood
Wavelength 800-1400 nm and 1st derivative + vector normalized

49. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
Principal component analysis for wood, bark, compression wood,
knot and fungi
bark
wood
fungi
compression wood
knot
Wavelength 800-1400 nm and 1st derivative + vector normalized

50. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
Principal component analysis for wood, bark, compression wood,
knot and fungi
bark
wood
fungi
compression wood
knot
Wavelength 800-1400 nm and 1st derivative + vector normalized

51. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
Principal component analysis for wood, bark, compression wood,
knot, fungi and resin/ resin pockets
bark
wood
fungi
compression wood
knot
resin
resin pocket
Wavelength 800-1400 nm and 1st derivative + vector normalized

52. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
Principal component analysis for wood, bark, compression wood,
knot, fungi and resin pockets
bark
wood
fungi
compression wood
knot
resin pocket
Wavelength 800-1400 nm and 1st derivative + vector normalized

53. NIR-Spectroscopic measurements
Data evaluation
Meeting
19/Jan/2016
Scores Loadings
Principal component analysis for wood, bark, compression wood,
knot, fungi and resin pockets
bark
wood
fungi
compression wood
resin pocket
Wavelength 800-1400 nm and 1st derivative + vector normalized
knot

54. Task 4.3 – Hyperspectral imaging of 23 logs – example
resin pockets intensity slabs, final explorations ongoing
Brussels
3/jul/2015
1190 nm 1377 nm

55. Task 4.3 Status of the sensor & model
development & implementation (D 4.09)
NIR measurements of BOKU samples with MicroNIR
Prototype of sensor arm
HSI measurements of BOKU samples - Hamamatsu
Pototype of LabView software
Focus lenses mounted on Hamatsu sensors





Integration of sensors, soft- & hardware, models
 Model development & quality index

Implementation of full system on sensor arm with
hard- and software

UntilFebruary/March
D4.09inApril

56. NIR-Spectrocopic measurements
Scientific publication in prep.
Principal component analysis for wood and resin (resin pockets)
Scores Loadings
Meeting
19/Jan/2016
Böhm, Zitek et al., in prep, Assessing resin pockets on freshly cut wood logs of spruce
by NIR and hyperspectral imaging, European Journal of Wood and Wood Products

57. Mid-term Review
2/Jul/15
Thank you for your attention!

58. The objectives of this task is to optimize testing procedures and
prediction models for characterization of wood along the harvesting
chain, using acoustic measurements (i.e. stress-wave tests).
A part of the activity will be dedicated to the definition of optimal
procedures for the characterization of peculiar high-value assortments,
typically produced in mountainous sites, such as resonance wood.
Task Leader: CNR
Task Participants: Greifenberg, Compolab
WP4:T 4.4 Data mining and model integration of log/biomass
quality indicators from stress-wave (SW) measurements, for the
determination of the “SW quality index”
Objectives

59. WP4:T 4.4 Deliverables
D4.05) Establishing acoustic-based measurement protocol: This deliverable
contains a report and protocol for the acoustic-based measurement procedure
Starting Date: August 2014 - Delivery Date: December 2014
D4.10) Estimation of log quality by acoustic methods: Numerical procedure for
determination of “SW quality index” on the base of optimized acoustic velocity
conversion models.
Starting Date: January 2015 - Delivery Date: August 2015
Estimated person Month= 6.00

60. T4.4: Data mining and model integration of
log/biomass quality indicators from stress-wave
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
the resources planned: 5.5 M/M
the resources utilized:
PROBLEMS: delay with access to sensors (arrived January 2016), change of Task Leader
SOLUTIONS: intensify efforts, change in staff involved



draft: Jan 2016


 draft: December 2014
accepted: July 2015
 






61. D: 4.5 Establishing acoustic-based measurement
protocol: hardware
Hardware design has been made by COMPOLAB in
collaboration with CNR
Two approaches for measuring stress waves on the
processor are considered:
• ToF (time of flight)
• FV (free vibrations)

62. D: 4.5 Establishing acoustic-based measurement
protocol

63. D: 4.5 Establishing acoustic-based measurement
protocol
Time of Flight in SLOPE
l1 l2
t0
t1
t2
01
1
10
tt
l
v
−
=−
02
21
20
tt
ll
v
−
+
=−
12
2
21
tt
l
v
−
=−

