Work Package 4: Multi-sensor model-based quality

control of mountain forest production

by CNR: Jakub Sandak

3rd SLOPE project, 19 January 2015, San Michele AllAdige

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

Technical Meeting 19-21 Jan 15

Work Package 4: work to be done T4.1

Quality rules & specificationsCNR, TRE:

Develop tool Harvest Simulator TRE:

Develop models of treesGRA, TRE:

Compare models with real dataTRE, GRA, TRE:

Link automatic system with visualTRE,CNR:

Develop 3D quality indexTRE, CNR:

Measurement of standing treesCNR, TRE:

Measurement of felled treesCNR:

T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04

Determine optimal protocolCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Measure NIR on standing treesTRE, CNR, FLY:

Measure NIR on felled treesCNR, GRE:

Measure NIR on processor headCNR, COM:

Measure NIR on pale of logsCNR, BOK:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop NIR quality indexCNR, BOK:

Develop provenance NIR modelsCNR, BOK:

Design data base of NIR spectraBOK, CNR:

T4.2 NIR quality

D04.03

CNR

D04.08

CNR

Determine usabilityCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Imaging standing trees BOK, FLY, TRE:

Imaging fallen trees BOK, GRE:

Imaging on processor headBOK, COM:

Imaging on pale of logsBOK, CNR:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop hyperspectral indexCNR, BOK:

Design data base of hyperspectraBOK, CNR:

T4.3 hyperspectral quality

D04.04

D04.09

BOK

BOK

Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:

D01.04

Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:

Develop report on using SWCNR:

Develop models for SW qualityCNR:

Test on standing trees CNR, GRE:

Tests on fallen trees CNR, GRE:

Tests on processor headCNR, COM:

Imaging on pale of logsCNR:

Develop SW quality indexCNR:

Define quality thresholdsCNR:

Analyze material dependant factorsCNR:

T4.4 stress wave quality

D04.05

D04.10

CNR

CNR

Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:

D01.04

Determine quality requirements for high-end assortments

CNR:

Laboratory scale tests for delimbing energy needs

CNR:

Develop CP quality indexCNR:

T4.5 cutting power quality

D04.11

D04.06

CNR

CNR

Determine optimal set-up for the measurement of cutting forces on the processor headCNR:

D01.04Laboratory 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 CPCNR:

Link in-field data with cloud database

CNR:

Compare automatic and visual grading resultsBOK, CNR:

Determine threshold valuesCNR:

Develop grading expert systemCNR:

Develop algorithm for data fusionCNR, COM, TRE:

In field visual quality assessment CNR, BOK:

Develop data base for prices of woody commodities

CNR, BOK:

Reliability studiesBOK:

Economic advantage studiesBOK, CNR:

T4.6 quality implementation

D04.01

CNR

D04.12

CNR

Identify grading rules for standard and niche productsCNR:

Prepare state-of-the-art report on grading rulesCNR:

Technical Meeting 19-21 Jan 15

T4.1: Data mining and model integration of stand quality indicators from on-field survey

Quality rules & specificationsCNR, TRE:

Develop tool Harvest Simulator TRE:

Develop models of treesGRA, TRE:

Compare models with real dataTRE, GRA, TRE:

Link automatic system with visualTRE,CNR:

Develop 3D quality indexTRE, CNR:

Measurement of standing treesCNR, TRE:

Measurement of felled treesCNR:

T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

31 October 2014

31.05.2015

the resources planned: 6.5 M/Mthe resources utilized: ?.? M/M (CNR: 0.195)PROBLEMS: Not reported

Technical Meeting 19-21 Jan 15

Work Package 4: work to be done T4.2

Quality rules & specificationsCNR, TRE:

Develop tool Harvest Simulator TRE:

Develop models of treesGRA, TRE:

Compare models with real dataTRE, GRA, TRE:

Link automatic system with visualTRE,CNR:

Develop 3D quality indexTRE, CNR:

Measurement of standing treesCNR, TRE:

Measurement of felled treesCNR:

T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04

Determine optimal protocolCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Measure NIR on standing treesTRE, CNR, FLY:

Measure NIR on felled treesCNR, GRE:

Measure NIR on processor headCNR, COM:

Measure NIR on pale of logsCNR, BOK:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop NIR quality indexCNR, BOK:

Develop provenance NIR modelsCNR, BOK:

Design data base of NIR spectraBOK, CNR:

T4.2 NIR quality

D04.03

CNR

D04.08

CNR

Determine usabilityCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Imaging standing trees BOK, FLY, TRE:

Imaging fallen trees BOK, GRE:

Imaging on processor headBOK, COM:

Imaging on pale of logsBOK, CNR:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop hyperspectral indexCNR, BOK:

Design data base of hyperspectraBOK, CNR:

T4.3 hyperspectral quality

D04.04

D04.09

BOK

BOK

Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:

D01.04

Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:

Develop report on using SWCNR:

Develop models for SW qualityCNR:

Test on standing trees CNR, GRE:

Tests on fallen trees CNR, GRE:

Tests on processor headCNR, COM:

Imaging on pale of logsCNR:

Develop SW quality indexCNR:

Define quality thresholdsCNR:

Analyze material dependant factorsCNR:

T4.4 stress wave quality

D04.05

D04.10

CNR

CNR

Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:

D01.04

Determine quality requirements for high-end assortments

CNR:

Laboratory scale tests for delimbing energy needs

CNR:

Develop CP quality indexCNR:

T4.5 cutting power quality

D04.11

D04.06

CNR

CNR

Determine optimal set-up for the measurement of cutting forces on the processor headCNR:

D01.04Laboratory 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 CPCNR:

Link in-field data with cloud database

CNR:

Compare automatic and visual grading resultsBOK, CNR:

Determine threshold valuesCNR:

Develop grading expert systemCNR:

Develop algorithm for data fusionCNR, COM, TRE:

In field visual quality assessment CNR, BOK:

Develop data base for prices of woody commodities

CNR, BOK:

Reliability studiesBOK:

Economic advantage studiesBOK, CNR:

T4.6 quality implementation

D04.01

CNR

D04.12

CNR

Identify grading rules for standard and niche productsCNR:

Prepare state-of-the-art report on grading rulesCNR:

Technical Meeting 19-21 Jan 15

T4.2: Evaluation of NIRS as a tool for determination of log/biomass quality index

D01.04

Determine optimal protocolCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Measure NIR on standing treesTRE, CNR, FLY:

Measure NIR on felled treesCNR, GRE:

Measure NIR on processor headCNR, COM:

Measure NIR on pale of logsCNR, BOK:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop NIR quality indexCNR, BOK:

Develop provenance NIR modelsCNR, BOK:

Design data base of NIR spectraBOK, CNR:

T4.2 NIR quality

D04.03

CNR

D04.08

CNR

Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:

the resources planned: 11.5 M/Mthe resources utilized: ??.? M/M (CNR: 3.325)PROBLEMS: Delay related to the processor head and final sensor selectionSOLUTIONS: LAB scanner + list of sensor(s) for purchase ready

31 October 2014

?! 30.06.2015

Technical Meeting 19-21 Jan 15

T4.2: Gantt (original)

Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass quality index in mountain forests1 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 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.2D.4.03

D.4.08test sensors avaliable on the market

finalize conceptdesign/adopt to the processor

test electronic systemassemble hardware

collect reference samplesanalyse reference samples

test hardware + softwarecalibrate system

develop algorithm for NIR qualityindexintegrate NIR quality index with quality grading/optymization (T4.6) D.4.12

D.4.03 Establishing NIR measurement protocol D.4.08 Estimation of log/biomass quality by NIR D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure

Technical Meeting 19-21 Jan 15

Work Package 4: work to be done T4.3

Quality rules & specificationsCNR, TRE:

Develop tool Harvest Simulator TRE:

Develop models of treesGRA, TRE:

Compare models with real dataTRE, GRA, TRE:

