农作物籽粒的图像处理和识别方法研究
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摘要
利用计算机视觉技术检测农作物籽粒外观特征并识别其品种，对提高农业生产自动化水平和农产品增值效益、加强我国农业在国际市场上的竞争优势具有重要意义。基于这样的背景，本文选择我国广泛种植的大米、玉米等作物籽粒的静态图像作为研究对象，重点研究了这些作物籽粒的图像分割技术、形态结构特征提取和分类方法，设计和开发了基于机器视觉的作物籽粒外观特征检测和识别系统。主要工作包括：
(1)采用迭代阈值法将玉米和大米籽粒从背景中提取，对籽粒内部的孔洞进行了区域填充，在此基础上对粘连籽粒的图像分割方法进行了研究。
(2)为解决分水岭算法对一个米粒经距离变换后常存在多个局部极小值而产生的过分割问题，提出了一种基于极小值合并的分水岭分割连接籽粒算法。应用形态学膨胀将邻近的局部极小值合并成一个区域，使每个米粒内部只有一个局部极小值，再用分水岭算法进行分割．对长江米、圆江米、粳米和黑米4个大米品种籽粒，提出的算法分割正确率分别为87％、93％、92％和89%。提出的方法应用于连接玉米籽粒的分割，也取得了较好的结果。
(3)提出了一种基于主动轮廓模型的分割算法。以距离变换合并后的局部极小值区域边界为初始曲线，在主动轮廓模型的指导下，曲线向籽粒的边界演化,最终将图像中各个作物籽粒分割。对长江米、圆江米、粳米和黑米4个大米品种籽粒的试验表明,该算法分割连接米粒图像的正确率分别达到88.0%、93.4%、92.4%和90.4%。
(4)为了对籽粒进行破损检测、品种识别等工作，提取了反映籽粒形态结构特征的一组参数来衡量籽粒的尺寸、形状和颜色。并依据大米国家标准，提出利用计算机视觉方法，对长江米、圆江米、粳米、红香米、泰国香米和黑米等6个品种的大米平均长度进行测算。
(5)尖端是许多农作物籽粒一个重要的外观特征。提出了一种基于Harris角点检测的尖端识别方法。Harris算子在一个局部区域中检测角特征，作为籽粒最明显的角结构，尖端在Harris角点检测中具有最大的响应值，并且对尖端不明显的籽粒也有着良好的响应。通过对玉米、南瓜和西葫芦等具有尖端特征的750粒籽粒的测试结果表明，提出算法的尖端检测综合准确率为95.6%。
(6)提出了玉米冠顶和胚部的分离算法。首先在定位玉米尖端的基础上，从尖端处沿着籽粒的轮廓延伸出圆形区域用来逼近果柄区域，并将该区域去掉。其次利用单通道的线性组合R+B-G和G+B-R，将彩色多通道图像转成2幅新的特征图像，再通过迭代阈值法得到它们的二值图像，并取其交集，在经过形态学优化后，分离出玉米胚部。
(7)在分析完整大米和破损大米粒形区别的基础上，选取面积、偏心率、圆形度、长短轴比和3个不变矩作为特征，利用支持向量机方法，检测破损大米。对长江米、圆江米和粳米的检测准确率分别达到100%、94%和92%。利用提取的玉米籽粒特征，基于支持向量机，检测破损玉米，综合准确率达92.5%。
(8)提出了一种基于稀疏表示的大米品种识别方法。以6种大米籽粒图像和4种玉米籽粒图像作为研究对象，采用颜色和形态结构参数表示单个籽粒。由训练样本组成稀疏表示方法的数据词典，对每一个测试样本，计算其在数据词典上的投影，将具有最小投影误差的类作为测试样本所属的品种。对于13种特征参数表示的大米品种和16种特征参数表示的玉米品种的识别结果表明，提出的方法的综合准确率分别为99.6%和88%，获得了良好的分类效果。
(9)基于MATLAB语言设计并开发了基于机器视觉的籽粒品质检测及分类系统。系统软件包括文件管理、图像预处理、粘连籽粒分割、特征参数提取、尖端识别、玉米胚部提取和品种识别等内容。
Detecting the appearance characters and identifying the breed for the crop’s grain withcomputer vision is important to improve the agricultural automation, increase theeffectiveness of agricultural production and strengthen the competitive advantage of Chineseagriculture. Based on these reasons, this thesis selects the static images of the kernels of rice,corn and so on as the research objects. The research designs and develops a system based oncomputer vision which could detect the kernels’ appearance characters and recognize them,and the research emphasis includes the image segmentation technology, the extraction ofmorphological features and the identification methods. The major work is as follow:
(1) Extracting the kernels of corn and rice from the background by using the iterativethreshold method and Filling the hole in the kernel. On these base，some segmentationmethods for the occluded kernels are studied.
