1st Technical Meeting - WP4
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Transcript of 1st Technical Meeting - WP4
Work Package 4: Multi-sensor model-based quality
control of mountain forest production
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Before starting…1. The forest in mountains is peculiar, and very different than such
of flat lands!!!2. Trees in mountains are (mostly) BIG…3. Big/old tree may be or superior quality, or “fuel wood”4. Trees from mountains might be of really high value5. We do support “PROPER LOG FOR PROPER USE”6. The quality of wood/log/tree is an issue!!!!!7. The quality of wood is not only external dimensions, taper and
diameter…
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Wood might not be perfect…
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Wood from mountains might be priceless…
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
The goals of this WP are:• to develop an automated and real-time grading (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
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Fine-grained timeline (all tasks):
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
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Interim delivery stages (with dates):D.4.01 R: Existing grading rules for log/biomass (December 2014)D.4.02 R: On-field survey data for tree characterization (March 2015)D.4.03 R: Establishing NIR measurement protocol (April 2015)D.4.04 R: Establishing hyperspectral imaging measurement protocol (May 2015)D.4.05 R: Establishing acoustic-based measurement protocol (June 2015)D.4.06 R: Establishing cutting power measurement protocol (July 2015)D.4.07 P: Estimation of log/biomass quality by external tree shape analysis (July 2015)D.4.08 P: Estimation of log/biomass quality by NIR (August 2015)D.4.09 P: Estimation of log quality by hyperspectral imaging (September 2015)D.4.10 P: Estimation of log quality by acoustic methods (October 2015)D.4.11 P: Estimation of log quality by cutting power analysis (November 2015)D.4.12 P: Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure (July 2016)
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Task 2.34.1.on-field forest survey
GPSPC/PAD3D scanner
3D vision
Tasks 3.14.2-4.3Mark treeConfirm route of cable crane
GPSPC/PADRFID TAGRFID reader
Tasks 3.24.4
Tree felling
Database
NIR QIH QI
RFID reader
RFID TAG(if cross cut)
Portable NIRHyperspectral
AccellerometersOscilloscope
SW QI
Tasks 3.3
Cable crane
Techno carriageGPRSRFID readerWIFISkyline launcherLoad sensorIntelligent chookersGPSPC/PADData loggerBlack box access
Control systemM/M interface
Tasks 3.44.2-4.3-4.4-4.5-4.6Processorde-brunch, cut to length, measures, mark
Load cell for cutting forceCutting feed sensorFeed force sensorDiameter digital caliperLengthRFID readerRFID TAGPC control comp.GPRS/WIFI
HyperspectralNIR scannerKinect® (or similar 3D vision)Microphone/accellerometer
Data loggerBlack box accessCode Printer
Control systemM/M interfaceID backupDatabase
NIR QI + H QI + SW QI + CF QI
Tasks 3.5
Truck
RFID tags are only used for identifying trees/logs along the supply chain, not to store information.Material parameters from sensors are stored in the database
GPSGPRSRFID antennaBUSCANLoad cell
Logistic Software
ID backup
ID backup
Weight, time
Quality class
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Risks and mitigating actions:
Significant delay related to changes in the consortium (lack of the practical expertise of the processor head engineers); technical meetings, new partners/collaborators
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
Thank you very much
TreeMetrics
“3D Quality Index”
Quality Index
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
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16cm
7cm7cmPulp
7cmPulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
• Taper Variation• Straightness• Branching• Rot etc.
