基于云计算的农业图像处理系统设计与算法研究
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摘要
图像处理在农产品检测、农作物病虫草害诊断识别、精准喷药和果实采摘等方面都有着广泛的应用,取得了丰硕的研究成果,对农业各领域的现代化进程起到了十分重要的促进作用。但由于图像处理系统复杂,成本高昂,使用和维护不便,不能适应恶劣的生产环境,许多成果还没有真正被农民所接受,发挥出应有的经济效益和社会效益。如何做到对图像的实时处理的同时降低农民的使用成本是亟需解决的问题。
论文将云计算与农业图像处理相结合,提出了农业图像云构想,构建了农业图像云架构。将云平台的结构分为三个层次,形成以控制层为核心,过渡层作决策判断,算法层执行具体功能的模式。建立了农业图像处理系统的Petri网模型,提出了农业图像处理系统的参数化设计方法,系统可以针对不同的目标设置不同参数进行处理,根据不同目标的不同参数,模型可自动对算法进行调度,并用FPGA对模型进行了实验验证。论文还对图像处理中各种分割、去噪和锐化算法进行了研究,重点研究了这些算法的优化和融合,提高了图像处理的质量。
论文研究的具体内容如下：
(1)为提高农业图像处理的实时性、系统功能的广泛性、操作使用的简便性、工作环境的适应性、使用成本的廉价性,提出了农业图像云的思想并进行了架构设计,将其核心的云平台分为三个层次：控制层、过渡层和算法层。控制层根据用户选择的服务,来调用服务需要的过渡层中的算法过渡模块；过渡层中的算法过渡模块根据服务参数来判断是否被调用,在执行条件满足时,选择算法层中相对应算法群里面空闲的算法模块执行具体的功能；算法层主要是接收过渡层中传递的数据执行算法的功能。此外还建立一个服务参数库,用来存储各个目标的参数信息。
(2)针对农业图像云云平台架构中的控制层与过渡层之间的工作模式,提出参数化农业图像处理系统设计方法。它由用户对系统进行参数设置,选择所需算法,系统将根据用户的设置执行相应的算法,完成用户要求的功能。这种设计方法提高了图像处理系统的灵活性,使系统适用范围更广,适用于云平台大规模分布式并行计算模式的设计,也可以用来设计独立的参数化农业图像处理系统。
(3)为了解决具有并行结构的农业图像处理系统的建模问题,提出农业图像处理系统的Petri网建模方法,构建基于参数的算法调用模式。基于Petri网对参数化农业图像处理系统的顶层控制模块进行建模,并对模型进行验证。论文使用SnoopyIOPT软件将顶层控制模块的Petri网建模图转换为PNML(Petri Net Markup Language)语言文本文件,再通过PNML2VHDL编译生成VHDL语言文件,在QuartusⅡ环境中进行仿真验证,仿真结果波形图说明了模型的正确性。
(4)为了使棉花等绿色农作物图像分割效果更好,提出G-分量彩色图像圆锥分割算法,并推导任意颜色的彩色图像圆锥分割算法公式并简单验证。在色差法的基础上,提出G-分量彩色图像圆锥分割算法。为了限定目标像素灰度值的取值范围,G-分量彩色图像圆锥分割算法与阈值化分割法相融合,提出G-分量彩色图像圆台分割算法。新算法以(0,0,0)点为顶点,G轴为中心轴,r为半径做圆锥,圆锥内部同时在阈值范围内的像素点即为目标像素点。通过直方图统计的方法确定新算法中的未知量,将新算法与色差法和阈值化分割法分割的结果进行比较,结果表明新算法分割效果较好。
(5)基于中值滤波和掩膜消噪法的图像滤波融合算法研究。其中5×5窗口的中值滤波去噪效果好但图像比较模糊,而掩膜消噪法去噪效果较差但图像比较清晰。将5×5窗口的中值滤波和掩膜消噪法通过分配不同权值的方式进行融合,得到新的图像去噪融合算法及结果。选择不同的权值,得到不同的结果。仿真结果表明,权值越大,图像去噪效果越好,图像越模糊,其中权值k=0.6的去噪融合算法得到的图像去噪效果较好且图像较为清晰。
Image processing has been widely used in detection of agricultural products, diagnosis and identification on diseases, pests and weeds of crops, precision spraying and fruit harvesting, etc; it has achieved lots of research results in agricultural field and promotes the development of agricultural modernization. But these achievements haven't been accepted by users because of the complexity and the high cost of image processing system, the inconvenience of use and maintenance, and then they don't give full play in economic benefit and social benefit. So it is a problem to realize the real-time processing of image and make low cost that could be accepted by users.