(2) In order to solve the problem of the over-segmentation of the rice kernel induced byseveral local minima after distance transform by watershed algorithm, an improved methodbased on the result of distance transform is proposed. The proposed method merges theadjacent local minima points into one region by morphological dilation operator, which makeseach rice kernel only has one local minimum as possible. Then watershed algorithm segmentsthe preprocessed image. The experimental results show that the proposed algorithm getsrespectively accurate segmentation ratio of87%,93and92%and89%for four types of rice.
(3) A segmentation method based on active contour model is proposed. The regionboundary of local minima after distance transform is taken as the initial curve. Under theguidance of active contour model, the curve converges to the edge of the kernel, and eachkernel in the images is segmented finally. The segmentation accuracy for long glutinous rice,round glutinous rice, non-glutinous rice and black rice reached88.0%,93.4%,92.4%and90.4%respectively.
(4) In order to detect the broken kernels and identify the kernels’ breeds, a set ofmorphological parameters of kernels are extracted to measure their size, shape and color.Besides, according to the national standard of rice, the average lengths of long glutinous rice，round glutinous rice, non-glutinous rice, red aromatic rice, Thailand aromatic rice and blackrice are estimated.
(5) The tipcap is an important appearance character of many crop kernels. A tipcapidentification algorithm based on Harris corner detector is developed. Harris algorithm detectsthe corner within a local region. As the most obvious corner structure, the tipcap has the maximal response of Harris corner detector, which makes it can also detect the kernelswithout the obvious tipcap accurately. The kernels with tipcap of corn, pumpkin and summersquash are selected for test. The experimental results on750kernels show that the proposedmethod gets overall accuracy of95.6%for tipcap of these kernels.
(6) An algorithm of departing the embryo part and non-embryo part of the corn kernelis proposed. Firstly, on the base of location of the tipcap of the corn kernel, a triangle regionextended from the tipcap is used to approximate the fruit stalk, and this region is deletedconsequently. Then linear combinations of single color channel R+B-G and G+B-R are usedto change the color image of multi-channels to two new feature image, and their binaryimages are get with the iterative threshold method. The embryo part of corn kernel isextracted from intersection set of the two binary images after morphological optimizing.
(7) On the base of distinguishing analysis of whole kernels and broken kernels, theparameters of area, eccentricity, circularity, the proportion between the longer axis and theshorter and three invariant moments are selected as characters to detect broken rice by usingSVM. The detected accuracies about long glutinous rice, round glurinous rice andnon-glutinous rice are100%,94%and92%respectively. The broken corn kernels are detectedbased on SVM, and the overall accuracy is92.5%.
(8) An identification method based on sparse representation was proposed fordiscriminating varieties of rice and corn precisely. The rice images of six varieties and thecorn images of four varietes were taken as the research objects. To represent single rice kernel,its color and morphological characters were extracted. All training samples make up the datadictionary of the sparse representation. For each variety, the projection of the testing sampleon the data dictionary is calculated. The breed, which had the minimum projection error,would be regarded as the right kind of rice which the testing sample belonged to.Experimental results demonstrated that the overall identification accuracy of the proposedalgorithm for the six rice breeds expressed with13features and the four corn breedsexpressed with16features were99.6%and88%respectively.
