基于机器学习的浅红色莫兰蒂(Shorea spp.)密度估计:使用定制的“KayuSort”颜色分类软件对自组织地图颜色簇进行多元回归的分割方法

IF 2.4 3区 农林科学 Q1 FORESTRY
Chiat Oon Tan, Ogata Shigenobu, Siew-Cheok Ng, Hwa Jen Yap, Zuriani Usop, Mohd ’Akashah Fauthan, Khairuddin Mahalil, Shaer Jin Liew
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引用次数: 0

摘要

木材密度是木材的一个重要特性,它关系到木材的强度。本研究提出了一种对预分割的木材彩色图像使用多元回归的算法来估计浅红色莫兰蒂(Shorea spp.) (LRM)的密度。从一家工厂随机抽取两批LRM木材(第1批:119个样品,第2批:79个样品)。木材样品是窑干的,没有边材和主要的视觉缺陷,表面是新鲜的。测定了各木材样品的表观密度和含水率。然后使用KayuSort对样品进行成像和颜色分类,KayuSort是一种使用自组织地图(SOM)算法的内部工业木材颜色分类原型。将Otsu阈值法应用于多个不同的色彩空间分量来获得特征。采用多元回归方法得到木材密度的估计方程。使用决定系数(\(\hbox {R}^{2}\))和95 \(\%\)一致限(LoA)来评估绩效。使用KayuSort对数据集进行平均\(\text {YC}_b{\text{C}}_r\)颜色空间的颜色分割,得分\(\hbox {R}^{2}\)为0.7109,LoA为±146.8 \(\hbox {kgm}^{-3}\)。因此,使用KayuSort使用木材的颜色特征来估计LRM的密度是可能的,需要注意的是木材是窑干到15以下的% MC, freshly surfaced, without major defects and sapwood, and within the thickness range of 26.9 to 30.6 mm.
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning based density estimation of light red meranti (Shorea spp.): a segmented approach to multiple regression of self-organising maps colour clusters using custom made ‘KayuSort’ colour sorting software

Machine learning based density estimation of light red meranti (Shorea spp.): a segmented approach to multiple regression of self-organising maps colour clusters using custom made ‘KayuSort’ colour sorting software

Wood density is an important characteristic of wood which correlates to its strength. This study proposes an algorithm using multiple regression on pre-segmented colour images of the wood to estimate the density of light red meranti (Shorea spp.) (LRM). Two batches of LRM timber were randomly selected from a factory (Batch 1: 119 samples, Batch 2: 79 samples). Timber samples were kiln-dried, free of sapwood and major visual defects, and freshly surfaced 2 sides. The apparent density and moisture content (MC) of each timber sample were measured. The samples were then imaged and colour-sorted using KayuSort, an in-house industrial timber colour sorting prototype that uses the self-organising map (SOM) algorithm. Otsu thresholding was applied to several different colour space components to obtain features. Multiple regression was applied to obtain an equation to estimate the density of the wood. Coefficients of determination (\(\hbox {R}^{2}\)) and 95\(\%\) Limits of Agreement (LoA) were used to assess performance. Performing colour segmentation to the dataset using KayuSort for average \(\text {YC}_b{\text{C}}_r\) colour space scored an \(\hbox {R}^{2}\) of 0.7109 and an LoA of ±146.8 \(\hbox {kgm}^{-3}\). Therefore, it is possible to estimate the density of LRM using colour features of the wood using KayuSort, with the caveat that timber is kiln-dried to under 15% MC, freshly surfaced, without major defects and sapwood, and within the thickness range of 26.9 to 30.6 mm.

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来源期刊
European Journal of Wood and Wood Products
European Journal of Wood and Wood Products 工程技术-材料科学:纸与木材
CiteScore
5.40
自引率
3.80%
发文量
124
审稿时长
6.0 months
期刊介绍: European Journal of Wood and Wood Products reports on original research and new developments in the field of wood and wood products and their biological, chemical, physical as well as mechanical and technological properties, processes and uses. Subjects range from roundwood to wood based products, composite materials and structural applications, with related jointing techniques. Moreover, it deals with wood as a chemical raw material, source of energy as well as with inter-disciplinary aspects of environmental assessment and international markets. European Journal of Wood and Wood Products aims at promoting international scientific communication and transfer of new technologies from research into practice.
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