农业工程学报
農業工程學報
농업공정학보
2013年
19期
145-151
,共7页
程洪%史智兴%尹辉娟%冯娟%李亚南
程洪%史智興%尹輝娟%馮娟%李亞南
정홍%사지흥%윤휘연%풍연%리아남
种子%图像处理%聚类分析%玉米%胚部特征
種子%圖像處理%聚類分析%玉米%胚部特徵
충자%도상처리%취류분석%옥미%배부특정
seed%image processing%cluster analysis%corn%the characteristics of corn kernel embryo
为了利用机器视觉进行多个玉米种子品种的自动识别,该文提出了一种针对多个玉米籽粒进行胚部检测的方法。该方法基于阈值分割和形态学,在RGB空间,采用自动屏蔽0值像素的大津法(Otsu法),根据B分量值对多粒玉米籽粒扫描图像进行分割,并采用改进的开闭运算对分割后的图像进行修整,最终得到多个玉米籽粒胚部区域。以4个黄玉米品种各45个籽粒为实验对象,以此方法进行胚部检测,为了验证所得胚部区域有效,提取胚部区域面积、周长分别与手工测量的面积、周长进行线性回归分析,R2的均值分别达到0.9834、0.9578;进一步提取所得胚部区域的形状参数,进行聚类识别,不同品种间的差距值反映了不同品种胚部视觉效果上的差异大小,4个品种中1种识别率为97.8%,其余3种均为100%;多个玉米胚部检测较每个籽粒单独处理的效率提高了29.59%。试验结果表明本文提出的多个玉米籽粒胚部检测方法可行。此研究结果为进一步研究玉米籽粒的胚部特征提供了有利条件,也为实现玉米品种的快速准确分类提供了参考。
為瞭利用機器視覺進行多箇玉米種子品種的自動識彆,該文提齣瞭一種針對多箇玉米籽粒進行胚部檢測的方法。該方法基于閾值分割和形態學,在RGB空間,採用自動屏蔽0值像素的大津法(Otsu法),根據B分量值對多粒玉米籽粒掃描圖像進行分割,併採用改進的開閉運算對分割後的圖像進行脩整,最終得到多箇玉米籽粒胚部區域。以4箇黃玉米品種各45箇籽粒為實驗對象,以此方法進行胚部檢測,為瞭驗證所得胚部區域有效,提取胚部區域麵積、週長分彆與手工測量的麵積、週長進行線性迴歸分析,R2的均值分彆達到0.9834、0.9578;進一步提取所得胚部區域的形狀參數,進行聚類識彆,不同品種間的差距值反映瞭不同品種胚部視覺效果上的差異大小,4箇品種中1種識彆率為97.8%,其餘3種均為100%;多箇玉米胚部檢測較每箇籽粒單獨處理的效率提高瞭29.59%。試驗結果錶明本文提齣的多箇玉米籽粒胚部檢測方法可行。此研究結果為進一步研究玉米籽粒的胚部特徵提供瞭有利條件,也為實現玉米品種的快速準確分類提供瞭參攷。
위료이용궤기시각진행다개옥미충자품충적자동식별,해문제출료일충침대다개옥미자립진행배부검측적방법。해방법기우역치분할화형태학,재RGB공간,채용자동병폐0치상소적대진법(Otsu법),근거B분량치대다립옥미자립소묘도상진행분할,병채용개진적개폐운산대분할후적도상진행수정,최종득도다개옥미자립배부구역。이4개황옥미품충각45개자립위실험대상,이차방법진행배부검측,위료험증소득배부구역유효,제취배부구역면적、주장분별여수공측량적면적、주장진행선성회귀분석,R2적균치분별체도0.9834、0.9578;진일보제취소득배부구역적형상삼수,진행취류식별,불동품충간적차거치반영료불동품충배부시각효과상적차이대소,4개품충중1충식별솔위97.8%,기여3충균위100%;다개옥미배부검측교매개자립단독처리적효솔제고료29.59%。시험결과표명본문제출적다개옥미자립배부검측방법가행。차연구결과위진일보연구옥미자립적배부특정제공료유리조건,야위실현옥미품충적쾌속준학분류제공료삼고。
This paper presents a method of multi-corn kernel embryos detection based on threshold segmentation and morphology. Corn kernel varieties identification is of great significance in the fields of agricultural production and crop breeding. In the seed market of China, the identification of corn varieties mainly depends on manual experience and measurement. In order to automatically, quickly, non-destructively identify kernel varieties, the study of automatic identification in a real time using machine vision technology is very active. Determination of the characteristics of the corn kernel is the first and the most important step of automatic identification. The corn kernel embryo is the most important part of the corn kernel. To analyze the characteristics of an embryo, an embryo must be separated from the corn kernel. The embryo detection speed and precision can influence the speed and precision of identification. In the paper, an algorithm based on threshold segmentation and morphology was proposed to segment embryos of multi-corn kernel at the same time, as a result of the deeper study of the identification. This algorithm was used to obtain multi-corn kernel embryos from a 2D digital image obtained by the scanner. It mainly included two parts, i.e. a maximum between-cluster deviation method (Otsu method) excluding pixels with zero value automatically, and improved open-close operation from morphology. Its process was as follow. In RGB color space, the multi-corn kernel embryos in the same image were segmented out at the same time by Otsu excluding pixels with zero value method based on the value of B(blue), in which the zero value pixels were auto-removed form histogram during processing. However, after segmentation, some corn kernel embryos showed a problem of lacking-segmentation or over-segmentation. To solve the problem, the improved open-close operation was used to repair the embryos. To validate the algorithm, four varieties of yellow corn which were common used in China were selected as study objects for our experiments. 45 samples were selected form each variety respectively. Then the total number of samples was 180. Every variety’s digital image was obtained by scanner. Four images were obtained. They were processed respectively with the above-mentioned algorithm. The embryos from each different variety were detected. To validate the effectiveness of the detected embryos, two methods were used. First, area and perimeter of each embryo were measured respectively by machine computer and manual measurement. Linear regression analysis was done between the auto measured values and the manual values. The mean values of R 2 were 0.9834 and 0.9578 respectively. Second, 6 shape-parameters which are perimeter, round degrees, ellipse strong and weak points axis ratio, rectangle degrees, and centrifugal rate were extracted from the embryo regions of 180 samples. Analyzing the data by K-means clustering method, the final clustering distances between different varieties reflected the difference in the visual of the embryos of the different varieties., and the checked out rate of the 4 varieties were 97.8%, 100%, 100%, and 100%. The efficiency of multi-corn kernel embryos detection was improved by 29.59%over single-corn kernel embryo detection. According to the experimental results, two conclusions were as follow:First, the auto-detected embryo region and the embryo region by manual experience and measurement were basically the same. The auto-detected embryo regions were effective. Second, the six parameters extracted from an embryo could be used to characterize the shape of the embryo. The results of this study provide favorable conditions for further study of the embryo characteristics of corn kernels, and provide a reference for the rapid and accurate identification of corn varieties.