(9) A system based on machine vision is designed and developed by using MATLABlanguage, which could detect the kernels’ appearance quality and classify them. The softwares of this system is composed of file managing, image preprocessing, segmenting theoccluding kernels, tipcap recognizing, extracting the embryo part of the corn kernel, breedidentification and so on.
(1)采用迭代阈值法将玉米和大米籽粒从背景中提取，对籽粒内部的孔洞进行了区域填充，在此基础上对粘连籽粒的图像分割方法进行了研究。
(2)为解决分水岭算法对一个米粒经距离变换后常存在多个局部极小值而产生的过分割问题，提出了一种基于极小值合并的分水岭分割连接籽粒算法。应用形态学膨胀将邻近的局部极小值合并成一个区域，使每个米粒内部只有一个局部极小值，再用分水岭算法进行分割．对长江米、圆江米、粳米和黑米4个大米品种籽粒，提出的算法分割正确率分别为87％、93％、92％和89%。提出的方法应用于连接玉米籽粒的分割，也取得了较好的结果。
(3)提出了一种基于主动轮廓模型的分割算法。以距离变换合并后的局部极小值区域边界为初始曲线，在主动轮廓模型的指导下，曲线向籽粒的边界演化,最终将图像中各个作物籽粒分割。对长江米、圆江米、粳米和黑米4个大米品种籽粒的试验表明,该算法分割连接米粒图像的正确率分别达到88.0%、93.4%、92.4%和90.4%。
(4)为了对籽粒进行破损检测、品种识别等工作，提取了反映籽粒形态结构特征的一组参数来衡量籽粒的尺寸、形状和颜色。并依据大米国家标准，提出利用计算机视觉方法，对长江米、圆江米、粳米、红香米、泰国香米和黑米等6个品种的大米平均长度进行测算。
(5)尖端是许多农作物籽粒一个重要的外观特征。提出了一种基于Harris角点检测的尖端识别方法。Harris算子在一个局部区域中检测角特征，作为籽粒最明显的角结构，尖端在Harris角点检测中具有最大的响应值，并且对尖端不明显的籽粒也有着良好的响应。通过对玉米、南瓜和西葫芦等具有尖端特征的750粒籽粒的测试结果表明，提出算法的尖端检测综合准确率为95.6%。
(6)提出了玉米冠顶和胚部的分离算法。首先在定位玉米尖端的基础上，从尖端处沿着籽粒的轮廓延伸出圆形区域用来逼近果柄区域，并将该区域去掉。其次利用单通道的线性组合R+B-G和G+B-R，将彩色多通道图像转成2幅新的特征图像，再通过迭代阈值法得到它们的二值图像，并取其交集，在经过形态学优化后，分离出玉米胚部。
(7)在分析完整大米和破损大米粒形区别的基础上，选取面积、偏心率、圆形度、长短轴比和3个不变矩作为特征，利用支持向量机方法，检测破损大米。对长江米、圆江米和粳米的检测准确率分别达到100%、94%和92%。利用提取的玉米籽粒特征，基于支持向量机，检测破损玉米，综合准确率达92.5%。
(8)提出了一种基于稀疏表示的大米品种识别方法。以6种大米籽粒图像和4种玉米籽粒图像作为研究对象，采用颜色和形态结构参数表示单个籽粒。由训练样本组成稀疏表示方法的数据词典，对每一个测试样本，计算其在数据词典上的投影，将具有最小投影误差的类作为测试样本所属的品种。对于13种特征参数表示的大米品种和16种特征参数表示的玉米品种的识别结果表明，提出的方法的综合准确率分别为99.6%和88%，获得了良好的分类效果。
(9)基于MATLAB语言设计并开发了基于机器视觉的籽粒品质检测及分类系统。系统软件包括文件管理、图像预处理、粘连籽粒分割、特征参数提取、尖端识别、玉米胚部提取和品种识别等内容。
Detecting the appearance characters and identifying the breed for the crop’s grain withcomputer vision is important to improve the agricultural automation, increase theeffectiveness of agricultural production and strengthen the competitive advantage of Chineseagriculture. Based on these reasons, this thesis selects the static images of the kernels of rice,corn and so on as the research objects. The research designs and develops a system based oncomputer vision which could detect the kernels’ appearance characters and recognize them,and the research emphasis includes the image segmentation technology, the extraction ofmorphological features and the identification methods. The major work is as follow:
(1) Extracting the kernels of corn and rice from the background by using the iterativethreshold method and Filling the hole in the kernel. On these base，some segmentationmethods for the occluded kernels are studied.
