基于多特征的田间杂草识别方法

毛文华; 曹晶晶; 姜红花; 王一鸣; 张小超

基于多特征的田间杂草识别方法

1.
中国农业机械化科学研究院，北京 100083
2.
中国农业大学信息与电气工程学院，北京 100083
3.
山东农业大学信息科学与工程学院，泰安 271018

基金项目: 国家自然科学基金资助项目(30500305)

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- 文章访问数: 1221
- HTML全文浏览量: 5
- PDF下载量: 499
出版历程
- 收稿日期: 2006-08-25
- 修回日期: 2007-03-17
- 发布日期: 2007-11-29

In-field weed detection method based on multi-features

1.
Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China
2.
College of Information and Electronic Engineering, China Agricultural University, Beijing 100083, China
3.
College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China

摘要

摘要: 该文阐述了通过利用植物的多种特征实现田间杂草的精准自动识别的方法。该方法先利用颜色特征分割土壤背景，然后利用位置和纹理特征识别行间和行内杂草，最后利用形态特征后处理误识别的作物和杂草。在实验室内利用实地采集的3～5叶期、不同作物行数的麦田图像对该方法进行了测试。作物和杂草的正确识别率最低为89%，最高为98％；处理时间最低为157 ms，最高为252 ms。试验结果表明：基于多特征的田间杂草识别方法具有较高的识别率和较快的识别速度。
- 田间杂草 /
- 自动识别 /
- 多特征 /
- 图像处理
Abstract: An automatic and precision method based on multi-features of plant was developed for weed detection. The color feature was used at first to segment green plant and soil background. Then the position feature was utilized to detect between-row weed and the texture feature was adopted to classify intra-row weed. Finally, the morphology feature was used to post-process the misclassified crop and weed. Images taken from the real wheat field (3～5 leaves seedling stage and within the different numbers of crop row) were used to test the novel solution of weed detection in the laboratory. The correct classification rate of crop and weed was over 89% and up to 98%. And the processing time was from 157 ms to 252 ms. Experimental results show that the weed detection method based on multi-features has a high classification rate and a quick processing speed.
- in-field weed /
- automatic detection /
- multi-feature /
- image processing

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计量

文章访问数: 1221
HTML全文浏览量: 5
PDF下载量: 499
被引次数: 0

基于多特征的田间杂草识别方法

计量

出版历程

In-field weed detection method based on multi-features

计量

出版历程

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