Link automatic system with visualTRE,CNR:

Develop 3D quality indexTRE, CNR:

Measurement of standing treesCNR, TRE:

Measurement of felled treesCNR:

T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04

Determine optimal protocolCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Measure NIR on standing treesTRE, CNR, FLY:

Measure NIR on felled treesCNR, GRE:

Measure NIR on processor headCNR, COM:

Measure NIR on pale of logsCNR, BOK:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop NIR quality indexCNR, BOK:

Develop provenance NIR modelsCNR, BOK:

Design data base of NIR spectraBOK, CNR:

T4.2 NIR quality

D04.03

CNR

D04.08

CNR

Determine usabilityCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Imaging standing trees BOK, FLY, TRE:

Imaging fallen trees BOK, GRE:

Imaging on processor headBOK, COM:

Imaging on pale of logsBOK, CNR:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop hyperspectral indexCNR, BOK:

Design data base of hyperspectraBOK, CNR:

T4.3 hyperspectral quality

D04.04

D04.09

BOK

BOK

Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:

D01.04

Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:

Develop report on using SWCNR:

Develop models for SW qualityCNR:

Test on standing trees CNR, GRE:

Tests on fallen trees CNR, GRE:

Tests on processor headCNR, COM:

Imaging on pale of logsCNR:

Develop SW quality indexCNR:

Define quality thresholdsCNR:

Analyze material dependant factorsCNR:

T4.4 stress wave quality

D04.05

D04.10

CNR

CNR

Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:

D01.04

Determine quality requirements for high-end assortments

CNR:

Laboratory scale tests for delimbing energy needs

CNR:

Develop CP quality indexCNR:

T4.5 cutting power quality

D04.11

D04.06

CNR

CNR

Determine optimal set-up for the measurement of cutting forces on the processor headCNR:

D01.04Laboratory 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 CPCNR:

Link in-field data with cloud database

CNR:

Compare automatic and visual grading resultsBOK, CNR:

Determine threshold valuesCNR:

Develop grading expert systemCNR:

Develop algorithm for data fusionCNR, COM, TRE:

In field visual quality assessment CNR, BOK:

Develop data base for prices of woody commodities

CNR, BOK:

Reliability studiesBOK:

Economic advantage studiesBOK, CNR:

T4.6 quality implementation

D04.01

CNR

D04.12

CNR

Identify grading rules for standard and niche productsCNR:

Prepare state-of-the-art report on grading rulesCNR:

Technical Meeting 19-21 Jan 15

T4.3: Evaluation of hyperspectral imaging for the determination of log/biomass quality index

Determine usabilityCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Imaging standing trees BOK, FLY, TRE:

Imaging fallen trees BOK, GRE:

Imaging on processor headBOK, COM:

Imaging on pale of logsBOK, CNR:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop hyperspectral indexCNR, BOK:

Design data base of hyperspectraBOK, CNR:

T4.3 hyperspectral quality

D04.04

D04.09

BOK

BOK

Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:

D01.04

the resources planned: 13.5 M/Mthe resources utilized: ??.? M/M (CNR: 0.835)PROBLEMS: Delay with Deliverable + setting of the lab scanner + final sensor selectionSOLUTIONS: LAB scanner + collaboration with experts + new solutions for HI sensor(s)

31 January 2015

?! 31.07.2015

Technical Meeting 19-21 Jan 15

Work Package 4: work to be done T4.4

Quality rules & specificationsCNR, TRE:

Develop tool Harvest Simulator TRE:

Develop models of treesGRA, TRE:

Compare models with real dataTRE, GRA, TRE:

Link automatic system with visualTRE,CNR:

Develop 3D quality indexTRE, CNR:

Measurement of standing treesCNR, TRE:

Measurement of felled treesCNR:

T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04

Determine optimal protocolCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Measure NIR on standing treesTRE, CNR, FLY:

Measure NIR on felled treesCNR, GRE:

Measure NIR on processor headCNR, COM:

Measure NIR on pale of logsCNR, BOK:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop NIR quality indexCNR, BOK:

Develop provenance NIR modelsCNR, BOK:

Design data base of NIR spectraBOK, CNR:

T4.2 NIR quality

D04.03

CNR

D04.08

CNR

Determine usabilityCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Imaging standing trees BOK, FLY, TRE:

Imaging fallen trees BOK, GRE:

Imaging on processor headBOK, COM:

Imaging on pale of logsBOK, CNR:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop hyperspectral indexCNR, BOK:

Design data base of hyperspectraBOK, CNR:

T4.3 hyperspectral quality

D04.04

D04.09

BOK

BOK

Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:

D01.04

Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:

Develop report on using SWCNR:

Develop models for SW qualityCNR:

Test on standing trees CNR, GRE:

Tests on fallen trees CNR, GRE:

Tests on processor headCNR, COM:

Imaging on pale of logsCNR:

Develop SW quality indexCNR:

Define quality thresholdsCNR:

Analyze material dependant factorsCNR:

T4.4 stress wave quality

D04.05

D04.10

CNR

CNR

Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:

D01.04

Determine quality requirements for high-end assortments

CNR:

Laboratory scale tests for delimbing energy needs

CNR:

Develop CP quality indexCNR:

T4.5 cutting power quality

D04.11

D04.06

CNR

CNR

Determine optimal set-up for the measurement of cutting forces on the processor headCNR:

D01.04Laboratory 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 CPCNR:

Link in-field data with cloud database

CNR:

Compare automatic and visual grading resultsBOK, CNR:

Determine threshold valuesCNR:

Develop grading expert systemCNR:

Develop algorithm for data fusionCNR, COM, TRE:

In field visual quality assessment CNR, BOK:

Develop data base for prices of woody commodities

CNR, BOK:

Reliability studiesBOK:

Economic advantage studiesBOK, CNR:

T4.6 quality implementation

D04.01

CNR

D04.12

CNR

Identify grading rules for standard and niche productsCNR:

Prepare state-of-the-art report on grading rulesCNR:

Technical Meeting 19-21 Jan 15

T4.4: Data mining and model integration of log/biomass quality indicators from stress-wave

Develop report on using SWCNR:

Develop models for SW qualityCNR:

Test on standing trees CNR, GRE:

Tests on fallen trees CNR, GRE:

Tests on processor headCNR, COM:

Imaging on pale of logsCNR:

Develop SW quality indexCNR:

Define quality thresholdsCNR:

Analyze material dependant factorsCNR:

T4.4 stress wave quality

D04.05

D04.10

CNR

CNR

Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:

D01.04

Determine quality requirements for high-end assortments

CNR:

the resources planned: 5.5 M/Mthe resources utilized: ?.? M/M (CNR: 2.640)PROBLEMS: Delay related to the processor head and final sensor selectionSOLUTIONS: LAB scanner + collaboration with engineers + list of sensor(s) for purchase ready

23 December 2014

?! 31.08.2015

Technical Meeting 19-21 Jan 15

T4.4: Gantt (original)

Data mining and model integration of log/biomass quality indicators from stress-wave (SW) measurements, for the determination of the SW quality 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 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.4D.4.05

D.4.10finalize concept

field testsdesign/adopy to the processor

test electronic systemassemble hardware

test hardware + softwarecallibrate system

develop algorithm for CP Q_indexintegrate CP quality index with quality grading/optimization (T4.6) D.4.12

D.4.05 Establishing acoustic-based measurement protocolD.4.10 Estimation of log quality by acoustic methodsD.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure

Technical Meeting 19-21 Jan 15

Work Package 4: work to be done T4.5

Quality rules & specificationsCNR, TRE:

Develop tool Harvest Simulator TRE:

Develop models of treesGRA, TRE:

Compare models with real dataTRE, GRA, TRE:

Link automatic system with visualTRE,CNR:

Develop 3D quality indexTRE, CNR:

Measurement of standing treesCNR, TRE:

Measurement of felled treesCNR:

T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04

Determine optimal protocolCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Measure NIR on standing treesTRE, CNR, FLY:

