Mid-term Review Meeting - WP4
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This document outlines the goals and tasks of Project SLOPE Work Package 4, which aims to develop automated quality control systems for mountain forest production using multi-sensor modeling. The goals are to improve log segregation, support efficient supply chain management, and refine growth models. The work package involves developing quality indices using 3D modeling, near infrared spectroscopy, hyperspectral imaging, stress wave analysis, and measuring cutting power to determine wood quality. Tasks include sensor calibration and optimization, data collection, model development and validation, and integrating results into a grading system.
Mid-term Review Meeting - WP4
- 1. Project SLOPE 1 WP 4 – Multi-sensor model-based quality control of mountain forest production
- 2. Work Package 4: Multi-sensor model-based quality control of mountain forest production The goals of this WP are: • to develop an automated and real-time grading (optimization) system for the forest production, in order to improve log/biomass segregation and to help develop a more efficient supply chain of mountain forest products • to design software solutions for continuous update the pre- harvest inventory procedures in the mountain areas • to provide data to refine stand growth and yield models for long-term silvicultural management
- 3. Work Package 4: work to be done T4.1 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory scale tests for chain saw energy needs CNR: Develop models linking CP in delimbing and quality CNR: Develop models linking CP in chain sawing and quality CNR: Develop report on using CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 4. T4.1: Data mining and model integration of stand quality indicators from on-field survey Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE draft: October 2014 accepted: July 2015 31.05.2015 the resources planned: 9 M/M the resources utilized: PROBLEMS: Not reported
- 5. Work Package 4: work to be done T4.2 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory scale tests for chain saw energy needs CNR: Develop models linking CP in delimbing and quality CNR: Develop models linking CP in chain sawing and quality CNR: Develop report on using CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 6. T4.2: Evaluation of NIRS as a tool for determination of log/biomass quality index D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: the resources planned: 13 M/M the resources utilized: PROBLEMS: Delay in purchasing sensor SOLUTIONS: The sensor already ordered 30.09.2015 ! ! ! ! draft: October 2014 accepted: July 2015 !
- 7. Work Package 4: work to be done T4.3 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory scale tests for chain saw energy needs CNR: Develop models linking CP in delimbing and quality CNR: Develop models linking CP in chain sawing and quality CNR: Develop report on using CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 8. T4.3: Evaluation of hyperspectral imaging for the determination of log/biomass quality index Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 the resources planned: 17 M/M the resources utilized: PROBLEMS: Delay with Deliverable + setting of the lab scanner + in-field sensor selection SOLUTIONS: collaboration with experts + new solutions for HI sensor(s) 31.10.2015 draft: May 2014 accepted: July 2015 ! ! ! ! !
- 9. Work Package 4: work to be done T4.4 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory scale tests for chain saw energy needs CNR: Develop models linking CP in delimbing and quality CNR: Develop models linking CP in chain sawing and quality CNR: Develop report on using CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 10. T4.4: Data mining and model integration of log/biomass quality indicators from stress-wave Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: the resources planned: 5.5 M/M the resources utilized: PROBLEMS: Delay related to the processor head SOLUTIONS: LAB scanner + collaboration with engineers 31.11.2015 draft: December 2014 accepted: July 2015 ! !
- 11. Work Package 4: work to be done T4.5 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory scale tests for chain saw energy needs CNR: Develop models linking CP in delimbing and quality CNR: Develop models linking CP in chain sawing and quality CNR: Develop report on using CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 12. T4.5: Evaluation of cutting process (CP) for the determination of log/biomass CP quality index Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory scale tests for chain saw energy needs CNR: Develop models linking CP in delimbing and quality CNR: Develop models linking CP in chain sawing and quality CNR: Develop report on using CP CNR: the resources planned: 6.0 M/M the resources utilized: PROBLEMS: Delay related to the processor head and final sensor selection/design SOLUTIONS: LAB scanner + collaboration with engineers + list of sensor(s) for purchase ready 31.12.2015 ! ! draft: January 2014 accepted: July 2015
- 13. Work Package 4: work to be done T4.6 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory scale tests for chain saw energy needs CNR: Develop models linking CP in delimbing and quality CNR: Develop models linking CP in chain sawing and quality CNR: Develop report on using CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 14. T4.6: Implementation of the log/biomass grading system Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR: the resources planned: 8.0 M/M the resources utilized: PROBLEMS: Delay related to other tasks – difficulties with implementation SOLUTIONS: LAB scanner + prototype software developed in lab + algorithms ready 31.06.2016 draft: October 2014 accepted: July 2015 ! !
