基于x射线成像分析的烟草条茎含量无损检测

Wenkui Zhu, Hongkun Liu, Bo Zhou, Meizhou Ding, Bing Wang, Bin Liu
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引用次数: 0

摘要

对于卷烟生产中使用的烟草条,茎含量是评价切叶杂质水平的重要质量指标。本文提出了一种利用低能x射线成像技术对农产品叶片茎部进行无损检测的方法。建立了主干图像处理算法和权重计算原理,并搭建了具有x射线成像和图像分析功能的机器视觉系统,验证了定量检测方法。结果表明,该检测方法的相对误差范围为−3.64% ~ 2.76%。在图像分析的基础上,实现了粗茎等不同形态茎的识别。粗茎和长茎的鉴定准确率分别为94.67%和99.33%。在相同检测条件下,该方法在准确度和效率上均优于现行的ISO烟叶茎部检测方法,可作为卷烟加工烟叶质量在线监测的有效方法。
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Nondestructive Detection of Stem Content in Tobacco Strips Using X-Ray Imaging Analysis
Summary For tobacco strips used in cigarette production, the stem content is an important quality index to assess the impurity level of the cut leaves. The presented work developed a nondestructive detection method of stems in cut leaf agricultural products by the low energy X-ray imaging. The algorithm of stem image processing and weight calculation principle was established, and then a machine vision system with X-ray imaging and image analysis was set up to verify the quantitative detection method. The results showed that the relative error of the detection method ranged from −3.64% to 2.76%. The determination of stems with a different morphology, such as the thick stem, were also realized based on the image analysis. The accuracy of determining thick stem and long stem was 94.67% and 99.33%, respectively. The developed method is superior to the current ISO detection method of tobacco stem in leaves under the same testing conditions in terms of accuracy and efficiency, which could be applied as an effective online detection method to monitor the quality of processed leaf for cigarette production.
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