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湖南农业大学学报(自然科学版) 2017, 43(1):112–116. DOI:10.13331/ki.jhau.2017.01.020 Journal of Hunan Agricultural University (Natural Sciences)投稿网址：基于HSV 空间的玉米果穗性状的检测李伟1，胡艳侠1，吕岑2(1.长安大学信息工程学院，陕西 西安 710064；2.陕西科技大学信息工程学院，陕西 西安 710021)摘 要：为高效检测玉米果穗性状，建立了基于HSV(色调、饱和度、明度值)空间的玉米果穗性状的检测方法：使用机器视觉技术采集绿色背景玉米果穗图像，用HSV 直方图阈值算法去除绿色背景，用FFT 滤波器去除尖锐边缘和噪声，运用粒子滤波分离单一图像中的多个玉米果穗图像，并采用形态学腐蚀方法，经过4次迭代腐蚀，得到玉米果穗中间3行；检测玉米果穗的大小、形状、纹理和颜色4个特征的性状。
随机检测67张玉米果穗样本图像的结果表明，果穗大小和形状特征检测的准确率为100%，果穗颜色和纹理特征检测的准确率分别为98.55%和96.25%，平均每果穗检测时间为0.1 s 。
关 键 词：玉米果穗；图像处理；HSV 颜色空间；二阶矩；最小外接矩形中图分类号：TP274+.3 文献标志码：A 文章编号：1007-1032(2017)01-0112-05Traits detection of corn ear based on HSV color spaceLI Wei 1，HU Yanxia 1，LÜ Cen 2(rmation Engineering College, Chang’an University, Xi’an 710064, China; rmation Engineering College, Shaanxi University of Science and Technology, Xi’an 710021, China)Abstract : In order to meet the high efficient detection of the corn ear quality, a detection method of traits for corn ear were presented based on hue, saturation, value (HSV) color space. Firstly, the corn ear images with green background were acquired by using the machine vision technology, and then remove the green background using HSV histogram threshold algorithm, as well as filtrate sharp edges and noise using FFT filter. The particle filter was used to separate corns in an image. After four iteration corrosion by the corrosion morphology method, the 3 row between the ear of corn was obtained . The size, shape, texture and color characteristics were detected for corn ear. Using this method tested the 67 images of corn ear, the test results show that the testing accuracy of corn ear size and shape feature was 100%, while the ear color and the texture feature detection accuracy rate was 98.55% and 96.25%, respectively. The average detection time of one corn was 0.1 s.Keywords : image processing; corn ear; HSV color space; second moment; the minimum circumscribed rectangle收稿日期：2016–03–16 修回日期：2016–11–05基金项目：国家自然科学基金项目(211024140375) 作者简介：李伟(1981— )，男，陕西咸阳人，博士研究生，副教授，主要从事光电检测、基于图像处理的道路检测研究，235240274@基于计算机视觉技术的玉米果穗性状的检测，可以去除过小、霉变、畸形、破损果穗，大幅度提高玉米果穗的精选效率[1]。
基于机器视觉的玉米果穗参数的图像测量方法刘长青;陈兵旗【期刊名称】《农业工程学报》【年(卷),期】2014(000)006【摘要】The parameters such as the length, the number of ear rows, and the quantity of kernels in an ear of corn were measured during corn breeding and quality studies. It is usually done mainly manually. This research proposes an efficient image processing algorithm to detect the parameters of an ear of corn based on a machine vision. An experimental device was designed to detect the parameters. It mainly included a computer, a module of data acquisition and control, a stepper motor, a stepper motor driver, a PC camera, and other mechanical components. The computer was used to control the stepper motor to rotate the ear of corn and trigger the PC camera to capture images. The image was segmented after the ear of corn was captured. Its contour was traced. The length and the width of it were obtained by measuring the contour. The horizontal and vertical accumulated pixel values histograms were used in this research. One point in the upper edge and one point in the lower edge of the central ear row were found by first searching for the concaves of the horizontal accumulated pixel values histogram in a specified region. All the points in the upper and the lower edges of the central row were obtained by searching for the concaves of the horizontal accumulated pixel valueshistograms in a