- 15. fulfillment of the project work plan: related deliverables (M17) WP4 M17 task delive rable title type of deliverable lead particip ant due date foreseen or actual delivery date comment T4.1 D4.2 on field survay data for tree characterization report TRE 31.10.2014 31.10.2014 accepted D4.7 estimation of log/biomass quality by external tree shape analysis software tool TRE 31.05.2015 same as planed working ver. T4.2 D4.3 establisghing NIR measurement protocol report CNR 31.10.2014 31.10.2014 accepted D4.8 estimation of log/biomass quality by NIR software tool CNR 30.09.2015 postponed (new DoW) NO T4.3 D4.4 establisghing hyperspectral imaging measurement protocol report BOK 30.11.2014 05.05.2015 accepted D4.9 estimation of log/biomass quality by hyperspectral imaging software tool BOK 31.10.2015 postponed (new DoW) NO T4.4 D4.5 establishing acoustic-based measurement protocol report CNR 31.12.2014 05.05.2015 accepted D4.10 estimation of log/biomass quality by acoustic methods software tool CNR 31.11.2015 postponed (new DoW) NO T4.5 D4.6 establisghing cutting power measurement protocol report CNR 31.01.2015 31.01.2015 accepted D4.11 estimation of log/biomass quality by cutting power analysis software tool CNR 30.12.2015 postponed (new DoW) NO T4.6 D4.1 existing grading rules for log/biomass report CNR 31.10.2014 31.10.2014 accepted D4.12 implementatio and callibration of prediction models for log/biomass quality classes software tool CNR 31.06.2016 postponed (new DoW) NO
- 16. Work Package 4: Multi-sensor model-based quality control of mountain forest production Planning actions for all activities and deliverables to be executed in M18-24: Finalize + close: D04.7, D04.8, D04.9, D04.10, D04.11 Deliver + finalize + close: - Initiate + deliver: D04.12 Finalize purchase of sensors + install sensors Perform field tests with portable instruments Collaborate with WP3 (and others) in hardware development
- 17. 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.
- 18. Work Package 4: Multi-sensor model-based quality control of mountain forest production Risks and mitigating actions: Significant delay related to DoW amandment: •the purchase and set-up of the new processor head was 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 Limited reliability of some sensors when implemented on the forest machinery; careful planning, collaboration with SLOPE (+outside) engineers
- 19. Work Package 4: Multi-sensor model-based quality control of mountain forest production criticalities, recommendations for partners/consortium How about demonstrations? • No sensors available yet (but ordered already) • expected state of WP4 development during the first demo? The communication between partners has been substantially improved, but can be always better!
- 20. MS Kinect MicroNIR Hamamatsu C11708 Hamamatsu C12666 Accelerometers time of flight Mechanical excitator Accelerometers free vibration LDS correction Laser Displacement Sensor AE sensor + amplifier Tensionmeters 1/4 bridge Dynamic load cell Hydraulic pressure sensor Hydraulic flow sensor Absolute encoders Hamamatsu C11351 NI 9234 NI 9223 NI 9235 NI 9220 Port #8 CRio (CompactDaq Win7) SENSORS Port #7 Port #6 Port #5 Port #4 Port #3 Port #2 Port #1 LAN port #2 Industrial PC LAN port #1 Port #6 Port #5 Video output + USB port #4 USB port #3 USB port #2 USB port #1 NI 9403 (Digital I/O) Custom line scan camera Port #8 CRio (real time?) MACHINE CONTROL Port #7 Port #6 Port #5 Port #4 Port #3 Port #2 Port #1 SEA 9744 (GSM + GPS) Joystic(s) RFID reader Hydraulic actuators ??? ??? ??? ??? CMOS camera 3D camera #1 3D camera #2LAN port #5 LAN port #4 LAN port #3 Touch screen T4.2+T4.3T4.4T4.5T4.5T4.4WP3 T4.2+T4.3WP3WP3 NI 9220 Temperatures of oil and air Sensors and electronics (WP3 & WP4)
- 21. Work Package 4: Multi-sensor model-based quality control of mountain forest production Thank you! – Grazie!
- 22. Task 4.01: Data mining and model integration of stand quality indicators from on-field survey for the determination of the tree “3D Quality Index” Task Leader: Treemetrics Partners involved: GRAPHITEC, CNR, FLYBY Deliverable: D4.02 On-field survey data for tree characterization Status: Completed Mid-term Review 2/July/15 T2.03 Timber Products Quality Index
- 23. Task 4.0 Timber Products Quality Index This task includes: •Overview of process for tree 3D model creation •TLS Quality Indicators •Harvest Simulation D4.2 TLS data analysis aims at evaluating the effectiveness/reliability, as quality indicators, of single and combined parameters related to the external characteristics of the standing tree, such as tree height, diameter, stem taper, straightness, sweep and lean, branchiness, branch length, thickness and dimension of the live crown. T2.03Timber Products Quality Index Introduction Mid-term Review 2/July/15
- 24. T2.03Timber Products Quality Index Introduction Mid-term Review 2/July/15
- 25. Index TLS data capture •Automated tree detection •Branches removal •3D tree shape (each 10cm) Mid-term Review 2/July/15
- 26. Automted generation of 3D model of one sampled trees. Profile disks are fitted around cylinders in the point cloud data at every 10cm. -Diameter distribution - Leaning of the tree Upper section of tree is calculated using local taper equations. 3D Models Index TLS data capture Mid-term Review 2/July/15
- 27. Index Tree defects The main stem is the most useful part of a tree for conventional wood products such as roundwood, pulpwood, posts, poles, and lumber. Defects reduce the total volume of usable wood in the tree. The stem defects are potential indicators of the timber quality Mid-term Review 2/July/15
