曹晶晶,王一鸣,毛文华,张小超.基于纹理和位置特征的麦田杂草识别方法[J].农业机械学报,2007,38(4):107-110.
.[J].Transactions of the Chinese Society for Agricultural Machinery,2007,38(4):107-110.
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基于纹理和位置特征的麦田杂草识别方法   [下载全文]
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DOI:10.3969/j.issn.1000-1298.[year].[issue].[sequence]
中文关键词:  杂草识别  纹理特征  颜色共生矩阵  位置特征
基金项目:
曹晶晶  王一鸣  毛文华  张小超
中国农业大学
中文摘要: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。根据条播作物小麦作物行的间距相对固定等位置特征,利用植物像素直方图法确定作物行的中心线和行宽,并识别行间杂草。然后,以作物行中心为基准来选取纹理块,计算量化级数为8级的H颜色空间的共生矩阵,提取5个纹理特征参数,利用K均值聚类法判别分析各块的类别来识别行内杂草。研究结果表明,杂草的正确识别率约为93%,作物的错误识别率约为
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Abstract:7%。 Take the weeds in wheat fields as the research object, a method of weed detection by using the texture and position features was studied. According to the position feature of drilled crops that were regularly sown as a constant row space, the pixel-histogram method was used to determine the central line and the width of crop row. As a result, weeds between crop rows were detected. Moreover, the block of texture was selected on the basis of the central line of crop row. The co-occurrence matrixes of the H color space that was quantified 8 levels were computed. Based on that, five texture parameters were extracted. Then, the K means clustering method was used to recognize weeds within crop rows. The result of research showed that the correct classification of weeds was 93% and the mistake classification of crops was 7%.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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