Kick-Off Meeting - WP4

Project SLOPE1

WP 4 – Multi-sensor model-based qualitycontrol of mountain forest production

Work Package 4: Multi-sensor model-based

quality control of mountain forest production

Some thoughts after the first day of kick-off meeting:

1. Complements for all partners for fascinating presentations, unique know-how and enthusiasm.

2. The forest in mountains is peculiar, and very different than such of flat lands!!!

3. Trees in mountains are (mostly) BIG…4. Big/old tree may be or superior quality, or “fuel wood”5. Trees from mountains might be of really high value6. We do support with our heart “PROPER LOG FOR PROPER USE”7. The quality of wood/log/tree is an issue!!!!!8. But, the quality of wood is not only external dimentions, taper

and diameter…



Wood might not be perfect…



Wood from mountains might be priceless…



The goals of this WP are:• to develop an automated and real-time grading system for the forest production, in order to improve log/biomass segregation and to help develop a more efficient supply chain of mountain forest products• to design software solutions for continuous update the pre-harvest inventory procedures in the mountain areas • to provide data to refine stand growth and yield models for long-term silvicultural management



Fine-grained timeline:

4TRE 4.1CNR 4.2

BOKU 4.3CNR 4.4CNR 4.5CNR 4.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36



Interim delivery stages (with dates):D.4.01 R: Existing grading rules for log/biomass (December 2014)D.4.02 R: On-field survey data for tree characterization (March 2015)D.4.03 R: Establishing NIR measurement protocol (April 2015)D.4.04 R: Establishing hyperspectral imaging measurement protocol (May 2015)D.4.05 R: Establishing acoustic-based measurement protocol (June 2015)D.4.06 R: Establishing cutting power measurement protocol (July 2015)D.4.07 P: Estimation of log/biomass quality by external tree shape analysis (July 2015)D.4.08 P: Estimation of log/biomass quality by NIR (August 2015)D.4.09 P: Estimation of log quality by hyperspectral imaging (September 2015)D.4.10 P: Estimation of log quality by acoustic methods (October 2015)D.4.11 P: Estimation of log quality by cutting power analysis (November 2015)D.4.12 P: Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure (July 2016)



Partners’ role and contributions:

Will be explained in presentations of tasks…



Dependences between activities:

•T1.2 (and your comments) vital for proper initiation of work…

•WP4 is strictly related to WP3

•WP4 provides data to WP5



Task 2.34.1.on-field forest survey

GPSPC/PAD3D scanner

3D vision

Tasks 3.14.2-4.3Mark treeConfirm route of cable crane

GPSPC/PADRFID TAGRFID reader

Tasks 3.24.4

Tree felling

Database

NIR QIH QI

RFID reader

RFID TAG(if cross cut)

Portable NIRHyperspectral

AccellerometersOscilloscope

SW QI

Tasks 3.3

Cable crane

Techno carriageGPRSRFID readerWIFISkyline launcherLoad sensorIntelligent chookersGPSPC/PADData loggerBlack box access

Control systemM/M interface

Tasks 3.44.2-4.3-4.4-4.5-4.6Processorde-brunch, cut to length, measures, mark

Load cell for cutting forceCutting feed sensorFeed force sensorDiameter digital caliperLengthRFID readerRFID TAGPC control comp.GPRS/WIFI

HyperspectralNIR scannerKinect ® (or similar 3D vision)Microphone/accellerometer

Data loggerBlack box accessCode Printer

Control systemM/M interfaceID backupDatabase

NIR QI + H QI + SW QI + CF QI

Tasks 3.5

Truck

RFID tags are only used for identifying trees/logs along the supply chain, not to store information.Material parameters from sensors are stored in the database

GPSGPRSRFID antennaBUSCANLoad cell

Logistic Software

ID backup

ID backup

Weight, time

Quality class



Risks and mitigating actions:

To keep focus on practical applications and not pure (fascinating for us) research; 2-monts progress reporting, contributions/comments of SLOPE partners

Properly define real user expectations; contribution of the development of WP1, discussions with stake holders, foresters, users of forest resources

Technologies provided will not be appreciated by “conservative” forest users; demonstrate financial (and other) SLOPE advantages

Difficulties with integration of some sensors with forest machinery; careful planning, collaboration with SLOPE engineers

Thank you very much

TreeMetrics

“PROVIDE MORE END PRODUCT FROM LESS TREES”

WP 4.1: Data Mining and Model Integration of Stand Quality Indicators

• Stem Taper Variation• Stem Quality Variation

– Straightness – Branching– Internal wood quality

• Stem Bucking Simulation Systems

Log Quality: Straightness (Sweep), Taper, Branching ,Rot,

New Opportunity UAV data

Terrestrial Laser Scanning Forest Measurement System(AutoStem Forest)

Automated 3D Forest Measurement System

New Stand Analytics – Log distribution

Harvest Modelling

• ‘Cutting to Value’ (Value Optimisation)

• ‘Cutting to Demand’ (Keep the market satisfied)– Manage the trade off’s– Combinatorial problem– Constraint Modelling

The Problems

• Productive Area

• Stratification

• Stocking

• Stem Taper Variation

• Stem Quality Variation

Products & Value

The Products

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7cm7cmPulp

7cmPulpPulp M3?

Large Sawlog M3?

Small Sawlog M3?

• Taper Variation• Straightness• Branching• Rot etc.

The Products: General Values

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7cmPulpPulp = €20 per M3

Large Sawlog = €60 per M3

Small Sawlog = €40 per M3

The Problem - “The Collision of Interests”

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Maximise Value

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Small Sawlog M3?

Maximise Value: Sawlog Lengths

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Large Sawlog M3?

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3.7mOption 1


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Large Sawlog M3?

Small Sawlog M3?

3.7mOption 1


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Large Sawlog M3?

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4.3mOption 2


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Large Sawlog M3?

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4.3mOption 2


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Large Sawlog M3?

Small Sawlog M3?

4.9mOption 3


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Large Sawlog M3?

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4.9mOption 3

Harvester Optimisation

Our Offering

Better Management

Targets

Incentives

Monitoring

Competitive Advantage

“the stronger the LINKAGES between the primary and secondary producers the greater the source of competitive advantage”

Michael Porter, Harvard Business School

Summary

• [email protected]

• www.treemetrics.com

mailto:[email protected]

Task 4.2

Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass

quality index in mountain forests

Task 4.2: Partners involvement

Task Leader: CNRTask Partecipants: KESLA, BOKU, FLY, GRE

CNR: Project leader, •will coordinate all the partecipants of this task•will evaluate the usability of NIR spectroscopy for characterization of bio-resources along the harvesting chain•will 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

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

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

Electromagnetic spectrum

Kick-off Meeting 8-9/jan/2014

The study of the interactions between electromagnetic radiation (energy, light) and matter

Source of spectra

TwistingWaggingRocking

Scissoringasimmetricstretching

simmetricstretching

Spectra represents molecular vibrations of chemical molecules exposed to infrared light.

http://en.wikipedia.org/wiki/Infrared_spectroscopy

http://en.wikipedia.org/wiki/Image:Symmetrical_stretching.gif

http://en.wikipedia.org/wiki/Image:Symmetrical_stretching.gif

http://en.wikipedia.org/wiki/Image:Asymmetrical_stretching.gif

http://en.wikipedia.org/wiki/Image:Asymmetrical_stretching.gif

http://en.wikipedia.org/wiki/Image:Scissoring.gif

http://en.wikipedia.org/wiki/Image:Scissoring.gif

http://en.wikipedia.org/wiki/Image:Modo_rotacao.gif

http://en.wikipedia.org/wiki/Image:Modo_rotacao.gif

http://en.wikipedia.org/wiki/Image:Wagging.gif

http://en.wikipedia.org/wiki/Image:Wagging.gif

http://en.wikipedia.org/wiki/Image:Twisting.gif

http://en.wikipedia.org/wiki/Image:Twisting.gif

NIR technique

No need special sample preparation Non-destructive testing Relatively fast measurement No residues/solvents to waste Possibility for determination of many components

simultaneously High degree of precision and accuracy Direct measurement with very low cost

Overlapping of spectral peaks Needs sophisticated statistics methods for data analysis Moisture sensitive Calibration transfer from lab equipment into field equipment

Spectrofotometers

How it works?

+

calibration (PLS)

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gęstość referencja (g/cm3)gę

stoś

ć es

tym

acja

(g/c

m3 )

r2 = 64,94RMSECV = 0,039RPD = 1,69

density

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loza

est

ymac

ja (%

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r2 = 84,98RMSECV = 0,0638RPD = 2,58

cellulose

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lignina referencja (%)

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lignin

R2 = 0.984

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icte

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ress

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a)

Tensile strength

spectra reference data

Identity test

Compare the unknown spectrum with all reference spectra, the result of comparison between two spectra is the spectral distance called hit quality. The better spectra match the smaller is spectral distance; HQ for

identical spectra is 0

Model sample1HQ1 > treshold1

Model sample3HQ3 > treshold3

Model samplenHQn > tresholdn

Model sample2HQ2 < treshold2

???sample

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

4.2 Activities: Feasibility study and specification of the

measurement protocols for proper NIR data acquisition

• spectra pre-processing, wavelength selection, classification,calibration, validation, external validation (sampling –prediction – verification)

• prediction of the log/biomass intrinsic “quality indicators”(such as moisture content, density, chemical composition,calorific value) (CNR).