- 28. Index Field Defects measurment Mid-term Review 2/July/15
- 29. Stemfiles are generated that fully support the Standard for "Forestry Data and Communication" (StanForD) standard in a widely accepted file with ".stm" extension. The allows for storing of x,y,z and diameter for each decimetre disk on the stem. The extra information should either be stored in a linked file to the .stm file or a new approach that does not support the StanForD standard can be used. STEM FILE GENERATION Index Stemfile Mid-term Review 2/July/15
- 30. Index Tree defects The stem defects can have different impact in the in the final timber product, depending on how there are cut. Defect affecting a low value log Defect affecting a high value log Treemetrics has developed a system to define and characterize the stems defects and to optimize the cutting of timber logs to minimise losses due to defects. Mid-term Review 2/July/15
- 31. Timber Log Specifications Length: Targeted length of the log. Small End Diameter (Min + Max SED) Large End Diameter (Min + Max LED) Straightness: Maximum deviation Index Log definition Mid-term Review 2/July/15
- 32. Timber Log Quality Specifications Maximum defects specification -Defect grade 1: Stem sections with severe timber defects -Defect grade 2: Stem sections with scar defects, cracks, decay, or similar that prevents to create quality timber logs. - Dead tree: Sanding dead tree that will not be commercialized. Index Log definition Maximum log bow (Straightnes) Defects can affect one section of the tree or the entire tree. Mid-term Review 2/July/15
- 33. 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 Index Log definition Mid-term Review 2/July/15
- 34. Log ID/name LOG1 LOG2 LOG3 LOG4 LOG5 LOG6 Length 4m 4m 5m 2.5m 4.8m 2.5 Min SED 20cm 40cm 40cm 20cm 20cm 40cm Straightness - - - - - - Quality restrictions No Defect No Defect No Defect Defect grade 1 Defect grade 1 Defect grade 2 Slope Log Definitions Index Log definition Mid-term Review 2/July/15
- 35. Index Cutting instrutions A Cutting Instruction is a collection of Log Products, weighted by priority (e.g. value). Treemetrics system needs a defined set of Cutting Instruction in order to run the cutting simulation. The cutting instruction needs to be defined by the user according the industry standards in his/her region. Cutting instruction LOG1 LOG2 LOG3 LOG4 LOG5 LOG6 Example 1 50 100 200 - - - Example 2 50 100 100 20 50 10 Example 3 50 100 300 20 30 10 Mid-term Review 2/July/15
- 36. LOG Weight LOG1 100 LOG2 10 LOG3 50 LOG4 0 Total Assortment 1 Value (m3) 1*0.3 2*0.15 - 0.1 33 Assortment 2 Value (m3) 1*0.3 - 1*0.2 0.2 40 Index Cutting simulation Mid-term Review 2/July/15
- 38. Optimising Waste Logs: waste log has a value of zero Index Stemfile Mid-term Review 2/July/15
- 39. Product ratio Index Quality indexes ∑ = = + = ni i i wv w WR 1 ∑ = = = ni i i n v v PR 1 Waste ratio valuepotentialMaximum valueTotal =TPI Total profitability index KWRPRTPI ⋅−⋅= )1( nn ni i ii vp vp K ⋅ ⋅ = ∑ = =1 Modified Total profitability index Assumes that the that the price of the highest volume is the double that the average price of the others products. )5.0()1( PRWRMTPI +⋅−= Mid-term Review 2/July/15
- 40. Index Quality indexes Quality index (MTPI) Quality class Description 1+ 1 Very high quality 0.5-1 2 High quality 0.3-0.5 3 Regular quality 0-0.3 4 Low quality 0 5 Very low quality Mid-term Review 2/July/15
- 41. Conclusions The main conclusions about the stand quality indicators and harvest simulation are the following: • The MAIN parameters to define the log can be easily measured using the stem 3D model created using TLS data (including straightness). • Additional quality indicators can be measured and applied to the log constraints. • Intelligent cutting instructions can reduce the loss caused by stem defects • It has successfully defined a quality index to apply in slope based on the timber product information. Mid-term Review 2/July/15
- 42. Future work Crosscutting results for Picine complete (to be implemented with FSI within the next weeks) Next analysis: •Montsover (Province of Trento, Italy) –August • Austria Mid-term Review 2/July/15
- 43. TASK 4.2 Evaluation 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)
- 44. Task 4.2: Partners involvement Task Leader: CNR Task Partecipants: 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 Greifenberg and Flyby: will support CNR in order to collect NIR spectra at various stages of the harvesting chain
- 45. • 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
- 46. 4.2 Deliverables Deliverable D.4.03 Establishing NIR measurement protocol evaluating the usability of NIR spectroscopy for characterization of bio-resources along the harvesting chain, providing guidelines for proper collection and analysis of NIR spectra. Delivery Date M10, October 2014 Estimated person Month = 5 Deliverable D.4.08 Estimation of log/biomass quality by NIR Set of chemometric models for characterization of different “quality indicators” by means of NIR and definition of “NIR quality index” Delivery Date M21, Septembe 2015 Estimated Man/Month = 8
- 47. T4.2: Evaluation of NIRS as a tool for determination of log/biomass quality index D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: the resources planned: 13 M/M the resources utilized: PROBLEMS: Delay in purchasing sensor SOLUTIONS: The sensor already ordered 30.09.2015 ! ! ! ! draft: October 2014 accepted: July 2015 !
