Image analysis to assess wood variability in longleaf pine cross-sectional disks

Image analysis is an important method for rapidly measuring wood property variation, but it is infrequently applied to disks collected from forestry studies. The objective of this study was to compare image estimated wood and bark volumes and diameters to reference measurements, and to extract more information from the images including the shape (out of round index, eccentric pith) and the amount and location of severe compression wood. A total of 1,120 disks were cut from multiple height levels of 48 defect-free and 56 defect-containing longleaf pine (Pinus palustris) trees from 16 stands across Georgia (U.S.). Disks were machined on one transverse surface using a computer numeric controlled router to prepare a clean surface for imaging. Three images; one under white light, second under blue light, and third under blue light with a green longpass filter, were taken for each disk. Volumes and diameters estimated from images were in close agreement with reference methods. Linear models fitted as measured versus image volumes for wood and bark had coefficient of determination (R2) values of >0.99 and 0.96. Linear models fitted as measured versus image diameters had R2 values of >0.99. Out of round index and pith eccentricity values calculated from images showed a moderate positive correlation (R=0.43). Algorithms developed were able to correctly identify severe compression wood, but not mild to moderate compression wood. Severe compression wood was moderately correlated to out of round index (R=0.54) and pith eccentricity (R=0.48). More than 98% of the disks having severe compression wood came from defect-containing trees

IDENTIFICATION AND RECOGNIZATION OF BAMBOO BASED ON CROSS-SECTIONAL IMAGES USING COMPUTER VISION

Identification of bamboo is of great importance to its conservation and uses. However, identify bamboo manually is complicated, expensive, and time-consuming. Here, we analyze the most evident and characteristic anatomical elements of cross section images, that’s a particularly vital breakthrough point. Meanwhile, we present a novel approach with respect to the automatic identification of bamboo on the basis of the cross-sectional images through computer vision.Two diverse transfer learning strategies were applied for the learning process, namely fine-tuning with fully connected layers and all layers, the results indicated that fine-tuning with all layers being trained with the dataset consisting of cross-sectional images of bamboo is an effective tool to identify and recognize intergenericbamboo, 100% accuracy on the training dataset was achieved while 98.7% accuracy was output on the testing dataset, suggesting the proposed method is quite effective and feasible, it’s beneficial to identify bamboo and protect bamboo in coutilization. More collection of bamboo species in the dataset in the near futuremight make EfficientNet more promising for identifying bamboo.  

DISTINGUISHING NATIVE AND PLANTATION-GROWN MAHOGANY (SWIETENIA MACROPHYLLA) TIMBER USING CHROMATOGRAPHY AND HIGH-RESOLUTION QUADRUPOLE TIME-OF-FLIGHT MASS SPECTROMETRY

Plantation-grown mahogany (Swietenia macrophylla) from Fiji has been preferred as a sus- tainable wood source for the craftingof electric guitars because its trade is not restricted by Convention on International Trade in Endangered Species of Wild Fauna andFlora (CITES), unlike S. macrophylla sourced from native forests. Ability to differentiate between the two wood types would deter sale of illegally harvested native-grown S. macrophylla to luthiers and other artisans. The chemical composition of wood is influenced bycambial age and geographical factors, and there are chemical differences between S. macrophylla grown in different regions. Thisstudy tested the ability of high-resolution mass spectrometry to chemotypically dif- ferentiate plantation-grown Fijian S. macrophylla from the same wood species obtained from native forests. Multiple heartwood specimens of both wood types were extracted and chromatographically profiled using gas and liquid chromatography tandem high-resolution quadrupole time-of-flight massspectrometry (GC/QToF, LC/QToF). Visual comparison of mass spectral ions, together with modern analytical data-mining techniques,were employed to screen the results. Principal component analysis scatter plots with 95% confidence ellipses showed unambiguousseparation of the two wood types by GC/LC/QToF. We conclude that screening of heartwood extractives using high-resolution massspectrometry offers an effective way of identifying and sepa- rating plantation-grown Fijian S. macrophylla from wood grown in native forests

USE OF A PORTABLE NEAR INFRARED SPECTROMETER FOR WOOD IDENTIFICATION OF FOUR DALBERGIA SPECIES FROM MADAGASCAR

This study focused on the use of Near InfraRed (NIR) Spectroscopy to address the lack of tools and skills for wood identification of Dalbergia species from Madagascar. Two sample sets of 41 wood blocks and 41 wood cores belonging to four Dalbergia species (D. abrahamii, D. chlorocarpa, D.greveana, and D. pervillei) were collected in the northern and western regions of Madagascar. Sapwood and heartwood NIR spectra were measured onwood at 12% moisture content by using a portable VIAVI MicroNIR 1700 spectrometer. Four discrimination models corresponding to sapwood and heartwood of the two sample forms were developed using Partial Least Square Discriminant Analysis (PLSDA). Good accuracy of 83.3% and 81.8% were obtained from the heartwood-based PLSDA models respectively for wood blocks and wood cores samples. All D. chlorocarpa samples were well-classified by the two models. Results highlighted the potential of portable NIR Spectroscopy as a helpful tool tosupport sustain- able management and trade of Madagascar’s Dalbergia species. Further studies are, however, needed for its operational use in identification routine

