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当前位置: 首页 > 文章 > 基于高光谱图像技术的稻田苗期杂草稻识别 农业机械学报 2013,44 (5) 253-257,163
Position: Home > Articles > 基于高光谱图像技术的稻田苗期杂草稻识别 Transactions of the Chinese Society for Agricultural Machinery 2013,44 (5) 253-257,163

基于高光谱图像技术的稻田苗期杂草稻识别

作  者：
陈树人;邹华东;吴瑞梅;闫润;毛罕平
单  位：
江西农业大学工学院;江苏大学
关键词：
杂草稻;水稻;高光谱图像;神经网络
摘  要：
以生长期为10 d的杂草稻和水稻为研究对象,采集其高光谱图像信息,对其进行滤波预处理后,利用主成分分析方法优选出1 448.89 nm和1 469.89 nm波长下的特征图像.对每个特征图像,分别提取其形状特征、纹理特征和颜色特征,共18个特征变量.基于这些特征变量,利用神经网络方法建立杂草稻和水稻的判别模型,模型训练时杂草稻和水稻的回判率都为100%;预测时,杂草稻的回判率为92.86%,水稻的回判率为96.88%.研究表明,利用高光谱图像技术快速鉴别稻田苗期杂草稻是可行的.
单  位：
Key Laboratory of Modern Agricultural Equipment and Technology,Ministry of Education,Jiangsu University, Zhenjiang 212013,China College of Engineering,Jiangxi Agricultural University,Nanchang 330045,China Department of Mechanical and Electrical Engineering,Jiangsu Polytechnic College of Agriculture and Forestry,Jurong 212400,China
关键词：
Weedy rice Rice Hyper-spectral image Neural network
摘  要：
The weedy rice and rice in growth period of 10 d were investigated.The hyper-spectral image data were captured from weedy rice and rice leaves.After image data were filtered,the feature images at wavelength of 1 448.89 nm and 1 469.89 nm were optimized by principal component analysis method.For each feature image,shape feature,texture feature and color feature were extracted,and 18 feature variables in all were attained.Neural network method was used to build the discriminate model.The discriminating rates for weedy rice and rice were both 100% in training set.The discriminating rate for weedy rice was 92.86% and the discriminating rate for rice was 96.88% in prediction set.Experimental results showed that the hyper-spectral imaging technology could be used to identify weedy rice and rice at seeding stage.

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