64. D: 4.5 Establishing acoustic-based measurement
protocol:ToF challenges
•Preliminary tests highlighted great
problem with coupling of
accelerometers and wood, especially
due to bark
•Wet wood attenuates a lot stress
wave – hardly measurable, especially
with ultrasound…
•Several properties of log/wood are
not known during test (such as MC,
density)
•What does the value of velocity
means? (regarding quality)
Special design of hardware on
the processor head
The QI is (may be) computed
after processing of log
Experimental campaign is
foreseen & self learning system
on the base of historic data

65. D: 4.5 Establishing acoustic-based measurement
protocol
Free vibrations
if:
f1 = f2 - machine vibrations
f3 <> f1 - free vibrations of log,
fundamental frequency
D1
l
D2
time
time
frequency
f2 f3
FFT
f1
frequencyFFT

66. D: 4.5 Establishing acoustic-based measurement
protocol: FV challenges
•Laser displacement sensor’s spot is
absorbed by rough surface
•Are we measuring free vibrations of
log or processor head?
•What is the noise of signal?
•Several properties of log/wood are
not known during test (such as MC,
density, diameters, length)
•What does the value of frequency
means? (regarding quality)
Special sensor with enlarged
spot size (Keyence LK-G87)
The QI is (may be) computed
after processing of log and
related later by RFID
identification
Experimental campaign is
foreseen & self learning system
on the base of historic data
Compensation of LDS results
with additional acclerometer

67. Conclusions
Many factors influence SW propagation in wood.
Parameters measured with the other NDT methods will be incorporated in the SW
prediction models
Multiple linear regression analysis will be implemented for the definition of the
importance of the different parameters (regression t-values) for the model.
The further development of Task 4.4 is based on the implementation of the lab scanner
(i.e. purchase of sensors)
For the implementation of the methodology in the real case scenario, some practical issues
(e.g. coupling-decoupling of sensors, etc.) have to be considered in combination with
activity of Task 3.4
Sensors arrived: work will be done… and is ongoing

68. Task 4.5: cutting process quality index
Objectives
The goals of this task are:
• to develop a novel automatic system for measuring of the
cutting resistance of wood processed during harvesting
• to use this information for the determination of log/biomass
quality index

69. Task 4.5: Cutting Process (CP) for the determination of
log/biomass “CP quality index”
Task Leader: CNR
Task Partecipants: Compolab
Starting : October 2014
Ending: January 2016
Estimated person-month = 4.00 (CNR) + 2.00 (Compolab)
CNR : will coordinate the research necessary, develop the knowledge base linking process and wood
properties, recommend the proper sensor, develop software tools for computation of the CP quality
index
Compolab: will provide expertise in regard to sensor selection and integration with the processor head
+ extensive testing of the prototype

70. Task 4.5: cutting process quality index
Deliverables
D.4.06 Establishing cutting power measurement protocol
Report: This deliverable will contain a report and recommended protocol for collection of
data chainsaw and delimbing cutting process.
Delivery Date: January 2015 (M.13) DONE
D.4.11 Estimation of log quality by cutting power analysis
Prototype: Numerical procedure for determination of “CP quality index” on the base of
cutting processes monitoring
Delivery Date: January 2016 (M.25)

71. T4.5: Evaluation of cutting process (CP) for the
determination of log/biomass CP quality index
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
the resources planned: 6.0 M/M
the resources utilized:
PROBLEMS: delay with access to sensors (arrived January 2016)
SOLUTIONS: intensify work, close collaboration with COMPOLAB

 
31.01.2016



 draft: January 2014
accepted: July 2015



72.  working time of the cutting tools (knifes and chain):
 estimation of the tool wear and correction of the cutting forces
 position of the saw bar while cross-cutting:
 monitoring of the cutting progress
 correction factors related to the determination of the cutting forces and material
characteristics
 log diameter (combined with position of the saw bar):
 determination of the cutting length at each moment of the cross-cutting
 position of the main hydraulic actuator while cutting-out branches:
 monitoring of the de-limbing progress
 determination/mapping of the detailed knot position
Task 4.5: cutting process quality index
other sources of information

73. sensor type
simplicity
reliability
informationquality
easyinterpretation
lowcost
suitableforSLOPE
laboratorytests
suitableforSLOPE
in-fieldapplication
load cell     
strain gauge    
electric multimeter     
oil pressure    
oil flow    
AE    
microphone    
Task 4.5: cutting process quality index
comparison of sensors

74. Task 4.5: cutting process quality index
working plan
activity responsible status (end of task)
Assemble sensors and controllers in lab CNR Ongoing (Feb 2016)
Design solutions for sensors placement COMPOLAB Ongoing (Feb 2016)
Report from lab measurements CNR Ongoing (Mar 2016)
Installation of sensor on processor head COMPOLAB (Mar 2016)
Testing of sensors in the shop COMPOLAB (Mar 2016)
Implementation of the software for QI CNR (April 2016)
Final adjustments + callibrations CNR + COM (May 2016)
Processor ready for pilot: June 2016

75. hydraulic pressure sensors , hydraulic flow sensor , termometer ,
linear gauge 
Task 4.5: cutting process quality index
schematic of the log cross-cutting system of the ARBRO1000




76. Task 4.5: cutting process quality index
cross-cutting with the chain saw
Hydraulic flow (l/min)
Oil pressure (MPa)
Oil temperature (°C)
Position of the saw (mm)
+
Total working time of tool (min)
Log diameter (mm)
time of one sawing stroke/cycle
cutting resistance log diameter quality Index
“easy” “small” “low” (0,2)
“easy” “small” “very low” (0,0)
“difficult” “small” “very high” (1,0)
“difficult” “big” “high” (0,8)

77. 


hydraulic pressure sensor , load cell 
Task 4.5: cutting process quality index
schematic of the instrumented de-branching system of the ARBRO1000

78. Task 4.5: cutting process quality index
de-branching
Load cell#1 (N)
Load cell#2 (N)
Oil pressure (MPa)
Oil temperature (°C)
Position of the feed piston (mm)
+
Total working time of tool (min)
time of one debranching stroke/cycle
map of knots
CF quality index#2

79. Task 4.5: cutting process quality index
de-branching
time of one debranching stroke/cycle

80. Task 4.5: cutting process quality index
de-branching
map of knots – displayed for operator
CF quality index#2

81. two quality indexes (numbers in the range from 0 to 1) associated to wood/log properties
are determined:
 CP quality index #1: reflects the estimation of the “wood density” as related to the
cutting resistance during cross-cutting of log by chain saw. The quality index #1 value
is unique for the whole log.
CP quality index #1 = f(wood moisture content, tool wear, cutting
speed, feed speed, log diameter, ellipsoid shape, presence of
defects)
 CP quality index #2: reflects the “brancheness” of the log along its length and is
estimated by means of signals associated with cutting out branches. The quality
index #2 is spatially reolved.
CP quality index #2 = f(hydraulic pressure changes along the log
length, changes of cutting forces in time, number of AE events or
sound pressure level)
Task 4.5: cutting process quality index
algorithms for data mining

82. Task 4.5: cutting process quality index
Challenges
Important delay with prototype developing:
the equipment just now ready for testing
How to physically install sensors on the processor?
How reliable will be measurement of cutting forces in forest?
What is an effect of tool wear?
How to link cutting force (wood density) with recent quality sorting
rules?
Delimbing or debarkining?

83. Thank you very much

84. Task 4.6: Implementation of the log/biomass grading system
Task Leader: CNR
Task Participants: GRAPHITECH, COMPOLAB ,MHG, BOKU, GRE, TRE
Starting : June 2014
Ending: July 2016
Estimated person-month = 1.50 (GRAPHITECH) + 2.0 (CNR) + 1.00 (COMPOLAB) + 1.00
(MHG) + 1.00 (BOKU), 0.50 (GRE) + 1.00 (TRE)
CNR: will coordinate the research necessary, develop the software tools (expert systems)
and integrate all available information for quality grading
TRE, GRE, COMPOLAB: incorporate material parameters from the multisource data
extracted along the harvesting chain
GRAPHITECH: integration with the classification rules for commercial assortments, linkage
with the database of market prices for woody commodities
MHG: propagate information about material characteristics along the value chain (tracking)
and record/forward this information through the cloud database
BOKU: validation of the grading system

