| 基于多特征的杂草逆向定位方法与试验 |
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| 引用本文： | 陈亚军,赵博,李树君,刘磊,苑严伟,张延立.基于多特征的杂草逆向定位方法与试验[J].农业机械学报,2015,46(6):257-262. |
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| 作者姓名： | 陈亚军 赵博 李树君 刘磊 苑严伟 张延立 |
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| 作者单位： | 西安理工大学,中国农业机械化科学研究院,中国农业机械化科学研究院,中国农业机械化科学研究院,中国农业机械化科学研究院,中国农业机械化科学研究院 |
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| 基金项目： | 国家高技术研究发展计划（863计划）资助项目（2012AA10A503）、陕西省自然科学基础研究计划资助项目（2014JM2—6111）和陕西省科技计划资助项目（2013k07—18) |
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| 摘 要： | 提出了一种基于多特征的杂草逆向定位方法。以田间作物作为研究对象,将多目标杂草定位问题转换为单目标的作物定位问题。采用作物叶片HU不变矩与形状特征的识别准确定位出每一株作物,然后基于颜色特征将作物区域以外的绿色植物均认定为杂草。设计了一款小型杂草定位装置,并应用在宽幅喷药机上。田间试验结果表明,在喷药机工作速度为5 km/h时,该系统对于大豆田间杂草识别的准确率为90%以上,较好地解决了杂草定位与精细喷洒农药问题。
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| 关 键 词： | 杂草 逆向定位 多特征 不变矩 形状特征 |
| 收稿时间： | 2015/2/10 0:00:00 |
Weed Reverse Positioning Method and Experiment Based on Multi-feature |
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| Chen Yajun,Zhao Bo,Li Shujun,Liu Lei,Yuan Yanwei and Zhang Yanli.Weed Reverse Positioning Method and Experiment Based on Multi-feature[J].Transactions of the Chinese Society of Agricultural Machinery,2015,46(6):257-262. |
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| Authors: | Chen Yajun Zhao Bo Li Shujun Liu Lei Yuan Yanwei and Zhang Yanli |
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| Abstract: | Field weed is the big enemy of agricultural production, and also is one of the key problems that blocked the crop growth in Chinese agriculture. Accurate positioning weeds and realizing the variable precision applying pesticide or herbicide are particularly important. To solve various field weeds positioning difficult problems, a multi-feature based weed reverse positioning method was proposed. By taking the field crops as the research object, the multi-objective weed positioning issue was transformed into single objective crop recognition problem. Firstly, seven moment invariants and eight shape feature parameters were extracted from many of the individual soybean crop leaves, and the mean value of moment invariants and shape features were taken as standard soybean leave feature value. Secondly, after a series of image preprocessing such as image segmentation, regional separation feature match and connected component analysis, multi-feature recognition method with HU invariant moments and shape features of crops were utilized to accurately locate each crop plant. Finally, based on color feature, the green plants outside of the crops region were treated as weeds. Furthermore, a small weed positioning device was designed based on this method, which was applied to wide pesticide spraying machine. Field experiment results showed that weed recognition accuracy of this system for weed in soybean field was more than 90% when the spraying machine working speed was 5km/h, hence weed positioning and pesticide accurate spraying problems could be well settled. |
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| Keywords: | Weed Reverse positioning Multi-feature Invariant moments Shape feature |
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