玉米种子内部机械裂纹特征与识别研究
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摘要
玉米种子脱粒产生的内部损伤即内部机械裂纹,不但使玉米种子抗压强度降低、后续加工过程中易于破碎、储存过程中易吸湿、霉变和产生虫害,而且直接影响种子发芽等,由于肉眼不易发现,内部损伤更具有潜在危害。论文结合国家自然科学基金项目(50675143)和教育部博士点基金项目(200801570007)实施,以内部机械裂纹的玉米种子为研究对象,在发芽试验研究基础上,利用图像处理技术研究玉米种子内部机械裂纹产生与扩展机理、裂纹特征提取和识别方法。
论文主要研究内容与结论如下
(1)初步对裂纹玉米种子进行了发芽试验,发现龟裂对种子发芽和发育的影响最为显著；同时对发芽弱和未发芽的玉米种子进行解剖观察,进一步研究了机械裂纹对玉米种子发芽和发育的影响机理。
(2)采用体视显微镜成像技术观察玉米种子内部机械裂纹的形态结构、生成位置和扩展方向等,得出裂纹主要集中在冠部和背部,并且裂纹大部分仅穿过胚乳,较少伤及种胚。
(3)应用Cottrell位错塞积模型分析玉米种子内部机械裂纹产生机理,采用Griffith能量平衡理论和分形几何理论推导裂纹扩展速度与扩展路径维数计算公式；建立机械裂纹的4种脆性断裂扩展模型并测得它们的分形维数。
(4)针对图像检测过程玉米种子裂纹图像出现模糊和噪声的问世,采用基于小波数据融合的方法得到的新图像峰值信噪比、均方根误差较高、噪声含量少。
(5)针对当前图像边缘检测算法检测精度较低的问题,引入基于分数阶微分和传统一阶微分算子相结合的新模型检测方法,本模型提取边缘特征的准确率较高,且对噪声具有一定的抑制能力,弥补现有边缘检测方法的一些不足。
(6)针对大量玉米种子相互粘连的问题,引入基于主动活动轮廓模型的图像分割新算法,分割准确率为91.5%,并且分割后边界无明显变形。
(7)本文提出基于数据融合的玉米种子内部机械裂纹检测方法。结果表明：基于数据融合的边缘检测均方根误差、熵、峰值信噪比和边缘保持度在图像融合后达到最优的效果；融合后的图像边缘清晰、连续、噪声少。
(8)以玉米种子冠部白色冲击面积和裂纹分形维数为特征,对玉米种子内部机械裂纹进行识别研究,两种方法的识别准确率高；以玉米种子冠部白色冲击面积、裂纹分形维数等特征为参数,采用小波神经网络对机械裂纹进行识别研究。
Internal damage of corn seeds that is internal mechanical cracks is produced by mechanical threshing, it not only make corn seeds compressive strength decreased, processing process easily broken, and storage process easily moisture absorption, mildew and produce pests, so seeds germinate and the activity are influenced, but also is easy to find by naked eye and has more potential hazards. Research contents attach to the National Natural Science Fund (50675143) and Doctoral Fund Project of the Ministry of Education (200801570007). Internal mechanical crack of corn seeds as the research object, based on the study of bud experiment, producing, expanding the mechanism, the extraction and recognition of mechanical cracks were studied by using image processing technology in corns seeds.
The main research contents and conclusion are as follows:
(1) Through germination experiment of corn seeds with cracks, the results showed that the influence of turtle crack on the seed germination and development was the most significant, at the same time weak germination and no sprout of corn seeds were dissected to observe, further study influence mechanism of mechanical crack on germination and development in corn seeds.
(2) The morphology, creative position and propagation direction of the internal stress cracks in corn seeds were observed through stereomicroscope, the results showed that mechanical cracks of corn seeds mainly concentrated in crown and back, and most cracks only passed through the endosperm, less injuryed embryos.
(3) Producing mechanism of inner mechanical cracks of corn seeds was researched by means of cottrell piling up of dislocation theory, and the formula of expanding speed and propagation route dimension of inner mechanical cracks were inferred based on theory of griffith energy balance and fractal geometry, moreover, establish4kinds of brittle fracture extension model of mechanical cracks in corn seeds and compute their fractal dimension.
(4) According to the process of image testing, to solve the blur and noise problem crack images appearing in corn seeds, new images achieved by wavelet data fusion method had higher PSNR and RMS, less noise.
(5) Based on the lower precision of image edge detection algorithm, new edge detection method of couple fractional order differential theory and traditional first order different edge detection operators were introduced. Accuracy rate of the edge character by model extraction was higher and had certain inhibition for noise, besides, made up some shortage of edge detection method existing.
(6) In order to solve mutual stick together of corn seed image, active active contour model of the new image segmentation algorithm was introduced, segmentation of accuracy rate is91.5%, and boundaries had not obvious deformation after partition.
(7) This paper put forward detection method based on data fusion internal mechanical crackof corn seeds. Results showed that RMS, entropy, PSNR and edge keeping degree of the edge detection based on data fusion achieved optimal effect, and image edge was clear, continuous and less noise after merging.