Agricultural image cloud is proposed by combining the cloud computing and agricultural image processing and then the architecture of agricultural image cloud is designed. The cloud platform of agricultural image cloud is divided into three levels: control layer is the core, transition layer makes the choice and algorithm layer executes the functions. For the purpose of designing the control layer, the parametric design method of agricultural image processing system is proposed, and then the system can set different parameters for different objects. The model of top-level controller of the system is built, and the model can schedule algorithm module automatically according to the parameters of different objects. At last, image segmentation, image de-noising and image sharpening are researched in the paper. The optimization and integration of these algorithms are researched for improving the quality of image processing.
The main research contents are as follows:
(1) Agricultural image cloud is proposed for improving the real-time of the system, the extensiveness of the system functions, the convenience of the operation, the adaptation of the working environment and the cheapness of the using cost, and then the architecture of agricultural image cloud is designed. The cloud platform, as the core of the agricultural image cloud, is divided into three levels:control layer, transition layer and algorithm layer. The control layer schedules the algorithm transition modules which the service needs in the transition layer according to the service that selected by users; the algorithm transition module in the transition layer should judge whether it is scheduled and select the idle algorithm modules in the algorithm group which corresponds with the algorithm transition module to executive the function when the execution conditions are favorable. There is also a service parameter library used to store the parameters of various objects.
(2) For the purpose of designing the control layer in the cloud platform of agricultural image cloud, the parametric design method of agricultural image processing system is proposed. Users can select the algorithms which are needed and set the parameters into the system, and then the system will execute the algorithms according to the parameters to complete the functions that the users requested. The design method improves the flexibility of image processing system and makes the system more widely used; it is applicable to design the large-scale, distributed parallel computing system, such as cloud platform, and it is also can be used to design independent parametric agricultural image processing system.
(3) In order to solve the modeling problem of agricultural image processing system which has a parallel structure, a Petri nets modeling method of algorithm image processing system is proposed and an algorithm call model based on parameters is constructed. The model of top-level controller of the agricultural image processing system based on Petri net is built, it is transferred to PNML by using SnoopylOPT software, and then the VHDL is got through the PNML2VHDL software; at last, the VHDL is simulated in QuartusII and the simulation result shows that the model is correct.
(4) Color image cone segment algorithm based on G-component is proposed for getting better segment results of cotton leaves and other green crops. The paper also proposed color image frustum segment algorithm of any color, reduced the expression and verified it. For the purpose of limiting the range of pixel gray value of the target, the color image frustum segment algorithm based on G-component is proposed by fusing the color image cone segment algorithm based on G-component and the thresholding method. The new algorithm makes a cone, the apex of the cone is (0,0,0), the center axis is G-axis and the radius is r. The pixels which in the cone at the same time the pixel gray value is in the range are the target pixels. The unknown quantities are determined by histogram method. At last, the new algorithm is compared with color difference method and thresholding method. The result shows that the pixel error number is the least and the pixel error rate is the smallest by using the new algorithm.
(5) Fusion algorithm research of image de-noising based on median filter and mask de-noising method. The de-noising effect is good but the image is fuzzy by using median filter5X5; and the de-noising effect is not good but the image is clear by using mask de-noising method. The fusion algorithm is got by fusing the two methods through assigning different weights to the results of the two methods. The result shows that the bigger the k is, the better the de-noising effect is and the fuzzier the image is; the de-noising effect is better and at the same time the image is clearer by using the fusion algorithm which the k is0.6.
论文将云计算与农业图像处理相结合,提出了农业图像云构想,构建了农业图像云架构。将云平台的结构分为三个层次,形成以控制层为核心,过渡层作决策判断,算法层执行具体功能的模式。建立了农业图像处理系统的Petri网模型,提出了农业图像处理系统的参数化设计方法,系统可以针对不同的目标设置不同参数进行处理,根据不同目标的不同参数,模型可自动对算法进行调度,并用FPGA对模型进行了实验验证。论文还对图像处理中各种分割、去噪和锐化算法进行了研究,重点研究了这些算法的优化和融合,提高了图像处理的质量。
论文研究的具体内容如下：
(1)为提高农业图像处理的实时性、系统功能的广泛性、操作使用的简便性、工作环境的适应性、使用成本的廉价性,提出了农业图像云的思想并进行了架构设计,将其核心的云平台分为三个层次：控制层、过渡层和算法层。控制层根据用户选择的服务,来调用服务需要的过渡层中的算法过渡模块；过渡层中的算法过渡模块根据服务参数来判断是否被调用,在执行条件满足时,选择算法层中相对应算法群里面空闲的算法模块执行具体的功能；算法层主要是接收过渡层中传递的数据执行算法的功能。此外还建立一个服务参数库,用来存储各个目标的参数信息。
(2)针对农业图像云云平台架构中的控制层与过渡层之间的工作模式,提出参数化农业图像处理系统设计方法。它由用户对系统进行参数设置,选择所需算法,系统将根据用户的设置执行相应的算法,完成用户要求的功能。这种设计方法提高了图像处理系统的灵活性,使系统适用范围更广,适用于云平台大规模分布式并行计算模式的设计,也可以用来设计独立的参数化农业图像处理系统。
(3)为了解决具有并行结构的农业图像处理系统的建模问题,提出农业图像处理系统的Petri网建模方法,构建基于参数的算法调用模式。基于Petri网对参数化农业图像处理系统的顶层控制模块进行建模,并对模型进行验证。论文使用SnoopyIOPT软件将顶层控制模块的Petri网建模图转换为PNML(Petri Net Markup Language)语言文本文件,再通过PNML2VHDL编译生成VHDL语言文件,在QuartusⅡ环境中进行仿真验证,仿真结果波形图说明了模型的正确性。
(4)为了使棉花等绿色农作物图像分割效果更好,提出G-分量彩色图像圆锥分割算法,并推导任意颜色的彩色图像圆锥分割算法公式并简单验证。在色差法的基础上,提出G-分量彩色图像圆锥分割算法。为了限定目标像素灰度值的取值范围,G-分量彩色图像圆锥分割算法与阈值化分割法相融合,提出G-分量彩色图像圆台分割算法。新算法以(0,0,0)点为顶点,G轴为中心轴,r为半径做圆锥,圆锥内部同时在阈值范围内的像素点即为目标像素点。通过直方图统计的方法确定新算法中的未知量,将新算法与色差法和阈值化分割法分割的结果进行比较,结果表明新算法分割效果较好。
(5)基于中值滤波和掩膜消噪法的图像滤波融合算法研究。其中5×5窗口的中值滤波去噪效果好但图像比较模糊,而掩膜消噪法去噪效果较差但图像比较清晰。将5×5窗口的中值滤波和掩膜消噪法通过分配不同权值的方式进行融合,得到新的图像去噪融合算法及结果。选择不同的权值,得到不同的结果。仿真结果表明,权值越大,图像去噪效果越好,图像越模糊,其中权值k=0.6的去噪融合算法得到的图像去噪效果较好且图像较为清晰。
Image processing has been widely used in detection of agricultural products, diagnosis and identification on diseases, pests and weeds of crops, precision spraying and fruit harvesting, etc; it has achieved lots of research results in agricultural field and promotes the development of agricultural modernization. But these achievements haven't been accepted by users because of the complexity and the high cost of image processing system, the inconvenience of use and maintenance, and then they don't give full play in economic benefit and social benefit. So it is a problem to realize the real-time processing of image and make low cost that could be accepted by users.
