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当前位置: 首页 > 文章 > 温室黄瓜叶片近红外图像消噪算法与含氮量快速检测 农业机械学报 2013,44 (7) 216-221
Position: Home > Articles > De-noising Algorithm of Multispectral Images and Nonlinear Estimation of Nitrogen Content of Cucumber Leaves in Greenhouse Transactions of the Chinese Society for Agricultural Machinery 2013,44 (7) 216-221

温室黄瓜叶片近红外图像消噪算法与含氮量快速检测

作  者：
杨玮;李民赞;孙红;郑立华
单  位：
中国农业大学现代精细农业系统集成研究教育部重点实验室
关键词：
温室;黄瓜叶片;图像处理;小波消噪;含氮量;快速检测
摘  要：
在温室基质栽培条件下,研究了温室黄瓜叶片近红外图像的消噪算法以及叶片氮素含量非线性预测。用普通CCD相机加滤光片采集不同生长时期水果型小黄瓜Deitastar的叶片图像,利用小波变换对黄瓜近红外图像进行小波消噪处理,再采用基于邓氏关联度的图像边缘检测法对图像进行分割,得到信噪比较好的目标图像,之后通过计算灰度值得到黄瓜叶片的植被指数。对获得的各种植被指数与黄瓜叶片氮含量之间进行相关分析后得到CNDVI与氮素含量相关系数最高达0.67,同时GNDVI、NDGI、NDVI与氮素相关性显著且相关系数均高于0.50。采用最小二乘支持向量机算法(LS-SVM)对植被指数同黄瓜叶片含氮量进行拟合,拟合模型的决定系数R2为0.825,验证R2为0.728,达到了较为理想的预测精度。
译  名：
De-noising Algorithm of Multispectral Images and Nonlinear Estimation of Nitrogen Content of Cucumber Leaves in Greenhouse
作  者：
Yang Wei Li Minzan Sun Hong Zheng Lihua(Key Laboratory of Modern Precision Agriculture System Integration Research,Ministry of Education,China Agricultural University,Beijing 100083,China)
关键词：
Greenhouse Cucumber leaf Image processing Eliminating noise Nitrogen content Rapid detection
摘  要：
De-noising of near infrared image and nonlinear estimation of nitrogen content were carried out to cucumber leaves in greenhouse.Fruit cucumber Deitastar was chosen as the object.A CCD camera with special filters was used to collect cucumber leaves ’ images in different growth time.After eliminating noise of image with wavelet transform,the images were separated based on grey theory.Correlation analysis between nitrogen content and each kind of vegetation index of cucumbers was conducted,and t tests to those coefficients of correlation were executed.The result showed that CNDVI,GNDVI,NDVI and NDGI were significantly related to nitrogen content of cucumber leaves.The correlation coefficient between CNDVI and nitrogen content of cucumber leaves reached 0.67,and the correlation coefficients between GNDVI,NDGI,NDVI and nitrogen content of cucumber leaves were all higher than 0.50.LS-SVM algorithm was used to construct estimation models between vegetation indexes and nitrogen content and CNDVI,GNDVI,NDGI and NDVI were used as the input of the model.The R2 values of calibration and validation models were 0.825 and 0.728 respectively.

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