- 48. 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.
- 50. 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
- 51. Mathematical methods and algorithms suitable for NIR spectroscopic evaluation of log/wood quality in SLOPE scenario Algorithms for pre-processing of spectra •Averaging •Derivative •Smoothing normalization •Baseline correction •Multiplicative Scatter Correction Algorithms for NIR data post-processing and data mining •Cluster Analysis (CA) •Principal Component Analysis (PCA) •Identity Test (IT) •Quick Compare (QC) •Partial Least Squares (PLS)
- 52. – 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
- 53. 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#6compute the log quality class (optimize cross-cut) estimated tree quality forest models update the forest database compare results of wet and dry woods combine all available char- acteristics of the log lab Calibration transfer f(MC, surface_quality) 3D tree quality index hyperspectral HI quality index stress wave SW quality index cutting force CF quality index compute NIR quality index#1
- 54. Detailed procedure related to measurement of NIR along the whole harvesting scenario Forest modeling NIR quality index #1 will be related directly to the health status, stress status and to the productivity capabilities of the tree(s) foreseen for harvest Tree marking Direct 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 tree testing the possibility of collecting sample of wood in a form of the triangular slice being a part of the chock cut-out from the bottom of the log (NIR quality index #3) Processor head NIR 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
- 55. the scanning bar #1 with NIR sensor Sensor position in the intelligent processor head
- 56. CRio NIR spectra (USB) Control system
- 57. Detailed procedure related to measurement of NIR along the whole harvesting scenario Pile of logs The cross section of logs stored in piles is easily accessible for direct measurement. Such measurements will be repeated periodically in order to monitor the quality depreciation and to determine the most optimal scanning frequency. The result of measuring NIR spectra of logs stored in piles will be NIR quality index #5 Laboratory Samples collected in the forest will be measured instantaneously after arrival in the laboratory (at the wet state and with rough surface) by using the bench equipment (NIR quality index #6). 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.
- 58. 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)
- 59. Important considerations Logs: • Resolution (both spatial and spectral) • Measurement time • Number of measurements • Effect of ruggedness (effect of moisture, temperature and vibrations) Wood: • Number of scanes per averaging • Number of measurements • Selection of scanning zones (wood section, early/late wood) • Effect of roughness and surface preparation • Effect of moisture • Effect of time (surface deactivation)
- 60. 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 WooddefectsaccordingtoEN 1927-1:2008 stain ? ? ? ? ? ? ? lignin ? ? ? ? ? ? ? cellulose ? ? ? ? ? ? ? hemicellulose ? ? ? ? ? ? ? extractives ? ? ? ? ? ? ? microfibryl angle ? ? ? ? ? ? calorific value ? ? ? ? ? ? ? ? heartwood/sapwood ? ? ? ? density ? ? ? ? mechanical properties ? ? ? ? moisture content provenance ? resonance wood ? ? ? ? ? ? ? ? ? ? ? Otherwoodproperties/characteristics
- 61. • 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 be developed and compared to the classification based on the expert’s knowledge. • calibrations transfer between laboratory instruments (already available) and portable ones used in the field measurements in order to enrich the reliability of the prediction (BOKU). Development and validation of chemometric models.
- 62. Thank you very much
- 63. Project SLOPE Mid-term Review 2/Jul/2015 T4.3– Evaluation of hyperspectral imaging (HI) for the determination of log/biomass “HI quality index” Brussels, July 2th, 2015
- 64. Overview Mid-term Review 2/Jul/2015 • Status: Completed (70 %) • Length: 14 Months (From M8 to M21) • Involved Partners • Leader: BOKU • Participants: CNR, GRAPHITECH, COMPOLAB, FLY, GRE • Aim: Evaluating the usability of hyperspectral imaging for characterization o bio-resources along the harvesting chain and providing guidelines for prope collection and analysis of data • Output: • D4.04 Establishing hyperspectral measurement protocol (M13/M15) • D4.09 Estimation of log quality by hyperspectral imaging (due to M21)
- 65. Mid-term Review 2/Jul/15 Task 4.3 – Output D4.04 Establishing hyperspectral measurement protocol (M13/M15) • Methodology, laboratory setup and field transfer D4.09 Estimation of log quality by hyperspectral imaging (M21) • Labscale investigations (visible range and near infrared hyperspectral cameras) • Validation by NIR measurements • Application of chemometric approaches for data evaluation and multivariate image analysis • Identification of most relevant spectral information • Transfer to (harsh) field conditions • Development of the “HI quality index” for quality grading • Technological implementation on prototype
