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 value
5. 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…

Wood might not be perfect…

Wood from mountains might be priceless…

The goals of this WP are:
• to develop an automated and real-time grading (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

Fine-grained timeline (all tasks):
4
TRE 4.1
CNR 4.2
BOKU 4.3
CNR 4.4
CNR 4.5
CNR 4.6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

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

Task 2.3
4.1.
on-field forest survey
GPS
PC/PAD
3D scanner
3D vision
Tasks 3.1
4.2-4.3
Mark tree
Confirm route of cable crane
GPS
PC/PAD
RFID TAG
RFID reader
Tasks 3.2
4.4
Tree felling
Database
NIR QI
H QI
RFID reader
RFID TAG
(if cross cut)
Portable NIR
Hyperspectral
Accellerometers
Oscilloscope
SW QI
Tasks 3.3
Cable crane
Techno carriage
GPRS
RFID reader
WIFI
Skyline launcher
Load sensor
Intelligent chookers
GPS
PC/PAD
Data logger
Black box access
Control system
M/M interface
Tasks 3.4
4.2-4.3-4.4-4.5-4.6
Processor
de-brunch, cut to length, measures, mark
Load cell for cutting force
Cutting feedsensor
Feed forcesensor
Diameterdigital caliper
Length
RFID reader
RFID TAG
PC control comp.
GPRS/WIFI
Hyperspectral
NIR scanner
Kinect® (or similar 3D vision)
Microphone/accellerometer
Data logger
Black box access
CodePrinter
Control system
M/M interface
ID backup
Database
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
GPS
GPRS
RFID antenna
BUSCAN
Load cell
Logistic Software
ID backup
ID backup
Weight, time
Quality class

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

TreeMetrics
“3D Quality Index”

Quality Index
14cm
7cm
14cm
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14cm
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7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
• Taper Variation
• Straightness
• Branching
• Rot etc.

The Products: General Values
14cm
7cm
14cm
7cm
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7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
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16cm
7cm7cm
Pulp
7cm
PulpPulp = €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
7cm7cm
Pulp
7cm
PulpPulp 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
7cm7cm
Pulp
7cm
PulpPulp 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
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
3.7mOption 1

14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.3mOption 2

14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.9mOption 3

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

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

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 forests
Task leader: Anna Sandak (CNR)

Task 4.2: Partners involvement
Task Leader: CNR
Task 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 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 M16 April 2015
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 M20 August 2015
Estimated person Month= 3.45

4.2Timing
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 forests
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
1 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
T4.2
D.4.03
D.4.08
test sensors avaliable on the market
finalize concept
design/adopt to the processor
test electronic system
assemble hardware
collect reference samples
analyse reference samples
test hardware + software
calibrate system
develop algorithm for NIR qualityindex
integrate 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 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).
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 countries
2163 samples measured
x 5 spectra/sample
= 10815 spectra

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: CNR
Task 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 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

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 36
1 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
T4.4
D.4.05
D.4.10
finalize concept
field tests
design/adopy to the processor
assemble hardware
callibrate system
develop algorithm for CP Q_index
integrate CP quality index with quality grading/optimization (T4.6) D.4.12
D.4.05 Establishing acoustic-based measurement protocol
D.4.10 Estimation of log quality by acoustic methods

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 about
material 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 prediction
models.
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, …
geometrical
data
TOF,
resonance
frequency

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 been
developed 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 will
be tested in the field:
on the delimbed stem: CNR – Greifenberg
on the cut logs: CNR – KESLA
Additionally measurements will be taken by operators
along the whole supply chain
Acquisition time of measurement, influence of
obstacles and factors limiting instrument performance,
reliability/quality of recorded signals and overall
validation of measurement procedures will be provided
for 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.

TASK 4.5
Evaluation of cutting process (CP) for the
determination of log/biomass “CP quality index”

Task 4.5: Cutting Process (CP) for the determination of
log/biomass “CP quality index”
Task Leader: CNR
Task Partecipants: Kesla
Starting : October 2014
Ending: November2015
Estimated 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 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: July 2015 (M.19)
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: November 2015 (M.23)

Timing
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 36
1 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
T4.5
D.4.06
D.4.11
finalize concept
design/adopr to the processor
assemble hardware
callibrate system
develop 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 protocol
D.4.11 Estimation of log quality by cutting power analysis

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

Principles
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)

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

Chainsaw

the scanning bar #1  and the chain saw  in the working positions

control system
CRio
cutting force
saw “push” force
feed force

Comments
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.

Challenges
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?

TASK 4.6
Implementation of the log/biomass grading
system

Task 4.6: Implementation of the log/biomass grading
system
Task Leader: CNR
Task Participants: GRAPHITECH, KESLA, MHG, BOKU, GRE, TRE
Starting : June 2014
Ending: July 2016
Estimated 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 grading
TRE, GRE, KESLA: 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

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: December 2014 (M.12)
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: July 2016 (M.31)

Timing
Implementation of the log/biomass grading system
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
1 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
T4.6
D.4.01
D.4.12
surveys
literature research
test quality measuring systems
develop software for integration of quality indexes
test software
calibrate system
validate the algorithm/system
D.4.01 Existing grading rules for log/biomass

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

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
Other avaliable info (1)



multisensor system  for 3D/color mapping of logs
Other avaliable info (2)


Results
Several grading rules are in use in different regions and/or niche
products: a systematic database of these rules will be developed for
this purpose.
• The performance
• Reliability
• Repetability
• Flexibility
of the grading system will be carefully validated in order to quantify
advantages from both economic and technical points of view.
at different stages of the value chain.

Challenges
What sensors set is optimal (provide usable/reliable information)?
How to merge various types of indexes/properties?
Can the novel system be accepted by “conservative” forest (and
wood transformation) industry?
How the SLOPE quality grading will be related to established
classes?

NI CompactRio master
Database
NI CompactRio client Wifi (in field)
FRID
weight
fuel
???
Wifi (home)
Wifi (home)
HD
or
GPRMS
Black box
CP
NIR
HI
SW
camera
kinect
Wifi (in field)
Wifi (home)
Wifi (home)

1st Technical Meeting - WP4

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