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Wood Identification is an important task, especially for wood anatomist. The identification process is required in many fields, such as custom ports, forest survey, and wood industries. However, expertise-in wood identification is limited due to a few wood anatomists. To enable wood identification can be performed in a broader area, a mobile-based...
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Wood is a material characterized by anisotropy of structure and variability of properties in the tree trunk. The indicators of wood properties can be predicted by the density value. Wood density formation in a tree trunk is influenced by many factors. The most important are: age changes, the position in the trunk and the impact of the environment....
Quantification and reduction of residues generated through wood conversion are important in wood resources management. This study was carried out, to contribute to this, by using air-dried sawnwood from three tropical hardwood (Afzelia africana, Milicia excelsa and Tectona grandis) with mean dimension 117x305x1830mm to produce furniture parts of di...
It is established that thermal modification provides a decrease in the hydrophobicity of wood. Additional wood surface treatment reduces water absorption factor of 2 and modified sample at a temperature of 220°C – 3 times. Geometric and dimensional stability is improved by 2 times. The effect of heat treatment on the water absorption of wood is sli...
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... The CNN models have mainly been trained to identify wood species automatically 11 . With a motivation to preserve endangered species, prevent illegal logging, and ensure the authenticity of wood products, DL solutions have been proposed to recognize wood species from images of wood surface [11][12][13][14][15][16][17] , standing trees species recognition from 3D point clouds of trees collected by light detection and ranging (LiDAR) or terrestrial laser scanning (TLS) [18][19][20] and near-infrared (NIR) spectroscopy based tree species identification 21,22 . Convolutional neural networks have also been proposed to process remote sensing and aerial images in order to monitor forest infestation and health conditions or detect fires [23][24][25][26][27][28] . ...
The structure and features of resin ducts provide valuable information about environmental conditions accompanying the growth of trees in the genus Pinus. Therefore analysis of resin duct characteristics has been an increasingly common measurement in dendrochronology. However, the measurement is tedious and time-consuming since it requires thousands of ducts to be manually marked in an image of an enlarged wood surface. Although tools exist to automate some stages of this process, no tool exists to automatically recognize and analyze the resin ducts and standardize them with the tree rings they belong to. This study proposes a new fully automatic pipeline that quantifies the properties of resin ducts in terms of the tree ring area to which they belong. A convolutional neural network underlays the pipeline to detect resin ducts and tree-ring boundaries. Also, a region merging procedure is used to identify connected components corresponding to successive rings. Corresponding ducts and rings are next related to each other. The pipeline was tested on 74 wood images representing five Pinus species. Over 8000 tree-ring boundaries and almost 25,000 resin ducts were analyzed. The proposed method detects resin ducts with a sensitivity of 0.85 and precision of 0.76. The corresponding scores for tree-ring boundary detection are 0.92 and 0.99, respectively.
