Identification of wood defect using pattern recognition technique

This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the F-measure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance

Hyperspectral imaging as a tool for profiling basidiomycete decay of Pinus sylvestris L.

Given the right climatic and environmental conditions, a range of microorganisms can deteriorate wood. Decay by basidiomycete fungi accounts for significant volumes of wood in service that need to be replaced. In this study, a short-wave infrared hyperspectral camera was used to explore the possibilities of using spectral imaging technology for the fast and non-destructive detection of fungal decay. The study encompassed different degradation stages of Scots pine sapwood (Pinus sylvestris L.) specimens inoculated with monocultures of either a brown rot fungus (Rhodonia placenta Fr.) or a white rot fungus (Trametes versicolor L.). The research questions were if the hyperspectral camera can profile fungal wood decay and whether it also can differentiate between decay mechanisms of brown rot and white rot decay. The data analysis employed Partial Least Squares (PLS) regression with the mass loss percentage as the response variable. For all models, the mass loss could be predicted from the wavelength range 1460–1600 nm, confirming the reduction in cellulose. A single PLS component could describe the mass loss to a high degree (90%). The distinction between decay by brown or white rot fungi was made based on spectral peaks around 1680 and 2240 nm, related to lignin.publishedVersio

Identification Of Wood Defect Using Pattern Recognition Technique

This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the Fmeasure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performanc

Development of low-cost portable spectrometers for detection of wood defects

Portable spectroscopic instruments are an interesting alternative for in-field and on-line measurements. However, the practical implementation of visible-near infrared (VIS-NIR) portable sensors in the forest sector is challenging due to operation in harsh environmental conditions and natural variability of wood itself. The objective of this work was to use spectroscopic methods as an alternative to visual grading of wood quality. Three portable spectrometers covering visible and near infrared range were used for the detection of selected naturally occurring wood defects, such as knots, decay, resin pockets and reaction wood. Measurements were performed on wooden discs collected during the harvesting process, without any conditioning or sample preparation. Two prototype instruments were developed by integrating commercially available micro-electromechanical systems with for-purpose selected lenses and light source. The prototype modules of spectrometers were driven by an Arduino controller. Data were transferred to the PC by USB serial port. Performance of all tested instruments was confronted by two discriminant methods. The best performing was the microNIR instrument, even though the performance of custom prototypes was also satisfactory. This work was an essential part of practical implementation of VIS-NIR spectroscopy for automatic grading of logs directly in the forest. Prototype low-cost spectrometers described here formed the basis for development of a prototype hyperspectral imaging solution tested during harvesting of trees within the frame of a practical demonstration in mountain forests

Fluorescence Spectrometry to Quantify Percent Wood Failure in Adhesive Bonds

Structural engineered wood manufacturers need a quantitative measurement technique to determine the ratio of wood to adhesive failure in their shear specimens. Visual estimation of percent wood failure has long stood as an extremely subjective quality control approach according to ASTM D5266 – Standard Practice for Estimating the Percentage of Wood Failure in Adhesive Bonded Joints. The inherent variability between readers often leads to completely different estimates for the same specimen. A UV-VIS spectrometer, coupled with a consistent light source and probe, offers an economic mode of chemometrics for automating the current standard and minimizing said subjectivity. Robust, repeatable, and open-source predictive models will provide a technician with a timely and accurate percent wood failure value after simply placing a lap or block shear specimen under the light probe. This approach is expected to maintain accuracy within five percent wood failure (ASTM D5266) and be as fast or faster than a trained technician. Three sets of phenol-formaldehyde bonded lap-shear specimens were provided with original ASTM visual percent wood failure estimates by trained technicians from outside organizations. High resolution images of each shear surface were then taken, and these images were evaluated with a grid overlay for an additional, more precise set of percent wood failure estimates. Thereafter, each shear surface was scanned with a probe connected to two separate light sources and data was acquired by a spectrometer. The data, having wavelengths (nm) as explanatory variables and intensity (counts) as dependent variables, was then modelled via RStudio with Partial Least Squares Regression and Simple Linear Regression using the two different wood failure estimates for each population as predictor variables. The results suggest that the grid estimates form better predictive models, and that spectrometry overall is a viable method for quantifying percent wood failure values in shear specimens – with some explained contingencies.Keywords: wood bonding, shear testing, UV-VIS fluorescence spectrometry, Multivariate Data Analysis, Engineered Wood Products, adhesive penetration, surface chemistry, automation, wood failure, machine learning, spectr

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools