基于机器视觉的杂草图像特征提取及识基于机器视觉的杂草图像特征提取及识 别研究别研究学生姓名： xxx 指导教师：xxx 所在院系：xxx 所学专业：xxx 研究方向：xxx xxx 大 学xxx 年xxx 月Based on machine vision image feature extraction and recognition weedsName：xxx Tutor：xxx College：xxx Major：xxx Direction：xxx xxxxxx UniversityMay xxx 基于机器视觉的杂草图像特征提取及识别研究- I -摘 要杂草同农田作物争夺阳光和养分，严重影响了农作物的生长。为了达到除草的目的， 人们开始喷洒大量的除草剂来进行除草。可是却忽略了除草剂的不当使用给人、畜以及环 境造成的危害。本文从实际应用出发，设计了一个基于机器视觉的杂草图像特征提取及识 别系统。系统的运行在参考了前人研究成果的基础上，不断进行对比试验和算法的改进， 找出适合于机器视觉的杂草识别的可行性方法。 本文对动态杂草图像的采集、处理和识别方法进行研究。采集来的图像经常会有模糊 现象的发生，对模糊图像的恢复处理做了大量的研究试验，得出维纳滤波具有较好的恢复 效果；绿色植物和土壤背景的分割试验中，提出了一种基于彩色图像的二值化方法，可以 不经过彩色图像灰度化就能够直接把绿色植物与土壤背景分割开，和以往的分割方法相比 处理速度快，分割效果好，更加满足实时性；杂草和作物的分割主要研究了行间杂草和作 物的分割，参考国内外资料，并进行研究试验，表明运用位置特征识别法有很好的分割效 果，寻找作物中心行采用了简单快速的像素位置直方图法，填充作物中心行采用了改进的 扫描线算法，和其他填充方法相比减少了重复操作，节省了时间，满足实时处理的要求； 分割后的图像为只含有杂草的二值图像，通常会有一些残余的叶片和颗粒的噪声，通过形 态学滤波和扫描线填充算法去除噪声，试验结果表明扫描线算法去除噪声效果更好。 基于机器视觉的杂草图像特征提取及识别系统的硬件组成主要有计算机、采集卡、摄 像头、实验平台。 本文从动态杂草识别的处理方法出发，在实验室内开展了一系列的试验和分析，对主 要的问题和技术难点作了较为深入的研究, 设计实现了基于机器视觉的杂草图像特征提取 及识别系统。 关键词关键词 : : 杂草识别；图像处理；机器视觉基于机器视觉的杂草图像特征提取及识别研究- II -Based on machine vision image feature extraction and recognition weedsAbstractWeed contests sunlight and nutrient with crop, as a result, the growth of crop was seriously affected. People fall back on spraying a great deal of herbicide for weeding purpose, but they ignored misapplying of herbicide will only pollute the environment but also jeopardizes human and livestock. So its necessary to develop an intelligent weeding method. The paper designed a system of image feature extraction and recognition weeds on the basis of machine. In order to find a feasible dynamic weed identifying method we consulted predecessor for their research, at the same time, we did a lot of comparison experiments to improve the arithmetic. The paper studied mostly on the image collection, processing and identification. There will be a blur in moving pictures, so we have to do a lot of experiment to restore the blurry image; at last we find that wiener filter is a preferable method. We put forward a binary method based on color image in the experiment of separating green plants from soil background, the method can separate green plants from soil background without converting color image to gray level image, so the method is more fast, better segmentation effect comparing with traditional methods, and thus meet the request of real-time processing. The paper studied on the separation of row-space weed from crop, having consulted materials of home and abroad, we did a lot of experiment and find out that location character method has a good segmentation effect. We adopted pixel location histogram which is simple and fast in finding central crop row, and adopted improved scan beam arithmetic to fill central crop row. Comparing with other methods the method can reduce repeating operations, so it can save time to meet the requirement of real-time processing. Segmented image only contains weed, but there usually will are some remnants leaves and random noise, we dispose noise applying Mathematical Morphology method and scan beam arithmetic, results of the experiment showed that scan beam arithmetic is better in disposing noise than other methods. Based on machine vision image feature extraction and recognition weeds is made up of PC, image collection card, digital vidicon, experiment console. The paper designed and realized a system of dynamic weed identification based on processing method. A series of experiment and analyses are developed and carried out an in-depth study to some primary problem in the laboratory. Study of the paper offered academic and practical references for the designing of basing on machine vision image feature extraction and weed identification system in actual outdoor environment. Keywords : Weed Identification ; Image Processing; Computer Vision基于机器视觉的杂草图像特征提取及识别研究- III -目 录摘 要.I Abstract.II1 前言.1 1.1 研究目的及意义.1 1.2 国内外研究动态和趋势.2 1.2.1 国外研究现状.2 1.2.2 国内研究现状.4 1.3 课题研究的主要内容.4 1.4 本章小结.