CAJ | 학술논문

为了实现天白花菇、白花菇、茶花菇和光面菇这4种类型香菇的分选，研究了多种菌盖纹理模型以及各个模型参量的融合，并设计了整个香菇类型自动分选系统。首先从香菇菌盖中截取合适大小的纹理区域，利用灰度直方图统计，灰度共生矩阵（grey level co-occurrence matrix），高斯马尔科夫随机场（Gauss Makov Random Field）模型和分形维数模型从该区域中共提取23个纹理特征参数。然后使用顺序前向搜索法对各个模型特征数据进行融合，从中得出6个简约特征。最后构建K近邻分类器作为香菇类别分类器并对提取后的简约特征进行分类。试验结果表明，香菇类型分选模型的分选正确率可达到93.57%，利用香菇菌盖纹理对香菇进行类型分类是可行的。
위료실현천백화고、백화고、다화고화광면고저4충류형향고적분선，연구료다충균개문리모형이급각개모형삼량적융합，병설계료정개향고류형자동분선계통。수선종향고균개중절취합괄대소적문리구역，이용회도직방도통계，회도공생구진（grey level co-occurrence matrix），고사마이과부수궤장（Gauss Makov Random Field）모형화분형유수모형종해구역중공제취23개문리특정삼수。연후사용순서전향수색법대각개모형특정수거진행융합，종중득출6개간약특정。최후구건K근린분류기작위향고유별분류기병대제취후적간약특정진행분류。시험결과표명，향고류형분선모형적분선정학솔가체도93.57%，이용향고균개문리대향고진행류형분류시가행적。
To achieve the design of an automatic shiitake grading system, the images of four varieties such as Tian pai-hua Shiitake, Pai-hua Shiitake, Tsa-hua Shiitake, and Smooth Cap Shiitake were taken as research objects. Shiitake texture is a vital indicator of shiitake quality. The more white texture of the shiitake pileus, the higher its price. Shiitake grading was mainly processed by a manual operation for a long time. The grading operation was heavy workload, inefficient, and not conducive to automatic production. So the shiitake market was eager for shiitake grading equipment. This study designed an automatic shiitake grading system based on machine vision. The grading system was divided into three subsystems, including a mechanical system, a single chip microcomputer system and a machine vision system. The mechanical system played an important role in the shiitake feeding and grading process. The single chip microcomputer system was responsible for the entire system control and coordination. The machine vision system performed the operation of image acquisition and processing. Texture was a significant image feature. Many experts researched texture across the world, and various texture models had been developed in recent years. This study selected three models to describe pileus texture. The first texture model was derived from a gray histogram and grey level co-occurrence matrix. The second model was called a Gauss Makov Random Field. The third model was defined by fractal dimention. The shiitake grading process was described as follows. First, the texture analysis region was intercepted from shiitake pileus by an appropriate rectangle. Five texture feature parameters were extracted from the texture analysis region according to the gray histogram; another five texture feature parameters were extracted according to grey level co-occurrence matrix; twelve texture feature parameters were extracted according to a Gauss Makov Random Field; the fractal dimension extracted from the fractal model was the last of the texture feature parameters. Three texture models could describe texture information from different perspectives. Each texture feature expressed specific physical meanings. However, it was relevant among texture features in most cases. This study chose a sequential feature selection algorithm to eliminate the defect. An sequential features selection algorithm could remove the correlation among features, and six effective features were selected after the correlation-removal operation. Finally, theK-nearest neighbor’s classifier was constructed as the shiitake species classifier, and then the test shiitake samples could be classified with the six effective features mentioned above by theK-nearest neighbor’s classifier. Experimental results showed that the final accuracy reached to 93.57%, which could meet the requirements of production.