- 66. Mid-term Review 2/Jul/15 D4.03 Hyperspectral measurement protocol – potential HSI application hyperspectral measurement (wet & rough state at differ- ent temperatures) compute wet wood HSI quality index#3 cut pieces for drying, wood moisture determination chemometric models for wet & rough wood and/or in field chemometric models for wet & rough wood (lab) collect samples: wood logs measurement hyperspectral image measurement of hyperspectral imaging handheld device compute HSI quality index#2 compute HSI quality index#5 (optional) measurement hyperspectral image handheld device compute HSI quality index#6 tree marking cutting tree processor head pile of logs expert system & data base condition rough samples to norm climate (20 °C, 60 %) hyperspectral measurement (cond. grinded state) compute the log quality class (optimize cross-cut) estimated tree quality forest models update the forest database compare results of different temperatures, roughness, wet and dry states combine all available char- acteristics of the log lab calibration transfer f(MC, surface_quality) 3D tree quality index NIR quality index stress wave SW quality index cutting force CF quality index compute HSI quality index#1 grind samples Storage of samples in lab (frozen -20°C) measure surface roughness & temp hyperspectral measurement (cond. rough state) compute dry wood HSI quality index#4
- 67. D4.03 Establishing HS measurement protocol – laboratory setups VIS-NIR HSI system a CNR (spectral range 400 – 1000 nm) NIR HSI system a BOKU (spectral range 900-1700 nm) Pushbroom Hyperspectral Imaging Systems at CNR and BOKU Mid-term Review 2/Jul/15 NIR used to validate HSI data D4.03 Establishing NIR measurement protocol
- 68. Mid-term Review 2/Jul/15 D4.03 Establishing HS measurement protocol – analytical approach Analytical approach • rough surface with original moisture content at 5 different temperatures (-5, 0, 5, 15, 25° C) from both sides • rough surface at conditioned moisture & temperature (norm climate 20 °C and 60 % air moisture, represents about 12 % wood moisture) from both sides • grinded surface at conditioned moisture/temperature to assess the effect of surface roughness on the results in relation to the targeted deficits from both sides • different angles/sources of lightning • different contaminations (soil and/or oil) • NIR measurements for validation Analytical steps & model developm
- 69. D4.03 – Initial results - fungus • Fungus clearly identifiable on the dry and wet wood • Influence of wood surface roughness was negligible (diffuse lightning) • Comparable results of HSI and NIR – causal/explanatory model possible @ IASIM Conference 3.-5.December 2014 Mid-term Review 2/Jul/15
- 70. Task 4.3 – Sampling campaign BOKU education forest at Forchtenstein (Rosalia), Burgenland 25 samples of spruce (Picea abies) with different defects (ø 15 - 45 cm), March 2015 Mid-term Review 2/Jul/15
- 71. Mid-term Review 2/Jul/15 Task 4.3 – 25 samples (spruce, Picea abies) with defects resin pockets eccentric pith + compression wood + rot eccentric pith + rot + knot shakes, checks, splits knots D4.01 Existing grading rules for log/biomass
- 72. Task 4.3 – First results resin pockets NIR vs. HSI (NIR) NIR HSI Mid-term Review 2/Jul/15
- 73. Task 4.3 – First results resin pockets NIR vs. HSI (NIR) Subtraction spectrum NIR Mid-term Review 2/Jul/15 Subtraction spectrum HSI
- 74. Task 4.3 – First results resin pockets NIR vs. HSI (NIR) Brussels 3/jul/2015 NIR HSI 1190 nm RGB NIR of resin 1000 nm HSI of resin Subtraction spectra HSI & NIR
- 75. Task 4.3 – Results for resin pockets Intensity slabs Brussels 3/jul/2015 1190 nm 1377 nm
- 76. Task 4.3 – First results training & classification Training sample - PLS-DA supervised classification Mid-term Review 2/Jul/15
- 77. Task 4.3 – First results training & classification Test sample – PLS-DA supervised classification Class Pred. Membership Class Pred. Probability Mid-term Review 2/Jul/15
- 78. Task 4.3 – Analytical challenges • Temperature • Roughness • Lightning & referencing • Water & Ice • Other contam. Roughness can be calculated by z-values of 3D scan Measurements at different temperatures yield temperature effect 0°C 5°C 15°C 1190 nm Diffuse lightning reduces morphological effects, needs to be carefully considered Ice and water have specific bands, wavelength selection important 1190 nm 1377 nm -5°C 5°C 15°C Mid-term Review 2/Jul/15
- 79. Task 4.3 – Field transfer options Implementation of the hyperspectral imaging in the field: • Hyperspectral imaging using new technologies Optimal accuracy and spatial resolution Rigidity of sensors (not suitable for harsh conditions) Relatively high cost • Mono/multi spectral imaging the log cross-section Optimal spatial resolution Reasonable cost Poor spectral accuracy Challenges with implementation • Several simple spectrometers installed on the scanning bar & measuring the log cross-section Optimal spectral accuracy and sufficient spatial resolution Reasonable cost Difficulties with implementation Mid-term Review 2/Jul/15 T3.4 Intelligent processor head
- 80. Mid-term Review 2/Jul/15 Task 4.3 – Dissemination outcomes (WP8) • Scientific publications 1. Inspection of log quality by hyperspectral imaging (Scientific Poster, Fifth IASIM conference in spectral Imaging, IASIM-14, Rome, DEC 3-5, 2014) 2. Assessing resin pockets on freshly cut wood logs of spruce by NIR and hyperspectral imaging, European Journal of Wood and Wood Products (Scientific paper, Oct 2015) 3. Determination of wood quality using HSI in the near infrared, European Journal of Wood and Wood Products (Scientific paper, Nov 2015) • HSI Workshop and links to other EU projects 58 participants /11 countries, 13 Universities/Research Institutions, 4 companies, from 8 out of 15 BOKU departments 1. FP7 project project n°284181 Trees4future - Designing Trees for the future 2. FP7 project n°211326 CONFFIDENCE - CONtaminants in Food and Feed: Inexpensive DEtectioN for Control of Exposure 3. FP7 project n°618080 HELICoiD - HypErspectraL Imaging Cancer Detection
- 81. Mid-term Review 2/Jul/15 Contact info Andreas Zitek: andreas.zitek@boku.ac.at Thank you for your attention
- 82. TASK 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” Work Package 4: Multi-sensor model- based quality of mountain forest production Task leader: Mariapaola Riggio (CNR)