Measure NIR on felled treesCNR, GRE:

Measure NIR on processor headCNR, COM:

Measure NIR on pale of logsCNR, BOK:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop NIR quality indexCNR, BOK:

Develop provenance NIR modelsCNR, BOK:

Design data base of NIR spectraBOK, CNR:

T4.2 NIR quality

D04.03

CNR

D04.08

CNR

Determine usabilityCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Imaging standing trees BOK, FLY, TRE:

Imaging fallen trees BOK, GRE:

Imaging on processor headBOK, COM:

Imaging on pale of logsBOK, CNR:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop hyperspectral indexCNR, BOK:

Design data base of hyperspectraBOK, CNR:

T4.3 hyperspectral quality

D04.04

D04.09

BOK

BOK

Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:

D01.04

Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:

Develop report on using SWCNR:

Develop models for SW qualityCNR:

Test on standing trees CNR, GRE:

Tests on fallen trees CNR, GRE:

Tests on processor headCNR, COM:

Imaging on pale of logsCNR:

Develop SW quality indexCNR:

Define quality thresholdsCNR:

Analyze material dependant factorsCNR:

T4.4 stress wave quality

D04.05

D04.10

CNR

CNR

Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:

D01.04

Determine quality requirements for high-end assortments

CNR:

Laboratory scale tests for delimbing energy needs

CNR:

Develop CP quality indexCNR:

T4.5 cutting power quality

D04.11

D04.06

CNR

CNR

Determine optimal set-up for the measurement of cutting forces on the processor headCNR:

D01.04Laboratory 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 CPCNR:

Link in-field data with cloud database

CNR:

Compare automatic and visual grading resultsBOK, CNR:

Determine threshold valuesCNR:

Develop grading expert systemCNR:

Develop algorithm for data fusionCNR, COM, TRE:

In field visual quality assessment CNR, BOK:

Develop data base for prices of woody commodities

CNR, BOK:

Reliability studiesBOK:

Economic advantage studiesBOK, CNR:

T4.6 quality implementation

D04.01

CNR

D04.12

CNR

Identify grading rules for standard and niche productsCNR:

Prepare state-of-the-art report on grading rulesCNR:

Technical Meeting 19-21 Jan 15

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 quality indexCNR:

T4.5 cutting power quality

D04.11

D04.06

CNR

CNR

Determine optimal set-up for the measurement of cutting forces on the processor headCNR:

D01.04Laboratory 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 CPCNR:

the resources planned: 6.0 M/Mthe resources utilized: ?.? M/M (CNR: 0.480)PROBLEMS: Delay related to the processor head and final sensor selection/designSOLUTIONS: LAB scanner + collaboration with engineers + list of sensor(s) for purchase ready

31.01. 2015

31.09.2015

Technical Meeting 19-21 Jan 15

T4.5: Gantt (original)

Evaluation of cutting process (CP) for the determination of log/biomass CP quality index1 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 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.5D.4.06

D.4.11finalize concept

design/adopt to the processortest electronic system

assemble hardwaretest hardware + software

calibrate systemdevelop algorithm for CP Q_index

integrate CP quality index with quality grading/optymization (T4.6) D.4.12

D.4.06 Establishing cutting power measurement protocolD.4.11 Estimation of log quality by cutting power analysis D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure

Technical Meeting 19-21 Jan 15

Work Package 4: work to be done T4.6

Quality rules & specificationsCNR, TRE:

Develop tool Harvest Simulator TRE:

Develop models of treesGRA, TRE:

Compare models with real dataTRE, GRA, TRE:

Link automatic system with visualTRE,CNR:

Develop 3D quality indexTRE, CNR:

Measurement of standing treesCNR, TRE:

Measurement of felled treesCNR:

T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04

Determine optimal protocolCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Measure NIR on standing treesTRE, CNR, FLY:

Measure NIR on felled treesCNR, GRE:

Measure NIR on processor headCNR, COM:

Measure NIR on pale of logsCNR, BOK:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop NIR quality indexCNR, BOK:

Develop provenance NIR modelsCNR, BOK:

Design data base of NIR spectraBOK, CNR:

T4.2 NIR quality

D04.03

CNR

D04.08

CNR

Determine usabilityCNR:

Calibration transferBOK, CNR:

Develop models for labCNR, BOK:

Imaging standing trees BOK, FLY, TRE:

Imaging fallen trees BOK, GRE:

Imaging on processor headBOK, COM:

Imaging on pale of logsBOK, CNR:

Develop models for in fieldCNR, BOK:

Compare models with lab dataCNR, BOK:

Develop hyperspectral indexCNR, BOK:

Design data base of hyperspectraBOK, CNR:

T4.3 hyperspectral quality

D04.04

D04.09

BOK

BOK

Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:

D01.04

Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:

Develop report on using SWCNR:

Develop models for SW qualityCNR:

Test on standing trees CNR, GRE:

Tests on fallen trees CNR, GRE:

Tests on processor headCNR, COM:

Imaging on pale of logsCNR:

Develop SW quality indexCNR:

Define quality thresholdsCNR:

Analyze material dependant factorsCNR:

T4.4 stress wave quality

D04.05

D04.10

CNR

CNR

Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:

D01.04

Determine quality requirements for high-end assortments

CNR:

Laboratory scale tests for delimbing energy needs

CNR:

Develop CP quality indexCNR:

T4.5 cutting power quality

D04.11

D04.06

CNR

CNR

Determine optimal set-up for the measurement of cutting forces on the processor headCNR:

D01.04Laboratory 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 CPCNR:

Link in-field data with cloud database

CNR:

Compare automatic and visual grading resultsBOK, CNR:

Determine threshold valuesCNR:

Develop grading expert systemCNR:

Develop algorithm for data fusionCNR, COM, TRE:

In field visual quality assessment CNR, BOK:

Develop data base for prices of woody commodities

CNR, BOK:

Reliability studiesBOK:

Economic advantage studiesBOK, CNR:

T4.6 quality implementation

D04.01

CNR

D04.12

CNR

Identify grading rules for standard and niche productsCNR:

Prepare state-of-the-art report on grading rulesCNR:

Technical Meeting 19-21 Jan 15

T4.6: Implementation of the log/biomass grading system

Link in-field data with cloud database

CNR:

Compare automatic and visual grading resultsBOK, CNR:

Determine threshold valuesCNR:

Develop grading expert systemCNR:

Develop algorithm for data fusionCNR, COM, TRE:

In field visual quality assessment CNR, BOK:

Develop data base for prices of woody commodities

CNR, BOK:

Reliability studiesBOK:

Economic advantage studiesBOK, CNR:

T4.6 quality implementation

D04.01

CNR

D04.12

CNR

Identify grading rules for standard and niche productsCNR:

Prepare state-of-the-art report on grading rulesCNR:

the resources planned: 8.0 M/Mthe resources utilized: ?.? M/M (CNR: 1.821)PROBLEMS: Delay related to other tasksSOLUTIONS: LAB scanner + prototype software developed in lab

31 October 2014

31.03.2016

Technical Meeting 19-21 Jan 15

T4.6: Gantt (original)

Implementation of the log/biomass grading system1 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 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.6D.4.01

D.4.12surveys

literature researchtest quality measuring systems

develop software for integration of quality indexestest software

calibrate systemvalidate the algorithm/system

D.4.01 Existing grading rules for log/biomassD.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure

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fulfillment of the project work plan:related deliverables

WP4 6th reporting period

task deliverable titletype ofdeliverable

leadparticipant

due date foreseen or actual delivery date comment

T4.1D4.2 on field survay data for tree characterization report TRE 31.10.2014 31.10.2014 draft

D4.7 estimation of log/biomass quality by external tree shape analysis software tool TRE 31.05.2015 same as planed

T4.2D4.3 establisghing NIR measurement protocol report CNR 31.10.2014 31.10.2014 draft