specified moving region which moved following the edgeof the central ear row direction. So the image of this central ear row was determined. Each gap between the adjacent kernels could be distinguished by searching for the concaves of the vertical accumulated pixel values histogram in the image area of the central ear row. Then the width of the central ear row and the quantity of kernels in this ear row were recorded. The image of the next adjacent ear row was taken while this ear row was rotated to the location in which the former ear row was imaged. The condition of stopping detection was judged by matching the image of the current ear row with the first. So the number of the ear rows was determined. The quantity of the kernels in this ear of corn could be obtained by accumulating the kernels of all ear rows. In this research, an experimental device was designed to detect the parameters of an ear of corn. And an algorithm was supplied on the base of a machine vision for the same purpose. The image of each ear row in the ear of corn was effectively taken with no repeat. The parameters were detected such as the length and the width of the ear of corn, the width of one ear row, the number of ear rows, and the quantity of kernels in the ear of corn. Experiments showed that the measurement accuracy of the length, the width, and the number of the ear rows of the ear of corn was up to 98%. The measurement accuracy of the width of each ear row and the quantityof kernels was up to 95%. The detection speed was about 102 seconds per ear of corn.%在玉米育种和品质研究中，经常需要对玉米的果穗长度、果穗宽度、穗行数、穗粒数等参数进行测量。
22智能考种分析系统在玉米穗部性状表型随着社会经济发展和城市化进程的推进，耕地面积逐年萎缩，端好中国人自己手中的饭碗，向有限的土地要产量是目前农业生产的现状和重任，而产量的提升要求农业工作者不断地推陈出新，培育出更好的稳产、丰产的新品种及研究出更佳的栽培种植方式。
1 智能考种研究品种和栽培方式好坏与成功，需要对收获后的果穗及籽粒进行性状分析即玉米穗部性状[1]分析。
之前科研工作者在每年收获后都是通过人工的方式进行考种，不仅费时、费功、费力而且效率低下，主观误差大且误差标准不一致，目前记录和考察这些参数最好的方法之一就是运用智能考种分析系统，借助先进的科学仪器可以大大缩短工作时间，提高工作效率，避免了人为读数的主观误差和记录、输入时的笔误，使所得数据更为科学准确。
关于智能考种方面的设备设计与图像采集计算，品种、品质分析，在农业工作领域的前辈们已做了不少的设计与研究工作，如北京农业智能装备技术研究中心的宋鹏[2]老师，研究设计了玉米高通量自动考种装置，实现玉米果穗及籽粒考种参数的快速、自动、实时测量；汪珂等[3~5]老师研究设计了玉米籽粒考种装置，通过线阵相机实现了玉米籽粒总粒数、长轴、短轴、长宽比的测量；吴刚等[6]设计了一种玉米果穗自动考种系统，实现了玉米果穗的自动上料、排序、图像采集分析和质量自动称量功能；杨锦忠、周金辉[7~8]等老师在玉米品种品质鉴别、籽粒与果穗性状测试分析方面做了许多研究分析。
这些前辈们所做的一切研究工作为今天更先进、便捷的自动化、智能化考种设备进入实验做了卓越的贡献。
2 测试实验2.1 仪器原理玉米穗部性状主要包括穗长、穗粗、穗型、轴粗、秃尖、穗行数、行粒数、百粒重及玉米的成色和籽粒的饱满度。
该仪器与周金辉、王传宇等老师在获取图片上相同，基于聚焦智能感知、图像识别、算法模型技术，通过光学分辨率为4000×3000 dpi 的高拍仪和光学分辨率为1200×2400 dpi 的扫描仪，对果穗、横截面及籽粒进行图象采集、识别、分析，感知数据采集等技术，同时连接一台千分位天平，将采集到的数据通过算法模块计算后传输到本地电脑，用导出功能将数据以excel 的形式导出到文件中，以供科研工作者分析数据所用。
基于机器视觉的玉米雄穗识别算法茅正冲;刘永娟【摘要】A corn tassel segmentation algorithm is proposed for corn field large area images to determine corn tasseling stage automatically. Firstly,a red green blue(RGB)image is converted to the YCbCr space,Cb and Cr component images are enhanced respectively;then a Fisher classifier is trained to classify the Cb and Cr value of each pixel and corn tassels are segmented preliminary;next,a new color index excess blue index( ExB) is used to gray the RGB image,and the gray image is clustered by an improved Kmeans;lastly,the Fisher classification results and clustering results are combined to determine final corn tassel pixels. Experimental results show that this algorithm can identify corn tassels effectively,fault rate and recall rate of a normal environment are 0. 177% and 0. 831% respectively,fault rate and recall rate of a drought environment are 0. 141% and 0. 811%respectively,this algorithm is robust for maize growth environments.%为了根据田间图像自动判断玉米抽雄期，提出了1种玉米雄穗分割方法。
