【作者】 宗华丽；

【导师】 白中英；

【作者基本信息】 北京邮电大学 ， 计算机应用， 2010， 硕士

【摘要】 随着计算机处理能力的不断增强,以及图像处理与识别技术的快速发展,数字图像处理与识别技术在农业中的应用越来越广泛,并将成为实现农业信息化与自动化的重要技术力量农业信息采集工作量巨大,信息的实时性和准确性是农业生产和科学研究领域普遍关注的问题,如何及时快速地进行农作物病害的准确判断一直是计算机技术面向农业领域研究工作的一项重要内容作物的病虫草害严重影响作物的产量和质量,本文针对作物病虫草害的自动化识别程度低,诊断不准确的问题,以计算机图像处理技术为重要手段,综合运用图像处理和植物病理学方面的知识,在国家自然科学基金项目(303060047)资助下,以常见的玉米叶部病害为研究对象,提出可行的识别方法,提高识别诊断的精度,为作物病虫草害自动识别诊断的相关研究提供理论依据本论文阐述了图像处理与识别的基本理论,使用VC++编程语言建立了玉米叶部病害的诊断识别软件系统课题组选择图像获取设备,搭建硬件检测系统,获取大田环境下的玉米叶部病害图片研究根据玉米叶部病害的特点,综合应用图像平滑阈值法区域标记局部阈值法和区域增长法相结合的算法(TSRG),对玉米叶部病害图片进行分割统计病斑的个数出去冗余斑点,同时提取出病斑的颜色和形状特征；采用基于模糊决策最大隶属度原则的模糊识别算法对玉米叶部的大斑病小斑病灰斑病褐斑病弯苞菌叶斑病锈病六种病害进行分类识别,并综合与其它识别方法的识别结果进行比较,得到较高精度的识别诊断结果,为研究作物病虫草害的智能识别诊断提供了软件和技术支持本研究取得了以下两方面的进展：第一,实现了玉米叶部大斑病小斑病灰斑病褐斑病弯苞菌叶斑病锈病六种病害的自动识别,识别准确率达到90%以上；第二,分析了利于玉米叶部病害识别的病斑特征,将模糊识别算法应用于玉米叶部病害识别适应玉米病害的特点,获得较高的识别精度同时综合其它的识别算法得出模糊识别应用的优势,提高了识别的准确性和可靠性将机器视觉应用于玉米叶部病害的识别诊断,拓展了机器视觉的应用范围,为机器视觉技术在农业领域的研究应用奠定了基础更多还原

【Abstract】 Along with increasing ability of computer process as well as rapid development of image processing and recognition technology, this technology not only is applied to agriculture more and more widely, but also is an important technology to achieve agriculture information and automation. The amount of work for gathering agriculture information is tremendous; besides the realtime and accuracy of information are widely paid attention to in agriculture production and scientific research. How to diagnose disease of crops timely and accurately is always an important part of research facing to the agriculture field through computer technology. At the same time crop disease, insect pest and weed affect the output and quality of crop badly. The paper mainly aimed at the problem of low precision and untimely diagnosis about crop disease, by means of computer image processing technology as an important means, took common maize leaf diseases as study object and brought forward feasible method to improve diagnosis precision applying the knowledge on image processing and plant physiology synthetically, therefore it provides theory to the correlative research of auto recognition and diagnosis of crop disease, insect pest and weeds.The paper represented the theory of image processing and recognition, made use of VC++ programming language build up the recognition software system and project team built the hardware system to get the diseases image. According to the characteristics of maize leaf disease, TSRG method was adopted to segment the disease image; area-marking and Freeman link code method were used calculating the num of disease well as wiping off redundancy dots. And then extracted the disease spot’s color and form feature and saved in the database. Based on fuzzy decision making and mean of maximum method, Fuzzy Recognition algorithm was used to recognize six kinds of maize leaf disease. The paper integrated the results of other recognition methods, which were used to research crops, and got the effective identification of diagnostic results, which provides software and technology support for crops’diseases, pests and weeds diagnosis and recognition research.The study made two aspects of progress in the following. First, it realized automatic identification of the common six kinds of maize leaf disease, and the recognition accuracy rate of over 90%. Secondly, the paper analysised the feature of the maize leaf disease and selected the features which made a greater contribution to recognition; at the same time, Fuzzy Recognition algorithm adopted to maize leaf disease recognition was a great breakthrough for crops Recognition Research. Because of it integrated the advantages of the other methods and got the identification of diagnostic results, it improved the accuracy and reliability of recognition.The research applied computer vision technology in maize disease recognition, which extends the application area of machine vision and also provide a use for reference in the field of agriculture technology for machine vision.更多还原