The Products: General Values
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cmPulp
7cmPulpPulp = €20 per M3
Large Sawlog = €60 per M3
Small Sawlog = €40 per M3
The Problem - “The Collision of Interests”
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cmPulp
7cmPulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
Maximise Value
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cmPulp
7cmPulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
Maximise Value: Sawlog Lengths
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cmPulp
7cmPulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
3.7mOption 1
Maximise Value: Sawlog Lengths
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cmPulp
7cmPulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
3.7mOption 1
Maximise Value: Sawlog Lengths
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cmPulp
7cmPulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.3mOption 2
Maximise Value: Sawlog Lengths
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cmPulp
7cmPulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.3mOption 2
Maximise Value: Sawlog Lengths
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cmPulp
7cmPulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.9mOption 3
Maximise Value: Sawlog Lengths
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cmPulp
7cmPulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.9mOption 3
Harvester Optimisation
Log Quality: Straightness (Sweep), Taper, Branching ,Rot,
Our Offering
Forest Mapper - First In The World – Online Forest Mapping & Analysis - Data Management System
Forest Mapper: Automated net area calculation, stratification and Location for ground sample plots to be collected
Sample Plots
Net Area
Stratification
(Inventory Planning)
Terrestrial Laser Scanning Forest Measurement System(AutoStem Forest)
Automated 3D Forest Measurement System
Trusted and Independent Data
Data Mining, Model Integration: e.g. Online Data, Harvest Planning
Harvest Planning: Cutting Production Scenarios
Forest Warehouse- Online Forest Valuation & Harvest Planning System
Latest Development
• Online Market Place• 15,000 forest owners• Irish Farmers Association
Task 4.2
Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass
quality index in mountain forestsTask leader: Anna Sandak (CNR)
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
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
4.2 Timing
Kick-off Meeting 8-9/jan/2014
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
Electromagnetic spectrum
Kick-off Meeting 8-9/jan/2014
The study of the interactions between electromagnetic radiation (energy, light) and matter
Spectrofotometers
laboratory
in-field
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
the scanning bar #1 with NIR sensor
4.2 sensor position in the intelligent processor head
CRio
NIR spectra (USB)
4.2 control system
• 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.
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
Thank you very much
WP4: Multi-sensor model-based quality control of mountain forest production
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”
Task leader: Mariapaola Riggio (CNR)
WP4: T 4.4 Role of partners involved
Kick-off Meeting 8-9/jan/2014
Task Leader: CNRTask Participants: Kesla, Greifenberg
CNR: will coordinate all the participants to this task and in particular will define the testing procedures and develop the prediction models for characterization of wood along the harvesting chain, using acoustic measurements
Greifenberg: will provide expertise and assistance for the collection for in field measurements of acoustic data on the felled/delimbed stems
Kesla: will provide expertise, in field assistance and product components (mainly sensors) to be tested for the harvester head integration, for in-field acoustic measurements on the logs
WP4: T 4.4 Deliverables
Kick-off Meeting 8-9/jan/2014
D4.05) Establishing acoustic-based measurement protocol: This deliverable will contain 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
WP4: T 4.4 Timing
Kick-off Meeting 8-9/jan/2014
Data mining and model integration of log/biomass quality indicators from stress-wave (SW) measurements, for the determination of the “ 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
WP4: T 4.4 A premise
Kick-off Meeting 8-9/jan/2014
Stress-waves
Parameters
SW velocity or time-of-flight (SW-TOF)
Acoustic impendance
Damping
Resonance frequency
WP4: T 4.4 Objectives
Kick-off Meeting 8-9/jan/2014
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.
WP4: T 4.4 Objectives
Kick-off Meeting 8-9/jan/2014
Task 4.4 does not aim at defining a procedure for the estimation of specific properties (e.g. dynamic moduli, etc.) of the harvested material.
The aim of Task 4.4 is to define a procedure for determination of “SW quality index” that will support final grading of logs.“SW quality index” will be used in combination with the other implemented “quality indices” developed from the multisource data extracted along the harvesting chain.