- 83. The objectives of this task is to optimize testing procedures and prediction models for characterization of wood along the harvesting chain, using acoustic measurements (i.e. stress-wave tests). A part of the activity will be dedicated to the definition of optimal procedures for the characterization of peculiar high-value assortments, typically produced in mountainous sites, such as resonance wood. Task Leader: CNR Task Participants: Greifenberg, Compolab WP4: T 4.4 Data mining and model integration of log/biomass quality indicators from stress-wave (SW) measurements, for the determination of the “SW quality index” Objectives
- 84. WP4: T 4.4 Deliverables D4.05) Establishing acoustic-based measurement protocol: This deliverable contains a report and protocol for the acoustic-based measurement procedure Starting Date: August 2014 - Delivery Date: December 2014 D4.10) Estimation of log quality by acoustic methods: Numerical procedure for determination of “SW quality index” on the base of optimized acoustic velocity conversion models. Starting Date: January 2015 - Delivery Date: August 2015 Estimated person Month= 6.00
- 85. T4.4: Data mining and model integration of log/biomass quality indicators from stress-wave Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: the resources planned: 5.5 M/M the resources utilized: PROBLEMS: Delay related to the processor head SOLUTIONS: LAB scanner + collaboration with engineers 31.11.2015 draft: December 2014 accepted: July 2015 ! !
- 86. D: 4.5 Establishing acoustic-based measurement protocol Stress-wave data acquisition and analysis
- 87. D: 4.5 Establishing acoustic-based measurement protocol Accelerometer #1 Accelerometer #2 Laser displacement sensor SW generator TOF Resonancemethod Local characterization Global characterization Stress-wavedata acquisition and analysis
- 88. D: 4.5 Establishing acoustic-based measurement protocol
- 89. D: 4.5 Establishing acoustic-based measurement protocol Test plan for on-line SW measurement in the processor head Laser measurements for longitudinal axial modes analysis with the resonance method
- 90. D: 4.5 Establishing acoustic-based measurement protocol Test plan for on-line SW measurement in the processor head Time of flight (ToF) measurements
- 91. 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
- 92. TASK 4.5 Evaluation 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 leader: Jakub Sandak (CNR)
- 93. Task 4.5: cutting process quality index Objectives The goals of this task are: • to develop a novel automatic system for measuring of the cutting resistance of wood processed during harvesting • to use this information for the determination of log/biomass quality index
- 94. Task 4.5: Cutting Process (CP) for the determination of log/biomass “CP quality index” Task Leader: CNR Task Partecipants: Compolab Starting : October 2014 Ending: November2015 Estimated person-month = 4.00 (CNR) + 2.00 (Compolab) CNR : will coordinate the research necessary, develop the knowledge base linking process and wood properties, recommend the proper sensor, develop software tools for computation of the CP quality index Compolab: will provide expertise in regard to sensor selection and integration with the processor head + extensive testing of the prototype
- 95. Task 4.5: cutting process quality index Deliverables D.4.06 Establishing cutting power measurement protocol Report: This deliverable will contain a report and recommended protocol for collection of data chainsaw and delimbing cutting process. Delivery Date: January 2015 (M.13) DONE D.4.11 Estimation of log quality by cutting power analysis Prototype: Numerical procedure for determination of “CP quality index” on the base of cutting processes monitoring Delivery Date: December 2015 (M.24)
- 96. T4.5: Evaluation of cutting process (CP) for the determination of log/biomass CP quality index Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory scale tests for chain saw energy needs CNR: Develop models linking CP in delimbing and quality CNR: Develop models linking CP in chain sawing and quality CNR: Develop report on using CP CNR: the resources planned: 6.0 M/M the resources utilized: PROBLEMS: Delay related to the processor head and final sensor selection/design SOLUTIONS: LAB scanner + collaboration with engineers + list of sensor(s) for purchase ready 31.12.2015 ! ! draft: January 2014 accepted: July 2015
- 97. • load cell: indirectly measuring the cutting force • strain gauge: rough estimation of cutting forces on the base of machine parts deformation • pressure sensor: continuously measure oil pressures both in the hydraulic motor and feed pistons • oil flow meter: indirect evaluation of the cutting speed and its changes due to cross-cutting progress • electrical multimeter: electrical analogy for measuring the oil pressure • other: dedicated to determine if emission generted while cutting out braches contain any signal features useful for detection of the branch presence and/or its size and health status • Acoustic emission (AE): (signal frequency > 50kHz) • Microphone: cutting sound (signal frequency < 20kHz) Task 4.5: cutting process quality index sensors for cutting process monitoring
- 98. • working time of the cutting tools (knifes and chain): • estimation of the tool wear and correction of the cutting forces • position of the saw bar while cross-cutting: • monitoring of the cutting progress • correction factors related to the determination of the cutting forces and material characteristics • log diameter (combined with position of the saw bar): • determination of the cutting length at each moment of the cross-cutting • position of the main hydraulic actuator while cutting-out branches: • monitoring of the de-limbing progress • determination/mapping of the detailed knot position Task 4.5: cutting process quality index other sources of information