D4.8 estimation of log/biomass quality by NIR software tool CNR 30.06.2015 same as planed

T4.3D4.4 establisghing hyperspectral imaging measurementprotocol report BOK 30.11.2014 DELAY

foreseen 31.01.2015

D4.9 estimation of log/biomass quality by hyperspectral imaging software tool BOK 31.07.2015 same as planed

T4.4D4.5 establishing acoustic-based measurement protocol report CNR 31.12.2014 31.12.2014 draft

D4.10 estimation of log/biomass quality by acoustic methods software tool CNR 31.08.2015 same as planed

T4.5D4.6 establisghing cutting power measurement protocol report CNR 31.01.2015 same as planed

D4.11 estimation of log/biomass quality by cutting power analysis software tool CNR 30.09.2015 same as planed

T4.6D4.1 existing grading rules for log/biomass report CNR 31.10.2014 31.10.2014 draft

D4.12 implementatio and callibration of prediction models for log/biomass quality classes software tool CNR 31.03.2016 same as planed

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Resources used (1 year): only CNR data available

reporting period

task deliverable 1st 2nd 3rd 4th 5th 6th usedforeseen

CNR % (CNR)foreseen

all

T4.1 D4.2 0,000 0,000 0,000 0,000 0,012 0,183 0,195 1 19,5 6,5D4.7 0,000 0,000 0,000 0,000 0,000 0,000 0,000

T4.2 D4.3 0,000 0,000 0,025 0,573 1,725 1,003 3,325 3,5 95,0 11,5D4.8 0,000 0,000 0,000 0,000 0,000 0,000 0,000

T4.3 D4.4 0,000 0,000 0,000 0,617 0,000 0,218 0,835 3,5 23,9 13,5D4.9 0,000 0,000 0,000 0,000 0,000 0,000 0,000

T4.4 D4.5 0,000 0,000 0,000 0,120 0,814 1,705 2,640 4 66,0 5,5D4.10 0,000 0,000 0,000 0,000 0,000 0,000 0,000

T4.5 D4.6 0,000 0,000 0,027 0,000 0,000 0,453 0,480 4 12,0 6,0D4.11 0,000 0,000 0,000 0,000 0,000 0,000 0,000

T4.6 D4.1 0,000 0,000 0,000 0,057 0,949 0,815 1,821 2 91,0 8,0D4.12 0,000 0,000 0,000 0,000 0,000 0,000 0,000

0,000 0,000 0,052 1,367 3,500 4,376 9,296 18 51

Technical Meeting 19-21 Jan 15

Work Package 4: Multi-sensor model-based quality

control of mountain forest production

Planning actions for all activities and deliverables to be executed in M13-18:

Finalize + close: D04.1, D04.2, D04.04, D04.05Deliver + finalize + close: D04.03, D04.6Initiate + deliver: D04.07, D04.08

Build lab scanner at CNR + purchase sensors + install sensors

Perform field tests with portable instruments

Collaborate with WP3 (and others) in hardware develeopment

Technical Meeting 19-21 Jan 15

Work Package 4: Multi-sensor model-based quality

control of mountain forest production

the expected potential impact in scientific, technological, economic, competition and social terms, and the beneficiaries' plan for the use and dissemination of foreground.

Technical Meeting 19-21 Jan 15

Work Package 4: Multi-sensor model-based quality

control of mountain forest production

Risks and mitigating actions:

Significant delay related to changes in the consortium:lack of the practical expertise of the processor head engineers; technical meetings, new partners/collaboratorsthe selection, purchase, set-up of the new processor hear is delayed; development of the laboratory scanner capable to simulate log scanning

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 (+outside) engineers

Technical Meeting 19-21 Jan 15

Work Package 4: Multi-sensor model-based quality

control of mountain forest production

criticalities, recommendations for partners/consortium

How about demonstrations?New schedule?Contribution of WP4 already during the first demo?All sensors are expected to work during first demo?

How to deal with the overall delay?Need to update Gantt(s) ?

How to deal with the new DoW?Need to update list of activities ?

The communication between partners is not optimal how to change it?

Technical Meeting 19-21 Jan 15

Work Package 4: Multi-sensor model-based quality

control of mountain forest production

Thank you! Grazie!

Technical Meeting 19-21 Jan 15

T4.1- 3D Quality IndexingDelive- rableNumber61

Deliverable Title

Lead benefi-ciary number

Estimated indicative person-months

Nature 62

Dissemi-nation level63

Delivery date 64

D4.01 Existing grading rules for log/biomass 2 1.00 R PU 10

D4.02 On-field survey data for tree characterization 9 3.00 R PU 10

D4.03 Establishing NIR measurement protocol 2 5.00 R PU 10

D4.04 Establishing hyperspectral imaging measurement protocol

6 6.00 R PU 11

D4.05 Establishingacoustic-based measurementprotocol

2 2.00 R PU 12

D4.06 Establishing cutting power measurementprotocol

2 2.00 R PU 13

D4.07 Estimation of log/biomass quality by externaltree shape analysis

9 5.50 P PU 17

D4.08 Estimation of log/biomass quality by NIR 2 8.00 P PU 18

D4.09 Estimation of log quality by hyperspectralimaging

6 11.00 P PU 19

Technical Meeting 19-21 Jan 15

T4.1- 3D Quality Indexing

Document 4.02 : (Complete By Treemetric sM10)

ON FIELD SURVEY FOR THE DETERMINATION OF 3D QUALITY INDEX

PRESENTATION:

Part 1: Overview of process for 3d model creation

Part2: TLS Quality Indicators

Part3: Harvest Simulation

Participants: Graphitech, CNR, FLYBY

D4.01 Existing Grading Rules (Complete by CNR)Very Detailed complex grading rules identified

Technical Meeting 19-21 Jan 15

T4.1 Overview

Task 4.1 - Data mining and model integration ofstand quality indicators from on-field survey forthe determination of the tree 3D quality index.

The Task 4.1 aims at evaluating the effectiveness/reliability, as qualityindicators, of single and combined parameters related to the externalcharacteristics of the standing tree, such as tree height, diameter, stemtaper, straightness, sweep and lean, branchiness, branch length, thicknessand dimension of the live crown

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Overview 3d model creating

Most basic parameters: Manually measured in field Diameter at breast height (DBH) Total tree height (h) RECORDED MANUALLY QUALITY INDICATORS

stem extraction from the point cloud 3D model of one sample tree

TLS

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TLS Analysis3D Model Creation

Steps:Pre-Processing: Filtering points and eliminating noise.

TLS point cloud filtered

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TLS Analysis

Step 2: Local DTM generation

Autostem software generates a best fit plane for the local DTM based on point cloud data.

Once Local DTM is established, tree profiles are defined relative to this.

Step 3: Single tree detection:

Autostem, Profile disks are fitted around cylinders in the point cloud data at every 10cm.

If insufficient points in the cloud a specific height, a disk is interpolated between nearest disks above and below.

Upper section of tree is calculated using local taper equations.

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TLS Analysis

Tree detection after filter

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TLS Analysis

Tree location

Tree position for a sample plot in Autostem

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TLS Analysis

Stemfiles are generated that fully support the Standard for "Forestry Data andCommunication" (StanForD) standard in a widely accepted file with ".stm" extension. Theallows for storing of x,y,z and diameter for each decimetre disk on the stem.

**This format does not allow for extra stem quality information to be stored. **

The extra information should either be stored in a linked file to the .stm file or a newapproach that does not support the StanForD standard can be used.

STEM FILE GENERATION

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TLS Quality Indicators

1-straight log;; 3 - maximum deviation (d) exceeds 1 cm over 1 m;

2- maximum deviation (d) does not exceed 1 cm over 1 m

4 - bow in more than one direction.