WP4: T 4.4 Interactions
Kick-off Meeting 8-9/jan/2014
WP4: interaction with all other tasks
tasks 4.1, 4.2, 4.3: Information aboutmaterial characteristics (such as diameter, length, moisture content and density), estimated through the other non-destructive tests implemented in WP4 and propagated along the harvesting chain, will be incorporated into predictionmodels.
task 4.6: “SW quality index” will be used in combination with the other implemented “quality indices” developed from the multisource data extracted along the harvesting chain. SW quality index
Density, MC, …
geometricaldata
TOF, resonancefrequency
Kick-off Meeting 8-9/jan/2014
WP4: T 4.4 manual measurement of the log mechanical properties
Task 4.4 will start from recent developments of acoustic-based diagnostics for forest resource segregation.
the scanning bar #1 with free vibrations sensor
WP4: T 4.4 sensor position in the intelligent processor head
Kick-off Meeting 8-9/jan/2014
WP4: T 4.4
For many years, the sawmilling industry has utilized acoustic technology for lumber
assessment and devices such as the in- line commercialized stress-wave grade sorter
METRIGUARD®VISCAN®
Kick-off Meeting 8-9/jan/2014
WP4: T 4.4
Recently, in New Zealand prototypes have beendeveloped integrating acoustic (resonance) measurement devices with process heads
The stress wave velocity measuring system for determination of the mechanical properties of the log; ultrasound transducer and ultrasound receiver
WP4: T 4.4 sensor position in the intelligent processor head (2)
CRio
SW waveform
4.2 control system
ultrasound excitation
ultrasound response
WP4: T4.4 Activities
Kick-off Meeting 8-9/jan/2014
Available acoustic measurement procedures willbe tested in the field:
on the delimbed stem: CNR – Greifenbergon the cut logs: CNR – KESLA
Additionally measurements will be taken by operatorsalong the whole supply chain
Acquisition time of measurement, influence of obstacles and factors limiting instrument performance, reliability/quality of recorded signals and overallvalidation of measurement procedures will be providedfor each field test.
Kick-off Meeting 8-9/jan/2014
WP4: T4.4 Challenges
Cope with the factors that might influence acoustic data:
• tree structure : Anisotropy, local variability, heterogeneity, presence/absence of branches, bark, etc.
• MC dependent on growing season (sap flow variation), time of measurement from the felling time, weather and environmental conditions, etc
• Type of sensors/coupling/acquisition setup
• Embodiment of acoustic instruments on a mechanized harvester head
Provide reliable data to be coupled with acoustic data:
i.e. Density, geometrical data, defects, MC, etc.
Thank you very much
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
Evaluation of cutting process (CP) for the determination of log/biomass “CP quality index”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
T4.5D.4.06
D.4.11finalize concept
design/adopr to the processortest electronic system
assemble hardwaretest hardware + software
callibrate 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
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
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=M3Pm9B5xXaI (ARBRO)
http://www.youtube.com/watch?v=XzaPvftspg0 (KESLA)
Task 4.5: cutting process quality indexDelimbing system
Schematic of the de-branching system; cutting knives and hydraulic actuator
Task 4.5: cutting process quality indexChainsaw
the scanning bar #1 and the chain saw in the working positions
Task 4.5: cutting process quality indexcontrol system
CRio
cutting forcesaw “push” force
feed force
Task 4.5: cutting process quality indexComments
The working principles of the selected processor head (ARBRO 1000) allows direct measurement of the cutting/feed force as related to (just) the cutting-out branches.
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)
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
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
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
Task 4.6: Implementation of the grading system
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)
color cameras for color mapping of log’s sides
Task 4.6: Implementation of the grading system
Other avaliable info (1)
multisensor system for 3D/color mapping of logs
Task 4.6: Implementation of the grading system
Other avaliable info (2)
Task 4.6: Implementation of the grading system
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.
Task 4.6: Implementation of the grading system
Challenges
What sensors set is optimal (provide usable/reliable information)?
How to merge various types of indexes/properties?
Can the novel system be accepted by “conservative” forest (and wood transformation) industry?
How the SLOPE quality grading will be related to established classes?
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)
Thank you very much