计算机视觉技术在玉米种子自动检测中的应用
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摘要
玉米种子的品质关系到后期玉米种植收获的好坏,也是玉米收获后的重要加工环节,一般在收获的玉米中挑选出品质优良的作为种子。利用计算机视觉对玉米种子品质进行挑选,具有效率高、准确度高的优势,可代替传统的人工作业,节省了大量劳动力。计算机视觉技术是通过对玉米种子采集的图像进行格式转换、图像变换、图像直方图信息统计、图像增强、图像分割及形态学处理等一系列图像预处理技术,并提取有效图像进行分析验算。计算机视觉技术对玉米种子品质的研究主要表现在玉米种子的品种、纯度、活力、裂纹等方面的检测,本文对玉米种子的检测智能化进行了阐述,为后期研究提供参考。
Corn quality is related to the quality of late corn harvested harvest is good,but also an important post-harvest processing of corn,harvested corn picked out of good quality as a seed.The use of computer vision to select the quality of corn seeds has the advantages of high efficiency and high accuracy,which can replace the traditional artificial division and save a lot of labor.Computer vision technology is a series of image preprocessing technologies,such as format conversion,image transformation,image histogram statistics,image enhancement,image segmentation,morphological processing and so on,which are collected by the corn seed.Feature extraction valid images are analyzed and checked.The researches on the corn seed quality by computer vision technology mainly study the detection of corn seed variety,purity,vitality,crack and so on,and summarize the detection intelligence of corn seed,and provide references for the later research.
Corn quality is related to the quality of late corn harvested harvest is good,but also an important post-harvest processing of corn,harvested corn picked out of good quality as a seed.The use of computer vision to select the quality of corn seeds has the advantages of high efficiency and high accuracy,which can replace the traditional artificial division and save a lot of labor.Computer vision technology is a series of image preprocessing technologies,such as format conversion,image transformation,image histogram statistics,image enhancement,image segmentation,morphological processing and so on,which are collected by the corn seed.Feature extraction valid images are analyzed and checked.The researches on the corn seed quality by computer vision technology mainly study the detection of corn seed variety,purity,vitality,crack and so on,and summarize the detection intelligence of corn seed,and provide references for the later research.
引文
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[21]司秀丽,吴巍,赵新子,等.玉米种子纯度的计算机图像识别[J].吉林农业大学学报,2005,27(6):694-697.
[22]闰小梅,刘双喜,张春庆,等.基于颜色特征的玉米种子纯度识别[J].农业工程学报,2010,26(S1):46-50.
[23]刘双喜,王盼,张春庆,等.基于优化DBSCAN算法的玉米种子纯度识别[J].农业机械学报,2012,43(4):188-192.
[24]朱晓.玉米高光谱图像的特征提取与分类研究[D].无锡:江南大学,2013.
[25]周红,刘光蓉,管庶安.数字图像处理技术在玉米种子轮廓检测中的应用[J].种子,2004,23(9):90-92.
[26]赵新子,吴巍,司秀丽.玉米种子活力图像识别与处理技术研究[J].吉林农业大学学报,2004,26(5):572-576.
[27]朱文学,曹崇文.玉米应力裂纹的显微分析[J].农业工程学报,1998,14(2):98-202.
[28]GUNASEKARAN S,COOPERT M,BERLAGE A G,et al.Image Processing for Stress Cracks in Corn Kerneis[J].Trans of the ASAE,1987,28(1):266-271.
[29]GIAKOUMIS I,NIKOLAIDIS N,PITAS I.Digitai image processing technigues for the detection and removai of cracks in digitized paintings[J].Transactions on Image Processing,2006,15(1):178-188.
[30]宁纪峰,何东健,杨蜀秦.玉米籽粒的尖端和胚部的计算机视觉识别[J].农业工程学报,2004,20(3):117-119.
[2]Yonekawa S,Sakai N,Kitani O.Three dimensional measurement of loam clods using machine vision[J].Trans ofthe ASAE,1994,37(3):1003-1009.
