The veneer industry is widely used in many countries of the world. Properties such as obtaining curved surfaces, concealing defects, usability in different designs, and patterns can be counted as the main advantages of veneers compared to solid wood. Although advanced production and quality control systems have developed in coating production, the number of countries where traditional production methods are used is quite high. The biggest problem in transition to advanced systems is investment costs. Therefore, companies allocate low budgets for growth. This study carried out machine vision estimation of different types of veneers obtained by the quarter-cutting and rotary methods. Fifteen different veneers samples were used in the study. Over 923 features were extracted from 75 veneer images to determine the selected features. Artificial neural network and decision tree techniques were used as decision-making algorithms. It was determined that the artificial neural network made predictions with higher accuracy in estimating the veneer type. Rays, annual rings, and light-dark color contrast were among the parameters effective in machine vision prediction. The importance of data is increasing in businesses that are undergoing digital transformation and automation. Today, data are expressed not only in numbers or texts, but also in features extracted from images. With this study, numerical features were extracted from the veneer images and species predictions were made. It was determined that among the algorithms used, the artificial neural network yielded more accurate results than the decision tree technique.

中文翻译：

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使用机器视觉确定一些木单板的种类

单板行业在世界许多国家得到广泛应用。与实木相比，获得曲面、隐藏缺陷、在不同设计中的可用性和图案等特性可以算作单板的主要优势。虽然在涂料生产方面已经发展了先进的生产和质量控制系统，但使用传统生产方法的国家数量相当多。向先进系统过渡的最大问题是投资成本。因此，公司为增长分配了低预算。本研究对通过四分之一切割和旋转方法获得的不同类型的单板进行机器视觉估计。研究中使用了 15 种不同的贴面样品。从 75 张单板图像中提取了超过 923 个特征以确定所选特征。人工神经网络和决策树技术被用作决策算法。确定人工神经网络在估计单板类型时做出了更准确的预测。光线、年轮和明暗颜色对比是机器视觉预测中有效的参数之一。在正在进行数字化转型和自动化的企业中，数据的重要性正在增加。今天，数据不仅用数字或文本表示，还用从图像中提取的特征来表示。通过这项研究，从单板图像中提取了数值特征并进行了物种预测。确定在使用的算法中，人工神经网络比决策树技术产生更准确的结果。