(2) In order to solve the problem of the over-segmentation of the rice kernel induced byseveral local minima after distance transform by watershed algorithm, an improved methodbased on the result of distance transform is proposed. The proposed method merges theadjacent local minima points into one region by morphological dilation operator, which makeseach rice kernel only has one local minimum as possible. Then watershed algorithm segmentsthe preprocessed image. The experimental results show that the proposed algorithm getsrespectively accurate segmentation ratio of87%,93and92%and89%for four types of rice.
(3) A segmentation method based on active contour model is proposed. The regionboundary of local minima after distance transform is taken as the initial curve. Under theguidance of active contour model, the curve converges to the edge of the kernel, and eachkernel in the images is segmented finally. The segmentation accuracy for long glutinous rice,round glutinous rice, non-glutinous rice and black rice reached88.0%,93.4%,92.4%and90.4%respectively.
(4) In order to detect the broken kernels and identify the kernels’ breeds, a set ofmorphological parameters of kernels are extracted to measure their size, shape and color.Besides, according to the national standard of rice, the average lengths of long glutinous rice，round glutinous rice, non-glutinous rice, red aromatic rice, Thailand aromatic rice and blackrice are estimated.
(5) The tipcap is an important appearance character of many crop kernels. A tipcapidentification algorithm based on Harris corner detector is developed. Harris algorithm detectsthe corner within a local region. As the most obvious corner structure, the tipcap has the maximal response of Harris corner detector, which makes it can also detect the kernelswithout the obvious tipcap accurately. The kernels with tipcap of corn, pumpkin and summersquash are selected for test. The experimental results on750kernels show that the proposedmethod gets overall accuracy of95.6%for tipcap of these kernels.
(6) An algorithm of departing the embryo part and non-embryo part of the corn kernelis proposed. Firstly, on the base of location of the tipcap of the corn kernel, a triangle regionextended from the tipcap is used to approximate the fruit stalk, and this region is deletedconsequently. Then linear combinations of single color channel R+B-G and G+B-R are usedto change the color image of multi-channels to two new feature image, and their binaryimages are get with the iterative threshold method. The embryo part of corn kernel isextracted from intersection set of the two binary images after morphological optimizing.
(7) On the base of distinguishing analysis of whole kernels and broken kernels, theparameters of area, eccentricity, circularity, the proportion between the longer axis and theshorter and three invariant moments are selected as characters to detect broken rice by usingSVM. The detected accuracies about long glutinous rice, round glurinous rice andnon-glutinous rice are100%,94%and92%respectively. The broken corn kernels are detectedbased on SVM, and the overall accuracy is92.5%.
(8) An identification method based on sparse representation was proposed fordiscriminating varieties of rice and corn precisely. The rice images of six varieties and thecorn images of four varietes were taken as the research objects. To represent single rice kernel,its color and morphological characters were extracted. All training samples make up the datadictionary of the sparse representation. For each variety, the projection of the testing sampleon the data dictionary is calculated. The breed, which had the minimum projection error,would be regarded as the right kind of rice which the testing sample belonged to.Experimental results demonstrated that the overall identification accuracy of the proposedalgorithm for the six rice breeds expressed with13features and the four corn breedsexpressed with16features were99.6%and88%respectively.
(9) A system based on machine vision is designed and developed by using MATLABlanguage, which could detect the kernels’ appearance quality and classify them. The softwares of this system is composed of file managing, image preprocessing, segmenting theoccluding kernels, tipcap recognizing, extracting the embryo part of the corn kernel, breedidentification and so on.
引文
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