基于Android的作物冠层抓拍传输及其图像自动管理系统开发
发布时间：2018-10-14 14:16
【摘要】：随着智能手机的普及,移动互联网的影响已经深入到人们生活和生产的方方面面。目前的农业使用的图像及相关信息采集传输和管理系统存在获取的图片数量过大、无法自动识别系统相关图片以及检索效率低等问题。为了满足现代农业生产的实际需要,帮助农业专家提高工作和研究效率,本文设计并实现了基于Android作物冠层图像采集传输及其WEB端管理系统。该系统先利用Android智能手机对图像及相关信息进行采集并上传到云服务器,然后通过非作物冠层图像自动识别分类模块对上传图像进行自动识别分类,最后通过web端系统对所有上传图像及相关信息进行管理。论文主要研究工作如下:(1)介绍了基于Android作物冠层图像采集传输及其WEB端管理系统的研究意义和国内外具体研究现状。阐述了各研究中所用到的方法及他们所取得的成果,总结目前研究存在的一些问题,确定本文研究的主要目标。(2)介绍了Android软件结构和框架,并对图像及其相关信息数据网络传输相关技术进行研究和比较,并从中选择本系统的传输方案。接着阐述了服务器端SSM框架,分析比较了其相对其他框架的优势。然后介绍了数据库sharding策略原理,最后介绍了浅层学习和深度学习的区别,并比较了现有的图像机器学习服务。(3)本文的重点和创新点是研究非作物图像识别实现和应用,使用计算图像绿色特征像素覆盖率的方法作为第一轮判断依据。先对上传图像处理光照影响,通过改进的超绿色法得到目标强化后的灰度图,使用黄氏模糊阈值算法将灰度图二值化为目标,然后结合连通域标记算法和面积过滤法填充背景中的杂物和目标物中的微小孔洞,最后计算出绿色特征像素覆盖率,比较实验结果得出通过以上步骤完整处理的相对覆盖率误差最小。通过微软图像深度学习服务对上传图片的返回值和置信度作为第二轮判断依据,根据对各类别图片进行实验,选取判定上传图片为作物图像的返回值及其置信度。通过以上两轮筛选能够准确判断上传图片是否为对于系统有效的作物冠层图像。在服务器接收端使用线程池,能够实现多个Android客户端同时上传图像及相关数据到服务器。针对防止恶意占用服务器端口线程的问题通过对连接服务器的IP地址进行检测和计数解决。(4)根据需求对基于Android作物冠层图像采集传输及其WEB端管理系统各模块功能和流程进行设计,完成了系统从服务器的数据库到业务逻辑的实现、页面的展示和系统各部分的交互,最后通过系统测试得出本系统各部分能达到预期效果,在服务器接收端能够自动识别分类非作物冠层图片。
[Abstract]:With the popularity of smart phones, the influence of mobile Internet has reached every aspect of people's life and production. In the current agricultural image collection, transmission and management system, the number of images obtained is too large to automatically identify the related images and the retrieval efficiency is low. In order to meet the practical needs of modern agricultural production and to help agricultural experts improve their work and research efficiency, this paper designs and implements a crop canopy image acquisition and transmission system based on Android and its WEB terminal management system. The system first uses Android smart phone to collect images and related information and upload them to the cloud server, and then automatically recognizes and classifies the uploaded images through the automatic recognition and classification module of non-crop canopy images. Finally, all the uploaded images and related information are managed through the web system. The main research work of this paper is as follows: (1) the research significance of crop canopy image acquisition and transmission and its WEB terminal management system based on Android are introduced, as well as the specific research status at home and abroad. This paper expounds the methods used in each research and their achievements, summarizes some problems existing in the present research, and determines the main objectives of this paper. (2) the structure and framework of Android software are introduced. The related technology of network transmission of image and related information data is studied and compared, and the transmission scheme of this system is selected. Then the server-side SSM framework is expounded, and its advantages compared with other frameworks are analyzed and compared. Then it introduces the principle of database sharding strategy, finally introduces the difference between shallow learning and deep learning, and compares the existing image machine learning services. (3) the emphasis and innovation of this paper is to study the realization and application of non-crop image recognition. The method of calculating image green feature pixel coverage is used as the basis of the first round of judgment. Firstly, the effect of illumination on the uploaded image is processed, then the enhanced gray image is obtained by the improved super-green method, and the binary value of the gray image is transformed into the target by using Huang's fuzzy threshold algorithm. Then the connected domain labeling algorithm and the area filter method are used to fill the debris in the background and the tiny holes in the object. Finally, the green feature pixel coverage is calculated. By comparing the experimental results, it is concluded that the relative coverage error can be minimized by the complete processing of the above steps. The return value and confidence degree of uploaded images are used as the second judgment basis through the Microsoft Image depth Learning Service. According to the experiments of each kind of images, the returned value and confidence of the uploaded images are selected as crop images. Through the above two rounds of screening, we can accurately judge whether the uploaded image is an effective crop canopy image for the system. The thread pool is used in the receiving end of the server, and multiple Android clients can upload images and related data to the server at the same time. The problem of preventing the malicious occupation of server port thread is solved by detecting and counting the IP address of the connection server. (4) according to the requirement, every module of the crop canopy image collection and transmission based on Android and its WEB end management system is realized. Able to design and process, The realization of the system from the database of the server to the business logic, the display of the page and the interaction of the various parts of the system are completed. Finally, through the system test, it is concluded that each part of the system can achieve the desired results. Can automatically identify and classify non-crop canopy images at the server receiving end.