[3]屈赘,吴玉洁,刘盼.计算机视觉技术在农作物病虫草害防治中的研究进展[J].安徽农业科学,2011,39(9):5570-5571.
[4]鲍官军,荀一,威利勇,等.机器视觉在黄瓜采摘机器人中的应用研究[J].浙江工业大学学报,2010,38(1):114-118.
[5]应义斌,蒋亦元.机器视觉技术在农产品品质自动识别中的应用研究进展[J].农业工程学报,2000,16(3):4-7.
[6]毛璐,赵春江,王开义,等.机器视觉在农产品物流分级检测中的应用[J].农机化研究,2011,33(7):7-13.
[7]宁纪峰.玉米品种的计算机视觉识别研究[D].杨凌:西北农林科技大学,2002.
[8]成芳.应义斌.机器视觉技术在农作物种子质量检验中的应用研究进展[J].农业工程学报,2001,17(6):175-179.
[9]成芳.稻种质量的机器视觉无损检测研究[D].杭州:浙江大学,2004.
[10]阮秋琦.数字图像处理学[M].北京:电子工业出版社,2001:192,199.
[11]何斌,马天予,王运坚,等.Visual C++数字图像处理[M].2版.北京:人民邮电出版社,2002.
[12]Rafacl C.Gonzalez,Richard E.Woods.数字图像处理[M].2版.北京:电子工业出版社,2005.
[13]William K Pratt.数字图像处理[M].北京:机械工业出版社,2005.
[14]SHEN X.Combining dinsceiminant analysis and neural networks for corn variety identification[J].Computers and Electronics in Agriculture,2010,71(S1):48-53.
[15]PEARSON T.Hardware-based image processing for high-speed inspection of grains[J].Computers and Electronics in Agriculture,2009,69;12-18.
[16]郑敏江.基于数字图像处理的玉米种子质量分级方法研究[D].武汉:武汉理工大学,2005.
[17]徐静玉.米种子光电分选技术及装置的研究[D].长春:吉林大学,2009.
[18]曹长虎.李亚非一种二值图像连通区域标记快速算法[J].科学技术与工程,2010,33(10):8167-8170.
[19]张俊雄,荀一,李伟.基于形态特征的玉米种子表面裂纹检测方法[J].光学精密工程,2007,15(6):951-956.
[20]史中辉,赵秀艳,于广洋,等.基于图像处理的玉米种子特征参数提取系统[J].农机化研究,2011,33(10):166-170.
[21]司秀丽,吴巍,赵新子,等.玉米种子纯度的计算机图像识别[J].吉林农业大学学报,2005,27(6):694-697.
[22]闰小梅,刘双喜,张春庆,等.基于颜色特征的玉米种子纯度识别[J].农业工程学报,2010,26(S1):46-50.
[23]刘双喜,王盼,张春庆,等.基于优化DBSCAN算法的玉米种子纯度识别[J].农业机械学报,2012,43(4):188-192.
[24]朱晓.玉米高光谱图像的特征提取与分类研究[D].无锡:江南大学,2013.
[25]周红,刘光蓉,管庶安.数字图像处理技术在玉米种子轮廓检测中的应用[J].种子,2004,23(9):90-92.
[26]赵新子,吴巍,司秀丽.玉米种子活力图像识别与处理技术研究[J].吉林农业大学学报,2004,26(5):572-576.
[27]朱文学,曹崇文.玉米应力裂纹的显微分析[J].农业工程学报,1998,14(2):98-202.
[28]GUNASEKARAN S,COOPERT M,BERLAGE A G,et al.Image Processing for Stress Cracks in Corn Kerneis[J].Trans of the ASAE,1987,28(1):266-271.
[29]GIAKOUMIS I,NIKOLAIDIS N,PITAS I.Digitai image processing technigues for the detection and removai of cracks in digitized paintings[J].Transactions on Image Processing,2006,15(1):178-188.
[30]宁纪峰,何东健,杨蜀秦.玉米籽粒的尖端和胚部的计算机视觉识别[J].农业工程学报,2004,20(3):117-119.