Straightness

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TLS Quality IndicatorsBranchiness

Branch inter-whorl distance: Some species of trees have branch patterns called whorls. The distance between whorls gives an important indicator of internal quality attributes

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TLS Quality Indicators

Forked Stem

TLS can be used to spot stem damage and defects. It can identify multi-stems and forks

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Harvest Simulation

Cutting instructions: Log quality specifications

Length: Targeted length of the log.

Small End Diameter (Min + Max SED)

Large End Diameter (Min + Max LED)

Straightness: Maximum deviation

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Harvest Simulator

Log ID/name LOG1 LOG2 LOG3 LOG4 LOG5 LOG6Length 3m 3.1m 3.1m 4.9m 4.9m 4.9mMin SED 7cm 12cm 12cm 16cm 16cm 16cmMax SED 36cm 36cmMin LED 13cmMax LED 38cmStraightness 20cm/m 2cm/m 2cm/m 1cm/m 1cm/m 1cm/m

Sample Log Constraints (Quality Limitations)

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Cutting SimulationForest Warehouse

SED Range 16-20cm 20-24cm 24-30cm 30cm+Weighting 500 700 600 500

Example of a range of weightings based on SED

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Harvest SimulationOptimising Waste Logs: waste log has a value of zero

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Conclusions

The main conclusions about the stand quality indicators and harvest simulation are thefollowing:

The MAIN indicators to define the log can be easily measured using the stem 3D modelcreated using TLS data.

Additional quality indicators can be measured and applied to the log constraints.

Internal quality of the timber can be estimated by using quality models. These modelswill be applied to the 3D stem profile and used in the harvest simulation. This will bedescribed in deliverable D4.07

Technical Meeting 19-21 Jan 15

TASK 4.2Evaluation of NIR spectroscopy as a tool for determination of

log/biomass quality index in mountain forest

Work Package 4: Multi-sensor model-based quality control of mountain forest production

Task leader: Anna Sandak (CNR)

Technical Meeting 19-21 Jan 15

Task 4.2: Partners involvement

Task Leader: CNRTask Partecipants: BOKU, FLY, GRE

CNR: Project leader, will coordinate all the partecipants of this taskwill evaluate the usability of NIR spectroscopy for characterization of bio-resources along the harvesting chainwill provide guidelines for proper collection and analysis of NIR spectra will develop the NIR quality index; to be involved in the overall log and biomass quality grading

Boku: will support CNR with laboratory measurement and calibration transfer

Greifenberg and Flyby: will support CNR in order to collect NIR spectra at various stages of the harvesting chain

Technical Meeting 19-21 Jan 15

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

The raw information provided here are near infrared spectra, to be later used for the determination of several properties (quality indicators) of the sample

4.2 Objectives

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4.2 Deliverables

Deliverable D.4.03 Establishing NIR measurement protocolevaluating 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 Estimated person Month= 5

Deliverable D.4.08 Estimation of log/biomass quality by NIRSet of chemometric models for characterization of different quality indicators by means of NIR and definition of NIR quality index

Delivery Date M18, June 2015Estimated person Month= 8

Technical Meeting 19-21 Jan 15

4.2 Timing

Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass quality index in mountain forests1 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 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.2D.4.03

D.4.08test sensors avaliable on the market

finalize conceptdesign/adopt to the processor

test electronic systemassemble hardware

collect reference samplesanalyse reference samples

test hardware + softwarecalibrate system

develop algorithm for NIR qualityindexintegrate NIR quality index with quality grading/optymization (T4.6) D.4.12

Technical Meeting 19-21 Jan 15

Deliverable 4.03

This report contains a recommended protocol for proper collection of NIR spectra within SLOPE project.

Brief presentation of currently available hardware, listing their advantages and disadvantages.

Basic information regarding mathematical algorithms for spectra pre-processing and data evaluation are provided.

Detailed procedure, potential obstacles and important considerations related to measurement of NIR along the whole harvesting scenario according to SLOPE approach are discussed here.

Brief description of various forest operation steps and information regarding quality indexes obtained at varying harvesting chain stages are provided.

Brief description of wood properties and log defects that can be measured and detected by means of NIR spectroscopy.

Technical Meeting 19-21 Jan 15

Spectrophotometers

laboratory

in-field

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NIR spectrophotometers

cameras FT-NIR DA LVF DM AOTF MEMS

Spectral range limited full limited limited full limited limited

Scanning time (s) cont. 30 1 0.5 10 1 1

resolution high very high high limited high limited limited

cost N/A high middle low middle middle middle

Signal/noise high high limited limited high limited limited

Calibrations transfer

limited very good

good good very good

good limited

Shock resistance yes no yes yes no yes yes

Suitable for SLOPE

Technical Meeting 19-21 Jan 15

Mathematical methods and algorithms suitable for NIR spectroscopic evaluation of log/wood quality in SLOPE scenario

Algorithms for pre-processing of spectraAveragingDerivativeSmoothing normalizationBaseline correctionMultiplicative Scatter Correction

Algorithms for NIR data post-processing and data miningCluster Analysis (CA)Principal Component Analysis (PCA)Identity Test (IT)Quick Compare (QC)Partial Least Squares (PLS)

Technical Meeting 19-21 Jan 15

NIR spectra will be collected at various stages of the harvesting chain

measurement procedures will be provided for each field test

In-field tests will be compared to laboratory results

Activities: Feasibility study and specification of the

measurement protocols for proper NIR data acquisition

Technical Meeting 19-21 Jan 15

Detailed procedure related to measurement of NIR along the whole harvesting scenario

Forest modelingNIR 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

Tree markingDirect measurement of the NIR spectra by means of portable instruments (DA and LVF) will be performed in parallel to the tree marking operation. The spectra will be collected and stored for further analysis (NIR quality index #2)

Cutting of treetesting 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)

Processor headNIR sensors will be integrated with the processor head (NIR quality index #4). All the sensors will be positioned on a lifting/lowering bar on the head processor near the cutting bar. The cutting bar will be activated in two modes: automatic and manual

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the scanning bar #1 with NIR sensor

Sensor position in the intelligent processor head

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CRio

NIR spectra (USB)

Control system

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Detailed procedure related to measurement of NIR along the whole harvesting scenario

Pile of logsThe 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

LaboratorySamples 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). However, samples will be conditioned afterward and their surfaces prepared (smoothed) in order to eliminate/minimize effects of the moisture variations and light scatter due to excessive roughness on the evaluation results of fresh samples.

Technical Meeting 19-21 Jan 15

Collection of NIR spectra and flow of samples/data at different stages of the harvesting process chain

(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#6 compute 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

Technical Meeting 19-21 Jan 15

Protocol for NIR measurement of logs/wood

Procedure for logs: turn on instruments warm up detector measure white reference measure black reference measure series of spectra save results post processing of spectra in field data mining

(assuming availability of previously developed chemometric models)

Procedure for wood: turn on instrument warm up detector perform instrument validation PQ (Performance Qualification) OQ (Operational Qualification) measure background measure series of spectra save results post-process the spectra develop calibration models perform calibration transfer (if required)

Technical Meeting 19-21 Jan 15

Important considerations

Logs:ResolutionMeasurement timeNumber of measurementsEffect of ruggedness (effect of moisture, temperature and vibrations)

Wood:Number os scanes per averagingNumber of measurementsSelection of scanning zones (wood section, early/late wood)Effect of roughness and surface preparationEffect of moistureEffect of time (surface deactivation)

Technical Meeting 19-21 Jan 15

Potential for detection of defects and determination of material properties as measured by means of various NIR sensors

Instrument type FT-NIR dispersive linear variable filter MicroNIR Moisture content of sample wet dry wet dry wet dry Surface of sample smooth rough smooth rough smooth rough smooth rough smooth rough smooth rough

knots resin pocket twist eccentric pith compression wood ? ? sweep taper shakes insects ? ? ? ? ? ? ? ? dote ? ? ? ? ? ? ? ? rot W

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stain ? ? ? ? ? ? ? lignin ? ? ? ? ? ? ? cellulose ? ? ? ? ? ? ? hemicellulose ? ? ? ? ? ? ? extractives ? ? ? ? ? ? ? microfibryl angle ? ? ? ? ? ? calorific value ? ? ? ? ? ? ? ? heartwood/sapwood ? ? ? ? density ? ? ? ? mechanical properties ? ? ? ? moisture content provenance ? resonance wood ? ? ? ? ? ? ? ? ? ? ?

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Technical Meeting 19-21 Jan 15

spectra pre-processing, wavelength selection,classification, calibration, validation, externalvalidation (sampling prediction verification)

prediction of the log/biomass intrinsic qualityindicators (such as moisture content, density,chemical composition, calorific value) (CNR).

classification models based on the qualityindicators will be developed and compared tothe classification based on the expertsknowledge.

calibrations transfer between laboratoryinstruments (already available) and portableones used in the field measurements in order toenrich the reliability of the prediction (BOKU).

Development and validation of chemometric models.

Technical Meeting 19-21 Jan 15

Thank you very much

Technical Meeting 19-21 Jan 15

SLOPEIntegrated proceSsing and controL systems fOr sustainable forest Production in mountain arEas

WORK PACKAGE 4: DEFINITION OFREQUIREMENTS AND SYSTEM ANALYSIS

T.4.3 EVALUATION OF HYPERSPECTRAL IMAGING (HI) FOR THEDETERMINATION OF LOG/BIOMASS HI QUALITY INDEX

THEME: Integrated processing and Control Systems forSustainable Production in Farms and ForestsDuration: 36 MonthsPartners: 10Coordinating institution: Fondazione GraphitechCoordinator: Dr. Raffaele De Amicis

Task leader: BOKU

Participants: GRAPHITECH, CNR, KESLA, FLY, GRE

Prepared by: Andreas Zitek, Katharina Bhm, Ferenc Firtha, Barbara Hinterstoisser

Technical Meeting 19-21 Jan 15

WT 4.3 - Aims Evaluating the usability of hyperspectral imaging for

characterization of bio-resources along the harvesting chain and providing guidelines for proper collection and analysis of data.

Intensive laboratory tests and transfer to field conditions will be tested and solutions ranked for their applicability in field

System calibration and calibration transfer Both visible range cameras and near infrared scanners will be

investigated.

Postprocessing of data with different chemometric approaches Development of the HI quality index for quality grading

from the set of characteristics as a result of image processing/data mining

Indices compared to expert judgements

Technical Meeting 19-21 Jan 15

Task 4.3 D 4.04. and D 4.09

Task 4.3 Evaluation of hyperspectral imaging (HI) for the determination of log/biomass HI quality index Deliverables under lead of BOKU

D 4.04 - Establishing hyperspectral measurement protocol (planned: month 11 November 2014; asked extension of deadline to January 2015 due to team reformation after death of Manfred Schwanninger)

D4.09 Estimation of log quality by hyperspectral imaging (planned: month 19 July 2015)

Project meeting19-22/jan/2015

Task 4.3 - NIR vs. VIS/NIR hyperspectral imaging

Project meeting19-22/jan/2015

Spectra at on one spot Spectra at each pixel

Ranges within the electromagentic spectrum Visible wavelength range ~ 390-700 nm NIR wavelength range ~ 780 nm and 3 m

Advantages: remote sensing segmentation of object scanning non-homogeneous surfaceDisadvantages: non-izolated (lower signal to noise ratio) setup dependant calibration (distance, lens, illumination) indefinite geometry (illumination/observation angle) huge amount of data must be processedSpecific software needed (control system, calibration, data processing)

Task 4.3 - HSI background

RGB 3 colours

Project meeting19-22/jan/2015

After: BURGER, J. & KAUAKYT, A., 2013. Visual Chemometrics Interactive Software for Hyperspectral Image Exploration and Analysis. 27 March, 2013 Gembloux, BE: SIA BurgerMetrics, Riga, Latvia.

Task 4.3 - HSI background

Multispectral 4-10 wavelenghts

Project meeting19-22/jan/2015

After: BURGER, J. & KAUAKYT, A., 2013. Visual Chemometrics Interactive Software for Hyperspectral Image Exploration and Analysis. 27 March, 2013 Gembloux, BE: SIA BurgerMetrics, Riga, Latvia.

Task 4.3 - HSI background

Hyperspectral hypercube > 100 wavelengths, quasi-continous, nm steps

After: BURGER, J. & KAUAKYT, A., 2013. Visual Chemometrics Interactive Software for Hyperspectral Image Exploration and Analysis. 27 March, 2013 Gembloux, BE: SIA BurgerMetrics, Riga, Latvia.

Project meeting19-22/jan/2015

Task 4.3 HSI general setups

Whiskbroom imaging : During whiskbroom imaging the sample is scanned pixel per pixel in the xyspatial 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.

Pushbroom imaging: Pushbroom imaging as a line scanning system acquires the information for each pixel in the line at once.

Project meeting19-22/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.

Task 4.3 available systems

At CORVINUS universityHeadWall Photonics push-broom hyperspectral system (Xenics NIR camera: 320*256 matrix, 14 bit A/D, 5 nm resolution, 250 mm Y-table gear, stable diffuse 45/0 illumination).

Project meeting19-22/jan/2015

Task 4.3 available systemsAt BOKU universitySetup of BOKU HSI system Zeutecsystem withupdatedXeneth Camera(Xenics LuxNIRInGaAs camera: 320*256 matrix, 12 bits, linear-table gear ISEL).

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Task 4.3 available systems

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Task 4.3 available softwareARGUS data aquisition softwareF. Firtha, CuBrowser hyperspectral data processingalgorithm ftp://fizika2.kee.hu/ffirtha/Argus-CuBrowser.pdf, (2012).

Cubrowser data browsing and pre-processing software F. Firtha, Argus hyperspectral acquisition software, ftp://fizika2.kee.hu/ffirtha/Argus-CuBrowser.pdf, (2010)

CAMO: Unscrambler

Eigenvector: PLS_toolbox, MIA, Model_exporter

BRUKER: OPUS

Task 4.3 First results

First results dry, wetted, fungi, normal wood

Project meeting19-22/jan/2015

Normal

Task 4.3 CUBE wavelength scroll

Project meeting19-22/jan/2015

Task 4.3 First results summary Fungi and/or structural abnormalities could be clearly identified on the

dry and wet wood The influence of wood surface roughness was negligible HSI and NIR provided comparable results providing explanatory model

Project meeting19-22/jan/2015

@ IASIM Conference 3.-5.December 2014

Task 4.3 training & classification

GELADI, P., SETHSON, B., NYSTRM, J., LILLHONGA, T., LESTANDER, T. & BURGER, J., 2004. Chemometrics in spectroscopy: Part 2. Examples. Spectrochimica Acta Part B: Atomic Spectroscopy, 59 (9): 1347-1357.

Project meeting19-22/jan/2015

WT 4.3 WorkflowSampling of wood logs

Austria, BOKUSampling of wood logs

Italy, CNR Ivalsa

Hyperspectral imaging BOKUNIR measurements CNR

NIR measurements BOKU

Sample exchange? Sample storage? (20C, 60 % air mositure)?

QuestionsCoverage of deficits?Number of samples totalNumber of samples along the stem of one tree Number of samples per deficitNumber of tree species

Data exchange?NIR data (WT. 4.2)existing data from J. Burger measurements

Interferences?Oil, soil, water, surface roughness, lightning

Prediction of quality by multivariate model

Index of log/biomass quality

Training on known deficits and interferences statistical model

evaluation

Fiel

dL

abor

ator

yFi

eld

Calibration transfer to sensors used in the field? Rugged field conditions?

Position of sensor?Type of sensor?Multispectral with filters?Hyperspectral sensors?