• classification models based on the quality indicators will bedeveloped and compared to the classification based on theexpert’s knowledge.

• calibrations transfer between laboratory instruments(already available) and portable ones used in the fieldmeasurements in order to enrich the reliability of theprediction (BOKU).

4.2 Activities: Development and validation of

chemometric models.

4.2 Deliverables

Kick-off Meeting 8-9/jan/2014

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 M16 April 2015

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 M20 August 2015Estimated person Month= 3.45

Development of “provenance models”. The set of spectra collected from selected samples (of known provenance and silvicultural characteristics) along the supply chain will be also processed in order to verify applicability of NIR spectroscopy to traceability of wood (CNR).

4.2 Additional deliverable

Wood provenance & NIRS

2163 trees of Norway spruce from 75 location

in 14 European countries2163 samples measured

x 5 spectra/sample = 10815 spectra

Wood provenance & NIRS

NIR workshop

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

Task 4.5: Cutting Process (CP) for the determination of

log/biomass “CP quality index”

Task Leader: CNRTask Partecipants: Kesla

Starting : October 2014Ending: November2015Estimated person-month = 4.00 (CNR) + 2.00 (Kesla)

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

Kesla : will provide expertise in regard to sensor selection and integration with the processor head + extensive testing of the prototype

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: July 2015 (M.19)

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: November 2015 (M.23)

Task 4.5: cutting process quality indexTiming

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 3611.11.21.31.41.522.12.22.32.42.533.13.23.33.43.53.64

4.14.24.34.44.54.655.15.25.35.45.566.16.26.36.477.17.27.37.488.18.28.38.48.58.68.78.899.19.29.3

Task 4.5: cutting process quality indexObjectives

The goals of this task are:• to develop a novel automatic system for estimation of the cutting resistance of wood processed during harvesting• to use this information for the determination of log/biomass quality index

Task 4.5: cutting process quality indexTheory

The value of cutting forces is related to:

• wood density• cutting conditions• selected mechanical properties of wood (i.e. fracture toughness and shear modulus).

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.

http://www.youtube.com/watch?v=bZoq7PkyO-c

http://www.youtube.com/watch?v=XzaPvftspg0

http://www.youtube.com/watch?v=bZoq7PkyO-c

http://www.youtube.com/watch?v=XzaPvftspg0

Task 4.5: cutting process quality indexChainsaw

Task 4.5: cutting process quality indexDelimbing systems

Task 4.5: cutting process quality indexComments

The average density and mechanical resistance will be a result of the analysis of the chainsaw cutting process.

Estimation of the “CP-branch indicator” will be computed only in the case of delimbing on the processor head. In this case, it will be correlated to the “3D-branch indicator” determined from the 3D stem model of the original standing tree (T4.1).

The information will be forwarded to the server in real-time and will support final grading of logs.

Task 4.5: cutting process quality indexChallenges

What sensors are appropriate for measuring cutting forces in processor head?

load cell? tensometer? oil pressure? electrical current?

How to 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?

Thank you very much

TASK 4.6Implementation of the log/biomass grading

system

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

Task 4.6: Implementation of the log/biomass grading

system

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

Starting : June 2014Ending: July 2016Estimated person-month = 1.50 (GRAPHITECH) + 2.0 (CNR) + 1.00 (Kesla) + 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, KESLA: 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

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: December 2014 (M.12)

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: July 2016 (M.31)


Timing

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 3611.11.21.31.41.522.12.22.32.42.533.13.23.33.43.53.64

4.14.24.34.44.54.655.15.25.35.45.566.16.26.36.477.17.27.37.488.18.28.38.48.58.68.78.899.19.29.3


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 wood from less trees


The concept

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)


Results

Several grading rules are in use in different regions and/or niche products: a systematic database of these rules will be developed for this purpose.

• The performance• Reliability • Repetability• Flexibility

of the grading system will be carefully validated in order to quantify advantages from both economic and technical points of view.at different stages of the value chain.


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?

Thank you very much

Education

Kick-Off Meeting - WP4