【学位授予单位】：浙江理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP315
[Abstract]:With the popularity of smart phones, the influence of mobile Internet has reached every aspect of people's life and production. In the current agricultural image collection, transmission and management system, the number of images obtained is too large to automatically identify the related images and the retrieval efficiency is low. In order to meet the practical needs of modern agricultural production and to help agricultural experts improve their work and research efficiency, this paper designs and implements a crop canopy image acquisition and transmission system based on Android and its WEB terminal management system. The system first uses Android smart phone to collect images and related information and upload them to the cloud server, and then automatically recognizes and classifies the uploaded images through the automatic recognition and classification module of non-crop canopy images. Finally, all the uploaded images and related information are managed through the web system. The main research work of this paper is as follows: (1) the research significance of crop canopy image acquisition and transmission and its WEB terminal management system based on Android are introduced, as well as the specific research status at home and abroad. This paper expounds the methods used in each research and their achievements, summarizes some problems existing in the present research, and determines the main objectives of this paper. (2) the structure and framework of Android software are introduced. The related technology of network transmission of image and related information data is studied and compared, and the transmission scheme of this system is selected. Then the server-side SSM framework is expounded, and its advantages compared with other frameworks are analyzed and compared. Then it introduces the principle of database sharding strategy, finally introduces the difference between shallow learning and deep learning, and compares the existing image machine learning services. (3) the emphasis and innovation of this paper is to study the realization and application of non-crop image recognition. The method of calculating image green feature pixel coverage is used as the basis of the first round of judgment. Firstly, the effect of illumination on the uploaded image is processed, then the enhanced gray image is obtained by the improved super-green method, and the binary value of the gray image is transformed into the target by using Huang's fuzzy threshold algorithm. Then the connected domain labeling algorithm and the area filter method are used to fill the debris in the background and the tiny holes in the object. Finally, the green feature pixel coverage is calculated. By comparing the experimental results, it is concluded that the relative coverage error can be minimized by the complete processing of the above steps. The return value and confidence degree of uploaded images are used as the second judgment basis through the Microsoft Image depth Learning Service. According to the experiments of each kind of images, the returned value and confidence of the uploaded images are selected as crop images. Through the above two rounds of screening, we can accurately judge whether the uploaded image is an effective crop canopy image for the system. The thread pool is used in the receiving end of the server, and multiple Android clients can upload images and related data to the server at the same time. The problem of preventing the malicious occupation of server port thread is solved by detecting and counting the IP address of the connection server. (4) according to the requirement, every module of the crop canopy image collection and transmission based on Android and its WEB end management system is realized. Able to design and process, The realization of the system from the database of the server to the business logic, the display of the page and the interaction of the various parts of the system are completed. Finally, through the system test, it is concluded that each part of the system can achieve the desired results. Can automatically identify and classify non-crop canopy images at the server receiving end.
【学位授予单位】：浙江理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP315
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