CASE STUDY OF 3-PLY COMMERCIAL SOUTHERN PINE CLT MECHANICAL PROPERTIES AND DESIGN VALUES

This work elucidates on a case study of industrially manufactured cross-laminated timber (CLT). Two methods are used to calculate specimens section modulus: Sgross and Seffective. The first assumes that specimens behave as a continuous material, whereas the second considers the cross laminations (shear analogy method). Although the shear analogy method is indicated for construction purposes, applications, such as trench shoring, matting, and work platforms, could benefit from a simpler calculation method. There- fore, theobjective of this work was to conduct a case study of Modulus of Rupture (MOR) and Modulus of Elasticity (MOE) of southern pine CLT to compare the previously mentioned calculation methods. Both parametric and nonparametric fifth percentiles and associated Fb values are reported and were substantially higher than those of the constituent lumber. For MOE, empirical testing and calculation based on gross moment of inertia provided lower values as compared with the constituent lumber

Farewell Letter from Vicki Herian

Farewell lette

Exec Secretary: Award write up

No abstract- file is the entire body of tex

PRESERVATIVE TREATMENT OF TASMANIAN PLANTATION EUCALYPTUS NITENS USING SUPERCRITICAL FLUIDS

Short rotation plantation forests in Tasmania, Australia, are dominated by Eucalyptus nitens (common name: shining gum). These forests were primarily planted to provide material for pulp and paper production, but the timber is increasingly sought after for higher value and more enduring applications. Plantation E. nitens has a high proportion of low durability heartwood that resists penetration by conventional fluid preservatives. This limits its use to indoor applications. One approach to overcoming the refractory nature of E. nitens is to modify the treatment fluid. We investigated the use of supercritical carbon dioxide to deliver biocides deep into the wood. Timbers varying in thickness from 19 to 35 mm and 900 mm long were treated with a multicomponent biocide under supercritical conditions in a commercial facility in Denmark. The resulting timber was cut into zones inward from the surface. Wood from these zones was ground and extracted for HPLC analysis for tebuconazole and propiconazole. Preservative was detected in the inner portion of every sample examined, indicating that the process resulted in treatment throughout the boards, with concentrations meeting and on average exceeding the targeted amounts

FIBER QUALITY PREDICTION USING NIR SPECTRAL DATA: TREE-BASED ENSEMBLE LEARNING VS DEEP NEURAL NETWORKS

The growing applications of near infrared (NIR) spectroscopy in wood quality control and monitoring necessitates focusing on data-driven methods to develop predictive models. Despite the advancements in analyzing NIR spectral data, literature on wood science and engineering has mainly uti- lizedthe classic model development methods, such as principal component analysis (PCA) regression or partial least squares (PLS) regression, with relatively limited studies conducted on evaluating machine learning (ML) models, and specifically, artificial neural networks (ANNs). This couldpotentially limit the performance of predictive models, specifically for some wood properties, such as tracheid width that are both time-consuming tomeasure and challenging to predict using spectral data. This study aims to enhance the prediction accuracy for tracheid width using deep neural networks and tree-based ensemble learning algorithms on a dataset consisting of 2018 samples and 692 features (NIR spectra wavelengths). Accord- ingly, NIR spectra were fed into multilayer perceptron (MLP), 1 dimensional-convolutional neural net- works (1D-CNNs), random forest, TreeNet gradient-boosting, extreme gradient-boosting (XGBoost), and light gradient-boosting machine (LGBM). It was of interest to study the performance of the models with and without applying PCA to assess how effective they would perform when analyzing NIR spectra with- out employing dimensionality reduction on data. It was shown that gradient-boosting machines outper- formed the ANNs regardless of the number of features (data dimension). Allthe models performed better without PCA. It is concluded that tree-based gradient-boosting machines could be effectively used for wood characterization utilizing a medium-sized NIR spectral dataset

EVALUATION OF TEST SPECIMEN SURFACE PREPARATION ON COMPUTER VISION WOOD IDENTIFICATION

Previous studies on computer vision wood identification (CVWID) have assumed or implied that the quality of sanding or knifing preparation of the transverse surface of wood specimens could influence model performance, but its impact is unknown and largely unexplored. As a preliminary step in investigating the possible effects of surface preparation quality, this study evaluates the predictive accuracy of a previously published 24-class model, trained on images from Peruvian wood specimens prepared at 1500 sanding grit, with testing images from specimens (not used for training) prepared across a series of progressively coarser sanding grits (1500, 800, 600, 400, 240, 180 and 80) and high-quality knife cuts.  The results show that while there was a drop in performance at the lowest sanding grit of 80, most of the higher grits and knife cuts did not exhibit statistically significant differences in predictive accuracy. These results lay the groundwork for a future larger-scale investigation into how the quality of surface preparation in both training and testing data will impact CVWID model accuracy