A test system of infield weed detection based on machine vision was designed and developed.
设计并开发了基于机器视觉的田间杂草识别试验系统。
The software system consisted of file management, system initialization, image preprocessing, background segmentation and weed detection.
系统软件包括文件管理、系统设定、图像预处理、背景分割、杂草识别等内容。
Take the weeds in wheat fields as the research object, a method of weed detection by using the texture and position features was studied.
以化学防除适期麦田杂草为研究对象，对利用条播作物的位置和纹理特征识别田间杂草的方法进行了研究。
The parallel and fast algorithms about capturing and processing dynamic images of infield weed detection were studied under the lighting, indoor and dynamic conditions.
对运动图像的采集和处理算法进行了研究，分析了在人工照明、室内、动态情况下，适用于田间杂草识别的图像处理算法。
We can use the last weed image to count the density of weed. So we get the detection of weed in real-time.
最后根据分割的图像统计杂草的密度，达到对杂草进行实时探察的目的。
We can use the last weed image to count the density of weed. So we get the detection of weed in real-time.
最后根据分割的图像统计杂草的密度，达到对杂草进行实时探察的目的。
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