This technical note determines the feasibility of using an InceptionV4_ResNetV2 convolutional neural network (CNN) to correctly identify hardwood species from macroscopic images. The method is composed of a commodity smartphone fitted with a 14× macro lens for photography. The end-grains of ten different North American hardwood species were photographed to create a dataset of 1869 images. The stratified 5-fold cross-validation machine-learning method was used, in which the number of testing samples varied from 341 to 342. Data augmentation was performed on-the-fly for each training set by rotating, zooming, and flipping images. It was found that the CNN could correctly identify hardwood species based on macroscopic images of its end-grain with an adjusted accuracy of 92.60%. With the current growing of machine-learning field, this model can then be readily deployed in a mobile application for field wood identification.
Activated carbon was prepared from pyrolyzed pinewood char using KOH, H3PO4, H2O2, and heat-only treatments. Activated carbon prepared by the heat-only treatment had a total surface area of 233.2 m 2
Effects of guayule resin on mechanical, physical, and biological performance of wood strand-based panels were evaluated. Southern yellow pine (Pinus spp. L) wood strands were mixed with phenol formaldehyde (PF) resin to a target resin content of 5% and hot-pressed to manufacture wood strand-based panels. A guayule resin solution was prepared and sprayed on the wood strands immediately after PF resin to different guayule resin contents of 0.5% and 1.0%. Specimens cut from treated panels and control panels were subjected to tensile, internal bond, water absorption and thickness swelling, and fungi soil block tests. Guayule resin had a positive effect on tensile strength, as specimens showed 8.0% and 9.5% increase compared to control specimens. However, the internal bond strength decreased 5.3% and 6.4%, respectively. Water absorption and thickness swelling for the treated specimens with guayule resin decreased as compared to control specimens. The fungal decay resistance test indicated little differences in the average percent mass loss across the untreated and treated wood strand-based composite materials. Regardless of increase or decrease, the effects of guayule resin on mechanical, physical, and biological performances of wood strand-based panels were not statistically significant.
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