85. Task 4.6: Implementation of the grading system
Objectives
The goals of this task are:
• to develop reliable models for predicting the grade (quality
class) of the harvested log/biomass.
• to provide objective/automatic tools enabling optimization of
the resources (proper log for proper use)
• to contribute for the harmonization of the current grading
practice and classification rules
• provide more (value) wood from less trees

86. Task 4.6: Implementation of the grading system
Deliverables
D.4.01 Existing grading rules for log/biomass
Report: This deliverable will contain a report on existing log/biomass grading criteria and
criteria gap analyses
Delivery Date: October 2014 (M.10) DONE
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes
and report on the validation procedure
Prototype: This deliverable will contain a report on the validation procedure, and results of
the quality class prediction models, and integration in the SLOPE cloud data base
Delivery Date: June 2016 (M.30)

87. T4.6: Implementation of the log/biomass grading
system
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
the resources planned: 8.0 M/M
the resources utilized:
PROBLEMS: Delay related to other tasks within WP4
SOLUTIONS: intensify efforts, implement ready theoretical solutions developed up-to-data


31.06.2016

 draft: October 2014
accepted: July 2015

 







88. Task 4.6: Implementation of the grading system
The concept (logic)
3D quality index (WP 4.1)
NIR quality index (WP 4.2)
HI quality index (WP 4.3)
SW quality index (WP 4.4)
CP quality index (WP 4.5)
Data from harvester
Other available info
Quality class
Threshold values and
variability models of
properties will be
defined for the
different end-uses
(i.e. wood processing
industries, bioenergy
production).
(WP5)

89. Task 4.6: Implementation of the grading system
implementation#1: Quality index concept
 Each index can be between:
0 – bad, not suitable, low, , …
and
1 – good, proper, perfect, appreciated, , …
 Computed for:
 Suitability modeled separately for different destination fields:
 resonance wood, structural timber, pulp/paper, chemical conversion…
 Presence of various defects, such as:
 Rotten wood, knottiness, compression wood, eccentric pith…
 Compatibility with standard quality classes
For each task of WP4 series of quality indexes will be computed as default

90. Task 4.6: Implementation of the grading system
implementation#2: Quality index computation
Set of experimental samples
with characteristics representing
poor quality QI = “0”
Set of experimental samples
with characteristics representing
superb quality QI = “1”
PLS models for prediction
validation of models
implementation of models
for routine data processing
never
ending
tuning
process

91. Task 4.6: Implementation of the grading system
implementation#3: summary of QI + weights
weight for each quality aspect
range
construct.
wood
biomass
/fuel pulp plywood class A class D
T4.2 moisture 0 - 1 0,2 1
density 0 - 1 1 1 1 1 1
carbohydrate content 0 - 1 1
lignin content 0 - 1 1 1
calorific value 0 - 1 1
rotten wood progress 0 - 1 -100 1 1 1
early/late wood ratio 0 - 1 0,2 1
width of sapwood 0 - 1 0,1
pith eccentricity 0 - 1 0,5 0,8 1
width of bark 0 - 1 0,2 1 1 1
presence of reaction wood 0 or 1 1 1 1 1
presence of resin 0 or 1 0,2 1 1
presence of rot 0 or 1 -100 0,7 1
presence of bark 0 or 1 -0,5 0,2 1 1
presence of contamination –soil 0 or 1 -0,1 -0,1
presence of contamination – oil 0 or 1 1
T4.3 ovalness 0 - 1 1 2 1
ratio of knot area 0 - 1 0,2 1
knot count 0 - 1 0,2 1
T4.4 velocity 0 - 1 1 0,8 1
homogenity velocity 0 - 1 1 1 1
density 0 - 1 1 0,8 1
elasticity 0 - 1 1 0,3 1
suitability for pales 1
T4.5 knotines 0 - 1 0,5 0,6 1
knots size 0 - 1 2 0,6 1
knot spatial distribution 0 - 1 1 1 1
log density 0 - 1 1 1 1 1
easy for processing 0 - 1 1 1 1 1

92. Task 4.6: Implementation of the grading system
implementation#4: maths behind
For each log:
∑
∑ ⋅
=
i
ii
market
w
QIw
Q
where:
Qmarket – log quality for specific use/market
wi – weight of quality index
QIi – quality index assessed by sensor
)( ii wtresholdQI >∀
where:
treshold(wi) – minumum value of QIi
AND/OR*
* - depending on application

