基于高光谱成像技术的田间杂草传感方法研究

项目名称： 基于高光谱成像技术的田间杂草传感方法研究

项目编号： No.31272056

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 农业科学

项目作者： 毛文华

作者单位： 中国农业机械化科学研究院

项目金额： 80万元

中文摘要： 如何快速准确的采集田间杂草信息，成为杂草精准控制技术所面临的首要问题。项目利用基于AOTF的高光谱成像仪，采集大豆及其伴生杂草的田间高光谱图像进行处理和分析；采用光谱统计分析方法研究高光谱图像采集的最佳田间环境条件，试验验证550-1700nm波段内绿色植物的生物特征和反射光谱之间的相关关系；采用光谱距离统计和光谱特征位置搜索方法，筛选高光谱图像中区分作物、杂草和土壤的光谱特征向量；针对农作物的种植生长基本相同的特性，提取特征波段下表达农作物植株一致性的位置、形态、纹理等特征，优选高光谱图像中区分作物和杂草的空间特征向量；开发光谱和空间特征向量的快速提取算法和模式识别算法，建立适用于田间杂草信息采集的通用高光谱成像系统，搭载在小型UAV平台上进行大豆田间动态试验，杂草识别率90%。

中文关键词： 田间杂草；高光谱成像；图像处理；光谱分析；模式识别

英文摘要： How to capture the infield weed information with a quick and accurate method is the main problem of precision weed control technology. Therefore, a infield weed monitor station based on an AOTF hyperspectral imaging meter was developed to capture the hyperspectral images with spectral and spatial informations of soybean seedlings and theirs infested weed seedlings. Firstly, the optimal field environmental condition was analyzed with spectral statistical method when the meter worked in the field. Then,the correlativity of green seedlings biological characteristics and their reflection spectra were tested in the 550-1700nm. The spectral feature vector for distinguishing crop, weed and soil was selected by use of the spectrum distance statistical analysis method and the spectrum feature position search method. On the whole, the crop seedlings have the same sowing and growing characteristics. For expression the coherence, the location, shape, texture and other features were extracted from the hyperspectral images in the given feature spectra. Among them, the spatial feature vector for distinguishing crop and weed was selected. The algorithms were developed to extract the spectral and spatial feature vector and to classify the corn and weed seedlings in quick. The universal hyperspectral imaging system was built for

英文关键词： infield weed；hyper-spectral imaging；image processing；spectrum analysis；pattern recognition