改进圆形Hough变换的田间红提葡萄果穗成熟度判别  

Maturity discrimination of“Red Globe”grape cluster in grapery by improved circle Hough transform

作　　者：周文静 查志华 吴杰[1,2] Zhou Wenjing;Zha Zhihua;Wu Jie(College of Mechanical and Electrical Engineering,Shihezi University,Shihezi 832003,China;Research Center of Agricultural Mechanization for Economic Crop in Oasis,Ministry of Education,Shihezi 832003,China)

机构地区：[1]石河子大学机械电气工程学院,石河子832003 [2]绿洲特色经济作物生产机械化教育部工程研究中心,石河子832003

出　　处：《农业工程学报》2020年第9期205-213,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金地区科学基金项目(31560476);石河子大学自主资助支持项目(ZZZC201746B)。

摘　　要：针对田间环境下红提葡萄果穗成熟度人眼判断效率低且易误判的问题,该研究采用K近邻(K-nearest neighbor,KNN)算法和最大类间方差(Otsu)法分别对葡萄果穗图像背景分割以找到最佳分割效果,采用圆形Hough变换识别葡萄果粒,并开发了可判别葡萄果穗成熟度的算法。研究结果表明,不论顺光、逆光或者与田间背景相似的绿色果穗,KNN法可实现良好的背景分割,然后圆形Hough变换法在边缘阈值和灵敏度分别取0.15和0.942时,识别葡萄果粒的准确率可达96.56%。在此研究基础上,采用该研究开发的葡萄果穗成熟度判断算法,可根据颜色将果粒划分不同成熟度等级,并实现对果穗成熟度判别,判别准确率为91.14%。该研究结果可为果农适宜期收获葡萄及自动化采摘提供重要指导。There are color differences between different berries of a“Red Globe”cluster in the vineyard in the same period.This makes it inefficient and error-prone for visual maturity judgment of the grape cluster.As a result,inaccurate judgment often leads to grape harvesting too early or too late.Therefore,it is necessary to achieve accurate maturity discrimination of the grape cluster for increasing the quality grade and the commodity rate of the“Red Globe”grape.In this study,79 images of the grape cluster in a grapery were acquired by the smartphone(HUAWEI Mate 10),including 59 images in natural light and 20 images in backlight.Firstly,the background of the grape cluster image was segmented using the K-Near Neighbor(KNN)algorithm and Otsu methods.For the KNN algorithm,2200 sets of R(Red),G(Green)and B(Blue)values were manually collected from the pixel of the image to be used as the data set.With the data set,different nearest numbers and the methods of distance calculation were tested to obtain a better background segmentation effect.For the Otsu method,the normalized color difference of(R-G)/(R+G)was applied as the background segmentation characteristic to reduce the influence of the lights on the R channel and G channel.For near red and green grape clusters under natural light and backlight,the background segmentation effect was compared using two algorithms.After labeling the images of grape clusters with the minimum bounding box,the Log operator was used to extract the edge of the first gradient image from the object region.Then,the Circle Hough Transform(CHT)method was applied to extract grape berries.The radius range of circle in the Hough transform was determined by measuring numbers of pixels of 60 grape berry images.In addition,we adjusted the values of the edge thresholds and sensitivities in Hough transform to obtain a higher accuracy of berry extraction.Meanwhile,the maturity of the grape berry was classified into four levels of G1,G2,G3,and G4 according to the H value of the pixels from the“Red G

关 键 词：图像处理 识别 机器视觉 HOUGH变换 成熟度 果穗 

分 类 号：S371[农业科学—农产品加工] TP274[农业科学—农艺学]