(8) Considering white impact area of corn seeds crown and fractal dimension of cracks as characters, internal mechanical crack of corn seeds implemented identification research, and identification accuracy of the two methods was higher; white impact area of corn seeds and fractal dimension as parameters, identification research for mechanical crack was carried out through the wavelet neural network method.
论文主要研究内容与结论如下
(1)初步对裂纹玉米种子进行了发芽试验,发现龟裂对种子发芽和发育的影响最为显著；同时对发芽弱和未发芽的玉米种子进行解剖观察,进一步研究了机械裂纹对玉米种子发芽和发育的影响机理。
(2)采用体视显微镜成像技术观察玉米种子内部机械裂纹的形态结构、生成位置和扩展方向等,得出裂纹主要集中在冠部和背部,并且裂纹大部分仅穿过胚乳,较少伤及种胚。
(3)应用Cottrell位错塞积模型分析玉米种子内部机械裂纹产生机理,采用Griffith能量平衡理论和分形几何理论推导裂纹扩展速度与扩展路径维数计算公式；建立机械裂纹的4种脆性断裂扩展模型并测得它们的分形维数。
(4)针对图像检测过程玉米种子裂纹图像出现模糊和噪声的问世,采用基于小波数据融合的方法得到的新图像峰值信噪比、均方根误差较高、噪声含量少。
(5)针对当前图像边缘检测算法检测精度较低的问题,引入基于分数阶微分和传统一阶微分算子相结合的新模型检测方法,本模型提取边缘特征的准确率较高,且对噪声具有一定的抑制能力,弥补现有边缘检测方法的一些不足。
(6)针对大量玉米种子相互粘连的问题,引入基于主动活动轮廓模型的图像分割新算法,分割准确率为91.5%,并且分割后边界无明显变形。
(7)本文提出基于数据融合的玉米种子内部机械裂纹检测方法。结果表明：基于数据融合的边缘检测均方根误差、熵、峰值信噪比和边缘保持度在图像融合后达到最优的效果；融合后的图像边缘清晰、连续、噪声少。
(8)以玉米种子冠部白色冲击面积和裂纹分形维数为特征,对玉米种子内部机械裂纹进行识别研究,两种方法的识别准确率高；以玉米种子冠部白色冲击面积、裂纹分形维数等特征为参数,采用小波神经网络对机械裂纹进行识别研究。
Internal damage of corn seeds that is internal mechanical cracks is produced by mechanical threshing, it not only make corn seeds compressive strength decreased, processing process easily broken, and storage process easily moisture absorption, mildew and produce pests, so seeds germinate and the activity are influenced, but also is easy to find by naked eye and has more potential hazards. Research contents attach to the National Natural Science Fund (50675143) and Doctoral Fund Project of the Ministry of Education (200801570007). Internal mechanical crack of corn seeds as the research object, based on the study of bud experiment, producing, expanding the mechanism, the extraction and recognition of mechanical cracks were studied by using image processing technology in corns seeds.
The main research contents and conclusion are as follows:
(1) Through germination experiment of corn seeds with cracks, the results showed that the influence of turtle crack on the seed germination and development was the most significant, at the same time weak germination and no sprout of corn seeds were dissected to observe, further study influence mechanism of mechanical crack on germination and development in corn seeds.
(2) The morphology, creative position and propagation direction of the internal stress cracks in corn seeds were observed through stereomicroscope, the results showed that mechanical cracks of corn seeds mainly concentrated in crown and back, and most cracks only passed through the endosperm, less injuryed embryos.
(3) Producing mechanism of inner mechanical cracks of corn seeds was researched by means of cottrell piling up of dislocation theory, and the formula of expanding speed and propagation route dimension of inner mechanical cracks were inferred based on theory of griffith energy balance and fractal geometry, moreover, establish4kinds of brittle fracture extension model of mechanical cracks in corn seeds and compute their fractal dimension.
(4) According to the process of image testing, to solve the blur and noise problem crack images appearing in corn seeds, new images achieved by wavelet data fusion method had higher PSNR and RMS, less noise.
(5) Based on the lower precision of image edge detection algorithm, new edge detection method of couple fractional order differential theory and traditional first order different edge detection operators were introduced. Accuracy rate of the edge character by model extraction was higher and had certain inhibition for noise, besides, made up some shortage of edge detection method existing.
(6) In order to solve mutual stick together of corn seed image, active active contour model of the new image segmentation algorithm was introduced, segmentation of accuracy rate is91.5%, and boundaries had not obvious deformation after partition.
(7) This paper put forward detection method based on data fusion internal mechanical crackof corn seeds. Results showed that RMS, entropy, PSNR and edge keeping degree of the edge detection based on data fusion achieved optimal effect, and image edge was clear, continuous and less noise after merging.
(8) Considering white impact area of corn seeds crown and fractal dimension of cracks as characters, internal mechanical crack of corn seeds implemented identification research, and identification accuracy of the two methods was higher; white impact area of corn seeds and fractal dimension as parameters, identification research for mechanical crack was carried out through the wavelet neural network method.
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