Online processing of data

Combination with other data

Judgement of log quality

Quality communication

Action selection

Model integration?Task 4.6 and D4.12 Implementation and calibration of predicition models for log/biomass quality classes and report on validation procedure (CNR)

Field application and integrated system?WP 5 (Forest information system), WP6 (System integration), WP7 (Pilot of SLOPE demonstrator) -time loss?

HSI sensors-latest developments

IMEC (Belgium) sensor combined with camera Imec has developed a process for depositing

hyperspectral filters directly on top of CMOS image sensors

VISNX (http://www.visnx.com/) built first consumer ready made mini HSI system

wedge design per pixel design area design

Project meeting19-22/jan/2015

(Precision farming)

HSI sensors-latest developments

BaySpec, Inc., San Jose, CA Push-broom (OCITM-U-1000) - true push-broom fast

hyperspectral imaging, simply by using your hand to move the imager or sample (600-1000 nm, 100 spectral bands).

Snapshot (OCITM-U-2000) hyperspectral cube data can be captured at video or higher rates (600-1000 nm, 20 spectral bands).

Project meeting19-22/jan/2015

HSI sensors-latest developments

EVK Graz Austria Fully integrated systems for harsh conditions

with algorithms on board

Project meeting19-22/jan/2015

Project meeting 19-22/jan/2015

HSI processor head and sensor

MIETTINEN, M., KULOVESI, J., KALMARI, J. & VISALA, A. 2010. New Measurement Concept for Forest Harvester Head. In: HOWARD, A., IAGNEMMA, K. & KELLY, A. (eds.) Field and Service Robotics. Springer Berlin Heidelberg.

Mller, 2011

Task 4.3 & WP 8 Hyperspectral Imaging Workshop Tulln 20.3.2015

Talks and topics Hyperspectral imaging general introduction (Rudolf

Kessler, DE) Hyperspectral imaging of food (Ferenc Firtha, HU) Hyperspectral imaging of biomaterials in agriculture

and other fields (Philippe Vermeulen, B) Hyperspectral imaging of wood (Ingunn Burud, NO) Integrated HSI solutions (EVK, AT) Chemometrics course (Eigenvector, USA) Workshop is dedicated to the SLOPE challenges &

questions with regard to hyperspectral imaging

Project meeting19-22/jan/2015

Thank you for your attention

ANDREAS ZITEKKATHARINA BHMandreas.zitek@boku.ac.atkatharina.boehm@boku.ac.at

Phone: +43 676 780 65 15Fax: +43 1 47654 6029

University ofNatural Resources and Life Sciences, Vienna

Department of Material Sciences and Process Engineering

BOKU - University of Natural Resourcesand Life Sciences, Vienna (BOKU-UFT),Dept. of Chemistry, Division ofAnalytical Chemistry, Konrad-Lorenz-Strae 24, 3430 Tulln, AUSTRIA

https://viris.boku.ac.at/WShyperspectral2015/WShyperspectral2015/HOME.html

Project meeting19-22/jan/2015

mailto:andreas.zitek@boku.ac.atmailto:andreas.zitek@boku.ac.athttps://viris.boku.ac.at/WShyperspectral2015/WShyperspectral2015/HOME.html

TASK 4.4Data mining and model integration of log/biomass quality indicators from

stress-wave (SW) measurements, for the determination of the

SW quality index

Work Package 4: Multi-sensor model-based quality of mountain forest

production

Technical Meeting 19-21 Jan 15

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: CNRTask 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

Technical Meeting 19-21 Jan 15

WP4: T 4.4 Deliverables

D4.05) Establishing acoustic-based measurement protocol: This deliverable contains a report and protocol for the acoustic-based measurement procedureStarting 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

Technical Meeting 19-21 Jan 15

Preliminary tests

Testing protocol

Lab scanner tests

ValidationMeasurements

Prediction models

Quality index

WP4: T 4.4 Schedule

Technical Meeting 19-21 Jan 15

D: 4.5 Establishing acoustic-based measurement protocol

1 Introduction

2 Determination of log quality indicators from stress-wave (SW)

3 Stress-wave data acquisition and analysis

3 Protocol of acoustic measurement within SLOPE for the determination of the quality

indicators

4 Determination of SW quality index

6 Test plan for on-line SW measurement in the processor head

Table of content

22.12.14: First draft submitted16:01.15: Revised version submitted

Technical Meeting 19-21 Jan 15

D: 4.5 Establishing acoustic-based measurement protocol

1.1 Application of stress wave-based techniques in forestry and wood characterization

1.2 Material-dependent factors affecting acoustic measurements1.2.1 Anisotropy and heterogeneity1.2.2 Moisture content1.2.3 Temperature

1.3 Methodology-dependent factors affecting acoustic measurements1.3.1 Frequency1.3.2 Transmission modes1.3.3 Coupling

TOC: chapter 1

Technical Meeting 19-21 Jan 15

D: 4.5 Establishing acoustic-based measurement protocol TOC: chapter 2

2 Determination of log quality indicators from stress-wave (SW)

2.1 Defectiveness indicators2.1.1 Slope of grain2.1.2 Compression wood2.1.3 Knottiness

2.2 Decay indicators2.2.1 Insect decay2.2.2 Rot

2.3 Density/stiffness indicator

Technical Meeting 19-21 Jan 15

D: 4.5 Establishing acoustic-based measurement protocol TOC: chapter 3

3 Stress-wave data acquisition and analysis

3.1 Test setup3.2 Signal processing and data mining

Technical Meeting 19-21 Jan 15

D: 4.5 Establishing acoustic-based measurement protocol TOC: chapter 4

4 Protocol of acoustic measurement within SLOPE for the determination of the qualityindicators

4.1 Preliminary analysis: SW measurement on standing trees4.2 Preliminary analysis: SW measurement on trees after felling4.3 SW analysis on de-branched logs4.4 Measurement of visible defects4.5 Direct measurement of wood material properties correlated with SW data

Technical Meeting 19-21 Jan 15

D: 4.5 Establishing acoustic-based measurement protocol

v

Technical Meeting 19-21 Jan 15

D: 4.5 Establishing acoustic-based measurement protocol TOC: chapter 5

5 Determination of SW quality index

5.1 Stress-wave velocity conversion models5.1.1 Stress wave and relation with measurement position in the stem5.1.2 Stress wave and relation with log diameter5.1.3 Stress wave and relation with density5.1.4 Stress wave and relation with moisture content5.1.5 Stress wave and relation with mechanical properties

5.2 Incorporation of parameters from other types of measurements

Technical Meeting 19-21 Jan 15

D: 4.5 Establishing acoustic-based measurement protocol TOC: chapter 6

Test plan for on-line SW measurement in the processor head

Features and functionality to testTypes of testing

Functional testingData testingUsability testingPerformance testingQuality testingInterface testingRegression testingOther testing

To be developed in the frame of WP3 T3.04

CONTRIBUTION OF COMPOLAB IS NEEDED TO FINALIZE THIS CHAPTER!

Technical Meeting 19-21 Jan 15

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

Technical Meeting 19-21 Jan 15

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

Accelerometer #1

Accelerometer #2

Laser displacementsensor

SW generator

TOF

Resonance method

Localcharacterization

Globalcharacterization

Technical Meeting 19-21 Jan 15

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

Technical Meeting 19-21 Jan 15

TASK 4.5Evaluation of cutting process (CP) for the

determination of log/biomass CP quality index

Work Package 4: Multi-sensor model-based quality control of mountain

forest production

Technical Meeting 19-21 Jan 15

Task 4.5: cutting process quality indexObjectives

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

Technical Meeting 19-21 Jan 15

Task 4.5: Cutting Process (CP) for the determination of log/biomass CP quality index

Task Leader: CNRTask Partecipants: Compolab

Starting : October 2014Ending: November2015Estimated person-month = 4.00 (CNR) + 2.00 (Compolab) (to be confirmed)

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

Technical Meeting 19-21 Jan 15

Task 4.5: cutting process quality indexDeliverables

D.4.06 Establishing cutting power measurement protocolReport: This deliverable will contain a report and recommended protocol for collection of data chainsaw and delimbing cutting process.