Agricultural image cloud is proposed by combining the cloud computing and agricultural image processing and then the architecture of agricultural image cloud is designed. The cloud platform of agricultural image cloud is divided into three levels: control layer is the core, transition layer makes the choice and algorithm layer executes the functions. For the purpose of designing the control layer, the parametric design method of agricultural image processing system is proposed, and then the system can set different parameters for different objects. The model of top-level controller of the system is built, and the model can schedule algorithm module automatically according to the parameters of different objects. At last, image segmentation, image de-noising and image sharpening are researched in the paper. The optimization and integration of these algorithms are researched for improving the quality of image processing.
The main research contents are as follows:
(1) Agricultural image cloud is proposed for improving the real-time of the system, the extensiveness of the system functions, the convenience of the operation, the adaptation of the working environment and the cheapness of the using cost, and then the architecture of agricultural image cloud is designed. The cloud platform, as the core of the agricultural image cloud, is divided into three levels:control layer, transition layer and algorithm layer. The control layer schedules the algorithm transition modules which the service needs in the transition layer according to the service that selected by users; the algorithm transition module in the transition layer should judge whether it is scheduled and select the idle algorithm modules in the algorithm group which corresponds with the algorithm transition module to executive the function when the execution conditions are favorable. There is also a service parameter library used to store the parameters of various objects.
(2) For the purpose of designing the control layer in the cloud platform of agricultural image cloud, the parametric design method of agricultural image processing system is proposed. Users can select the algorithms which are needed and set the parameters into the system, and then the system will execute the algorithms according to the parameters to complete the functions that the users requested. The design method improves the flexibility of image processing system and makes the system more widely used; it is applicable to design the large-scale, distributed parallel computing system, such as cloud platform, and it is also can be used to design independent parametric agricultural image processing system.
(3) In order to solve the modeling problem of agricultural image processing system which has a parallel structure, a Petri nets modeling method of algorithm image processing system is proposed and an algorithm call model based on parameters is constructed. The model of top-level controller of the agricultural image processing system based on Petri net is built, it is transferred to PNML by using SnoopylOPT software, and then the VHDL is got through the PNML2VHDL software; at last, the VHDL is simulated in QuartusII and the simulation result shows that the model is correct.
(4) Color image cone segment algorithm based on G-component is proposed for getting better segment results of cotton leaves and other green crops. The paper also proposed color image frustum segment algorithm of any color, reduced the expression and verified it. For the purpose of limiting the range of pixel gray value of the target, the color image frustum segment algorithm based on G-component is proposed by fusing the color image cone segment algorithm based on G-component and the thresholding method. The new algorithm makes a cone, the apex of the cone is (0,0,0), the center axis is G-axis and the radius is r. The pixels which in the cone at the same time the pixel gray value is in the range are the target pixels. The unknown quantities are determined by histogram method. At last, the new algorithm is compared with color difference method and thresholding method. The result shows that the pixel error number is the least and the pixel error rate is the smallest by using the new algorithm.
(5) Fusion algorithm research of image de-noising based on median filter and mask de-noising method. The de-noising effect is good but the image is fuzzy by using median filter5X5; and the de-noising effect is not good but the image is clear by using mask de-noising method. The fusion algorithm is got by fusing the two methods through assigning different weights to the results of the two methods. The result shows that the bigger the k is, the better the de-noising effect is and the fuzzier the image is; the de-noising effect is better and at the same time the image is clearer by using the fusion algorithm which the k is0.6.
引文
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