- 99. sensor type simplicity reliability informationquality easyinterpretation lowcost suitableforSLOPE laboratorytests suitableforSLOPE in-fieldapplication load cell strain gauge electric multimeter oil pressure oil flow AE microphone Task 4.5: cutting process quality index comparison of sensors
- 100. • a process where several cutting edges are involved in the cutting at the same time. The angle between the main cutting edge and fibre direction is ~0°, while the angle between feed direction and grain angle is ~90°. • rather difficult to direct monitoring due to peculiarities related to the processing in the field/forest (suitable sensors, capability for acquisition of the proper energetic effects of cutting) • relatively resistant to various sources of noise • great effect of the tool sharpness changing along the processing time • cutting speed and/or feed speed changes considerably between (and within) cutting cycles. • cutting power is related to the quantity of the material to split (log diameter) and variation within the material itself: • reaction wood • knots • small ring width • decayed/rooted wood Task 4.5: cutting process quality index cross-cutting with the chain saw
- 101. Depends on: • wood density • cutting conditions • selected mechanical properties of wood (such as fracture toughness, shear strength, MOE...) Task 4.5: cutting process quality index cutting forces in (chain) sawing
- 102. hydraulic pressure sensors , load cell , strain gauges , AE sensors , microphone Task 4.5: cutting process quality index schematic of the log cross-cutting system of the ARBRO1000
- 103. electric chain saw , feeding actuator of the chain saw , load cell , strain gauges , AE sensors , microphones , electric multimeter Task 4.5: cutting process quality index the laboratory log cross-cut simulator
- 104. • one cutting edge is involved in the branch cutting-out. • the most demanding cutting configuration as the cutting forces foreseen in that arrangement are the highest: the angle between the main cutting edge and fibre direction is ~90°, and between feed direction and grain angle is also ~90°. • the cutting angle is very small • simpler to asses than of cross-cutting with a chain saw: due to the peculiar design of the processor head where the cutting knife if fixed to the machine body and not any high-frequency changes to the process occurs • depending on the processor design, the log may be moved against knives or alternatively the knives may be shifted along the trunk • the fixed knifes system is relatively resistant for external sources of noise allowing measuring of the cutting power by means of various sensors • an important issue to be considered is an effect of the tool geometry and sharpness • de-branching forces depend on: • branch size • health state and moisture Task 4.5: cutting process quality index process of tree de-branching
- 105. source: Benjamin Hatton et. al (2015) Experimental determination of delimbing forces and deformations in hardwood harvesting, Croat. j. for. eng. 36-1:43-53 dimensionless displacement force,kN 0.0 0.5 1.0 0 10 20 30 40 ɸ=85.7mm ɸ=62.5mm branch area, cm2 maximumforce,kN 0 10 20 30 40 30 40 50 60 low cutting speed high cutting speed Task 4.5: cutting process quality index cutting forces in delimbing
- 106. hydraulic pressure sensors , load cell beside the cutting knives , strain gauges , AE sensor(s) , microphone(s) Task 4.5: cutting process quality index schematic of the instrumented de-branching system of the ARBRO1000
- 107. feeding system of the scanner frame , instrumented cutting knife , load cell , strain gauges , AE sensors , microphones , electric multimeter Task 4.5: cutting process quality index laboratory de-branching simulator
- 108. Cutting process chain saw de-branching Surface of sample laboratory processor laboratory processor knots resin pocket twist eccentric pith compression sweep taper shakes insects dote rot WooddefectsaccordingtoEN 1927-1:2008 stain chemical heartwood/sapwo density mechanical moisture content Otherwood properties/chara resonance wood Task 4.5: cutting process quality index applications for detection defects in logs
- 109. two quality indexes (numbers in the range from 0 to 1) associated to wood/log properties are determined: • CP quality index #1: reflects the estimation of the “wood density” as related to the cutting resistance during cross-cutting of log by chain saw. The quality index #1 value is unique for the whole log. CP quality index #1 = f(wood moisture content, tool wear, cutting speed, feed speed, log diameter, ellipsoid shape, presence of defects) • CP quality index #2: reflects the “brancheness” of the log along its length and is estimated by means of signals associated with cutting out branches. The quality index #2 is spatially reolved. CP quality index #2 = f(hydraulic pressure changes along the log length, changes of cutting forces in time, number of AE events or sound pressure level) Task 4.5: cutting process quality index algorithms for data mining
- 110. • to plot a quality map – suport operator in taking decision - that is mostly affecting the whole following transformation chain!!! • to grade the log • (to optimize cut-to-length) Concept of the “quality map” indicating as a pattern sections of selected log properties, geometries and presence of various defects derived from the cutting-power analysis. Task 4.5: cutting process quality index use of quality indexes
- 111. Task 4.5: cutting process quality index Challenges Some additional delay with prototype developing: the equipment ordered and soon ready for installation 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?