Delivery Date: January 2015 (M.13)

D.4.11 Estimation of log quality by cutting power analysisPrototype: Numerical procedure for determination of CP quality index on the base of cutting processes monitoring

Delivery Date: September 2015 (M.21)

Technical Meeting 19-21 Jan 15

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 quality indexCNR:

T4.5 cutting power quality

D04.11

D04.06

CNR

CNR

Determine optimal set-up for the measurement of cutting forces on the processor headCNR:

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 CPCNR:

Technical Meeting 19-21 Jan 15

Task 4.5: cutting process quality indexTiming

Evaluation of cutting process (CP) for the determination of log/biomass CP quality index1 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 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.5D.4.06

D.4.11finalize concept

design/adopt to the processortest electronic system

assemble hardwaretest hardware + software

calibrate systemdevelop algorithm for CP Q_index

integrate CP quality index with quality grading/optymization (T4.6) D.4.12

D.4.06 Establishing cutting power measurement protocolD.4.11 Estimation of log quality by cutting power analysis D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure

Technical Meeting 19-21 Jan 15

Task 4.5: cutting process quality indexPrinciples

The indicators of cutting forces: energy demand hydraulic pressure in the saw feed piston power consumption

will be collected on-line and regressed to the known log characteristics.

Sensors to be tested on the lab scanner:Electrical power consumption of feedElectrical power consumption of chain sawTensionmeters for deformations due to forcesLoad cells directly measuring cutting forcesOptional: Acoustic Emission sensorOptional: microphone array

Technical Meeting 19-21 Jan 15

Task 4.5: cutting process quality indexDelimbing system

Schematic of the de-branching system; cutting knives and hydraulic actuator

Technical Meeting 19-21 Jan 15

Task 4.5: cutting process quality indexChainsaw

the scanning bar #1 and the chain saw in the working positions

Technical Meeting 19-21 Jan 15

Task 4.5: cutting process quality indexcontrol system

CRio

cutting forcesaw push force

feed force

Technical Meeting 19-21 Jan 15

Task 4.5: cutting process quality indexComments

The working principles of the selected processor head (upscaledARBRO 1000) allows direct measurement of the cutting/feed force as related to (just) the cutting-out branches.

The output of this task provide a quality map of log for grading (and assisting operator in cutting decision?)

Technical Meeting 19-21 Jan 15

Task 4.5: cutting process quality indexChallengesNo prototype developed due to delays; lab scanner under construction.What sensors are appropriate for measuring cutting forces in processor head?

load cell? tensometer? oil pressure? electrical current? microphone? AE sensor?

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?

Technical Meeting 19-21 Jan 15

Thank you very much

Technical Meeting 19-21 Jan 15

TASK 4.6Implementation of the log/biomass grading

system

Work Package 4: Multi-sensor model-based quality control of mountain

forest production

Technical Meeting 19-21 Jan 15

Task 4.6: Implementation of the log/biomass grading system

Task Leader: CNRTask Participants: GRAPHITECH, COMPOLAB ,MHG, BOKU, GRE, TRE

Starting : June 2014Ending: July 2016Estimated 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 gradingTRE, GRE, COMPOLAB: incorporate material parameters from the multisource data extracted along the harvesting chainGRAPHITECH: integration with the classification rules for commercial assortments, linkage with the database of market prices for woody commoditiesMHG: 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

Technical Meeting 19-21 Jan 15

Task 4.6: Implementation of the grading system

Deliverables

D.4.01 Existing grading rules for log/biomassReport: This deliverable will contain a report on existing log/biomass grading criteria and criteria gap analyses

Delivery Date: October 2014 (M.10)

D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedurePrototype: 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: March 2016 (M.27)

Technical Meeting 19-21 Jan 15

Task 4.6: Implementation of the grading system Timing

Implementation of the log/biomass grading system1 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 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.6D.4.01

D.4.12surveys

literature researchtest quality measuring systems

develop software for integration of quality indexestest software

calibrate systemvalidate the algorithm/system

Technical Meeting 19-21 Jan 15

Deliverable 4.01

Introduction and purpose of the report General overview on the recent wood market in relation to SLOPE Wood market within and beside legal norms and regulation Quality of wood from perspectives of various players Potential advantages of wood from mountain areas List of legal norms, standards and regulations in relation to log grading

Global level European Union level Specific countries

Specific regulation in various wood industries

Technical Meeting 19-21 Jan 15

Currently used logs grading practice

Currently used log grading practices Procedure for estimation of the logs geometrical characteristics and volume Visual grading procedures Machine grading systems of logs Detailed criteria of Norway spruce (Picea abies) quality sorting according to

EN 1927-1:2008 List of wood/log defects: knots, resin pocket, twist (or spiral grain), eccentric

pith, compression wood, sweep, taper, shakes, checks and splits, insect or worm holes, dote, rot, stain

Round wood quality classes according to EN 1927-1:2008 Criteria of classification

Technical Meeting 19-21 Jan 15

Wood defects and possibilities of their detection/identification (focus on SLOPE sensors)

Sensor type

Multispectral cameras for remote sensing (satellite)

Multispectral cameras for remote sensing (UAV)

3D laser scanner and cloud of points

Near Infrared spectrometer (laboratory)

Near Infrared spectrometer (in-field)

Hyperspectral imaging VIS

Hyperspectral imaging NIR

Ultrasound sensor

Free vibrations meter

Cutting forces meters (de-branching)

Acoustic emission sensor

Cutting resistance of cross cut sensor

Vision CCD camera on side of log

3D camera on side of log

Log-geometry sensors (diameter f(length))

Condition of forest area

Health condition of tree

?

?

Foliar index

Crow

n damage

? ?

Tree species recognition

? ?

Branch index

?

?

Macro properties of the forest area or the whole tree

knots

?

?

?

resin pocket

twist

?

?

?

eccentric pith

?

compression w

ood

?

?

?

sweep

?

taper

?

shakes

?

?

insects

?

?

dote

? ?

rot

?

stain

?

Log defects according to EN 1927-1:2008

lignin

?

cellulose

?

hem

icellulose

?

extractives

?

microfibryl angle

?

?

calorific value

?

heartwood/sapw

ood

density

?

?

? ?

mechanical properties

?

m

oisture content

?

? ?

?

provenance

?

?

w

ood tracking

?

? ?

bottom-end diam

eter

top-end diameter

external shape of log

log diameter w

ithout bark

log volume

Other wood properties/characteristics

resonance w

ood

? ?

?

Suitability for detection of resources

for niche products

Technical Meeting 19-21 Jan 15

Missing contributions to D04.01

1. Grading rules from Austria2. Final discussion with TreeMetrics about strategy for automatic

calssifications3. Final conclussions

Foreseen deadline for conclussion: January 31st

Technical Meeting 19-21 Jan 15

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

Technical Meeting 19-21 Jan 15

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)

Technical Meeting 19-21 Jan 15

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

Technical Meeting 19-21 Jan 15

Task 4.6: Implementation of the grading system

The concept (diagram)#1Measure 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

Technical Meeting 19-21 Jan 15

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

Technical Meeting 19-21 Jan 15

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

Technical Meeting 19-21 Jan 15

Task 4.6: Implementation of the grading system

The concept (data flow & hardware)

NI CompactRio master

Database

NI CompactRio client Wifi (in field)

FRID

wei

ght

fuel

???

Wifi (home)

Wifi (home)

HDor

GPRMS

Black box

CP NIR HI SW

cam

era

kine

ct

Wifi (in field)

Wifi (home)

Wifi (home)

Technical Meeting 19-21 Jan 15

Task 4.6: Implementation of the grading system

The real world actions: lab scanner

Technical Meeting 19-21 Jan 15

Task 4.6: Implementation of the grading system

The real world actions: proc