93. Task 4.6: Implementation of the grading system
implementation#4: quality map
Map of knots
Map of quality
QIT4.4
QIT4.1
QIT4.2
QIT4.3
QIT4.5

94. Task 4.6: Implementation of the grading system
The concept (diagram)
Measure 3D shape of
several trees
Measure NIR spectra of
tree X in forest
Extract 3D shape of
tree X
Compute 3D quality in-
dexes for log X.1 … X.n
Measure NIR spectra of
tree X on processor
Measure NIR spectra of
tree X on the pale
Compute NIR quality in-
dex for tree X
Compute NIR quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dexes for log X.1 … X.n
Data base for harvest
data
Data base for Forest In-
formation System
Determine quality grade
for log X.1 … X.n
T4.1
T4.2
Measure hyperspectral
image of tree X in forest
Measure cross section
image of log X.1 … X.n
Measure NIR spectra of
tree X on the pale
Compute HI quality index
for tree X
Compute HI quality in-
dexes for log X.1 … X.n
Compute HI quality in-
dexes for log X.1 … X.n
T4.3
Measure stress waves on
tree X in forest
Measure stress waves of
tree X on processor
Measure stress waves of
log X.1 …X.n on the pale
Compute SW quality in-
dex for tree X
Compute SW quality in-
dexes for log X.1 … X.n
Compute SW quality in-
dexes for log X.1 … X.n
T4.4
Measure delimbing force
on log X.1 … X.n
Measure cross-cutting
force on log X.1 … X.n
Compute CF quality in-
dexes for tree X
Compute CF quality in-
dexes for log X.1 … X.n
T4.5

95. Task 4.6: Implementation of the grading system
The concept (diagram)#1
Measure 3D shape of
several trees
Measure NIR spectra of
tree X in forest
Extract 3D shape of
tree X
Compute 3D quality in-
dexes for log X.1 … X.n
Measure NIR spectra of
tree X on processor
Measure NIR spectra of
tree X on the pale
Compute NIR quality in-
dex for tree X
Compute NIR quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dexes for log X.1 … X.n
Data base for harvest
data
Determine quality
for log X.1 …
T4.1
T4.2
Measure hyperspectral
image of tree X in forest
Measure cross section
image of log X.1 … X.n
Measure NIR spectra of
tree X on the pale
Compute HI quality index
for tree X
Compute HI quality in-
dexes for log X.1 … X.n
Compute HI quality in-
dexes for log X.1 … X.n
T4.3

96. Task 4.6: Implementation of the grading system
The concept (diagram)#2
Measure stress waves on
tree X in forest
Measure stress waves of
tree X on processor
Measure stress waves of
log X.1 …X.n on the pale
Compute SW quality in-
dex for tree X
Compute SW quality in-
dexes for log X.1 … X.n
Compute SW quality in-
dexes for log X.1 … X.n
T4.4
Measure delimbing force
on log X.1 … X.n
Measure cross-cutting
force on log X.1 … X.n
Compute CF quality in-
dexes for tree X
Compute CF quality in-
dexes for log X.1 … X.n
T4.5

97. Task 4.6: Implementation of the grading system
The concept (diagram)#3
Compute 3D quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dex for tree X
Compute NIR quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dexes for log X.1 … X.n
Data base for harvest
data
Data base for Forest In-
formation System
Determine quality grade
for log X.1 … X.n
Compute HI quality index
for tree X
Compute HI quality in-
dexes for log X.1 … X.n
Compute HI quality in

98. Task 4.6: Implementation of the grading system
data flow & in-field hardware
NI CompactRio master
Database
NI CompactRio client
FRID
weight
fuel
???
Data storage
CP
NIR
HI
SW
camera
kinect

99. Task 4.6: Implementation of the grading system
Challenges
What sensors set is optimal (provide usable/reliable information)?
How to merge various types of indexes/properties?
Can the novel system be accepted by “conservative” forest (and
wood transformation) industry?
How the SLOPE quality grading will be related to established
classes?
the final answer possible only after demonstrations

100. Thank you very much