- 112. Thank you very much
- 113. TASK 4.6 Implementation of the log/biomass grading system Work Package 4: Multi-sensor model- based quality control of mountain forest production Task leader: Jakub Sandak (CNR)
- 114. Task 4.6: Implementation of the log/biomass grading system Task Leader: CNR Task Participants: GRAPHITECH, COMPOLAB ,MHG, BOKU, GRE, TRE Starting : June 2014 Ending: July 2016 Estimated person-month = 1.50 (GRAPHITECH) + 2.0 (CNR) + 1.00 (COMPOLAB) + 1.00 (MHG) + 1.00 (BOKU), 0.50 (GRE) + 1.00 (TRE) CNR: will coordinate the research necessary, develop the software tools (expert systems) and integrate all available information for quality grading TRE, GRE, COMPOLAB: incorporate material parameters from the multisource data extracted along the harvesting chain GRAPHITECH: integration with the classification rules for commercial assortments, linkage with the database of market prices for woody commodities MHG: propagate information about material characteristics along the value chain (tracking) and record/forward this information through the cloud database BOKU: validation of the grading system
- 115. 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
- 116. Task 4.6: Implementation of the grading system Deliverables D.4.01 Existing grading rules for log/biomass Report: This deliverable will contain a report on existing log/biomass grading criteria and criteria gap analyses Delivery Date: October 2014 (M.10) DONE D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure Prototype: This deliverable will contain a report on the validation procedure, and results of the quality class prediction models, and integration in the SLOPE cloud data base Delivery Date: June 2016 (M.30)
- 117. T4.6: Implementation of the log/biomass grading system Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR: the resources planned: 8.0 M/M the resources utilized: PROBLEMS: Delay related to other tasks – difficulties with implementation SOLUTIONS: LAB scanner + prototype software developed in lab + algorithms ready 31.06.2016 draft: October 2014 accepted: July 2015 ! !
- 118. Task 4.6: Implementation of the grading system Deliverable 4.01 - content • 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 • General overview on the recent wood market in relation to SLOPE • Global level • European Union level • Specific countries • Specific regulations in various wood industries • Currently used log grading practices • Procedures for estimation of the log’s geometrical characteristics and volume • Visual grading procedures • Machine grading systems of logs
- 119. Currently used logs grading practice • Detailed criteria of Norway spruce (Picea abies) quality sorting are provided in European Standard 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
- 120. 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)) Conditionofforestarea Healthconditionoftree?? Foliarindex Crowndamage?? Treespeciesrecognition?? Branchindex?? Macro properties of the forest area or the whole tree knots??? resinpocket twist??? eccentricpith? compressionwood??? sweep? taper? shakes?? insects?? dote?? rot? stain? Log defects according to EN 1927-1:2008 lignin? cellulose? hemicellulose? extractives? microfibrylangle?? calorificvalue? heartwood/sapwood density???? mechanicalproperties? moisturecontent???? provenance?? woodtracking??? bottom-enddiameter top-enddiameter externalshapeoflog logdiameterwithout bark logvolume Other wood properties/characteristics resonancewood??? Suitability for detection of resources for niche products
- 121. 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)
- 122. 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
- 123. Task 4.6: Implementation of the grading system The concept (diagram)#1 Measure 3D shape of several trees Measure NIR spectra of tree X in forest Extract 3D shape of tree X Compute 3D quality in- dexes for log X.1 … X.n Measure NIR spectra of tree X on processor Measure NIR spectra of tree X on the pale Compute NIR quality in- dex for tree X Compute NIR quality in- dexes for log X.1 … X.n Compute NIR quality in- dexes for log X.1 … X.n Data base for harvest data Determine quality for log X.1 … T4.1 T4.2 Measure hyperspectral image of tree X in forest Measure cross section image of log X.1 … X.n Measure NIR spectra of tree X on the pale Compute HI quality index for tree X Compute HI quality in- dexes for log X.1 … X.n Compute HI quality in- dexes for log X.1 … X.n T4.3
- 124. 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
- 125. 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
- 126. Task 4.6: Implementation of the grading system data flow & in-field hardware NI CompactRio master Database NI CompactRio client FRID weight fuel ??? Data storage CP NIR HI SW camera kinect
- 127. Task 4.6: Implementation of the grading system The “real world” actions: lab scanner
- 128. Task 4.6: Implementation of the grading system The “real world” actions: processor
- 129. Task 4.6: Implementation of the grading system Challenges What sensors set is optimal (provide usable/reliable information)? How to merge various types of indexes/properties? Can the novel system be accepted by “conservative” forest (and wood transformation) industry? How the SLOPE quality grading will be related to established classes? the final answer possible only after demonstrations
- 130. Thank you very much