利用人工神经网络的监督图像分类法估算桉木堆垛的换算系数

IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES
Vinicius Andrade de Barros, Carlos Pedro Boechat Soares, Gilson Fernandes Da Silva, Gianmarco Goycochea Casas, Helio Garcia Leite
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引用次数: 0

摘要

堆放的木材以店面为单位进行量化,然后用转换系数进行调整,以立方米为单位估算体积,这对南美洲的木材贸易非常重要。然而,精确测量大量木材是一项挑战。数字图像处理和人工智能的进步提供了前景广阔的解决方案,使这一领域的研究越来越具有吸引力。本研究旨在利用人工神经元网络(ANN)对图像进行监督分类,从而估算叠层桉树木材的换算系数。为此,我们使用了涉及 30 堆木材的实验测量数据和照片。转换系数是通过应用等距点和人工神经元网络的摄影方法确定的,随后与人工方法观察到的数值进行验证。与等距点法相比,方差网络法得出的换算系数估算值更为精确。即使使用低分辨率数码照片,约 97% 的方差网络估算值误差也在±1% 范围内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conversion Factor Estimation of Stacked Eucalypt Timber Using Supervised Image Classification with Artificial Neural Networks
Stacked timber is quantified in-store units and then adjusted with a conversion factor for volume estimation in cubic meters, which is important for the wood trade in South America. However, measuring large quantities accurately can be challenging. Digital image processing and artificial intelligence advancements offer promising solutions, making research in this area increasingly attractive. This study aims to estimate conversion factors of stacked Eucalyptus grandis timber using supervised image classification with Artificial Neuronal Network (ANN). Measured data and photographs from an experiment involving thirty stacks of timber were used to achieve this. The conversion factor was determined using photographic methods that involved the applications of equidistant points and ANN and subsequently validated with values observed through the manual method. The ANN method produced more accurate conversion factor estimates than the equidistant points method. Approximately 97% of the ANN estimates were within the ±1% error class, even when using low-resolution digital photographs.
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来源期刊
Pertanika Journal of Science and Technology
Pertanika Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
1.50
自引率
16.70%
发文量
178
期刊介绍: Pertanika Journal of Science and Technology aims to provide a forum for high quality research related to science and engineering research. Areas relevant to the scope of the journal include: bioinformatics, bioscience, biotechnology and bio-molecular sciences, chemistry, computer science, ecology, engineering, engineering design, environmental control and management, mathematics and statistics, medicine and health sciences, nanotechnology, physics, safety and emergency management, and related fields of study.
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