Institute of Image Processing and Pattern Recognition, Henan University, Kaifeng 475001, China.
Sensors (Basel). 2011;11(3):2369-84. doi: 10.3390/s110302369. Epub 2011 Feb 25.
Most of classification, quality evaluation or grading of the flue-cured tobacco leaves are manually operated, which relies on the judgmental experience of experts, and inevitably limited by personal, physical and environmental factors. The classification and the quality evaluation are therefore subjective and experientially based. In this paper, an automatic classification method of tobacco leaves based on the digital image processing and the fuzzy sets theory is presented. A grading system based on image processing techniques was developed for automatically inspecting and grading flue-cured tobacco leaves. This system uses machine vision for the extraction and analysis of color, size, shape and surface texture. Fuzzy comprehensive evaluation provides a high level of confidence in decision making based on the fuzzy logic. The neural network is used to estimate and forecast the membership function of the features of tobacco leaves in the fuzzy sets. The experimental results of the two-level fuzzy comprehensive evaluation (FCE) show that the accuracy rate of classification is about 94% for the trained tobacco leaves, and the accuracy rate of the non-trained tobacco leaves is about 72%. We believe that the fuzzy comprehensive evaluation is a viable way for the automatic classification and quality evaluation of the tobacco leaves.
大多数烤烟烟叶的分类、质量评价或分级都是手动操作的，这依赖于专家的判断经验，不可避免地受到个人、身体和环境因素的限制。因此，分类和质量评价是主观的和基于经验的。本文提出了一种基于数字图像处理和模糊集理论的烟叶自动分类方法。开发了一种基于图像处理技术的分级系统，用于自动检查和分级烤烟烟叶。该系统使用机器视觉提取和分析颜色、大小、形状和表面纹理。模糊综合评价基于模糊逻辑为决策提供高度的置信度。神经网络用于估计和预测烟叶在模糊集中的特征的隶属函数。两级模糊综合评价（FCE）的实验结果表明，对于经过训练的烟叶，分类准确率约为 94%，对于未经训练的烟叶，准确率约为 72%。我们相信模糊综合评价是烟叶自动分类和质量评价的一种可行方法。
