李旭青,李龙,庄连英,刘玮琦,刘湘南,李杰.基于小波变换和BP神经网络的水稻冠层重金属含量反演[J].农业机械学报,2019,50(6):226-232.
LI Xuqing,LI Long,ZHUANG Lianying,LIU Weiqi,LIU Xiangnan,LI Jie.Inversion of Heavy Metal Content in Rice Canopy Based on Wavelet Transform and BP Neural Network[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(6):226-232.
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基于小波变换和BP神经网络的水稻冠层重金属含量反演   [下载全文]
Inversion of Heavy Metal Content in Rice Canopy Based on Wavelet Transform and BP Neural Network   [Download Pdf][in English]
投稿时间:2018-11-19  
DOI:10.6041/j.issn.1000-1298.2019.06.025
中文关键词:  小波变换  高光谱  重金属胁迫  农作物污染  反演模型
基金项目:国家高分辨率对地观测系统重大专项(67-Y20A07-9002-16/17)、国家自然科学基金项目(41371407)、河北省青年科学基金项目(D2018409029)、河北省高等学校科学技术研究重点项目(ZD2016126)、北华航天工业学院博士基金项目(BKY-2015-02)和河北省航天遥感信息处理与应用协同创新中心开放课题项目(XTZXKF201701)
作者单位
李旭青 北华航天工业学院 
李龙 北华航天工业学院 
庄连英 北华航天工业学院 
刘玮琦 北华航天工业学院 
刘湘南 中国地质大学(北京) 
李杰 北华航天工业学院 
中文摘要:自然农田生态系统中,农作物的各种生化参数受重金属污染胁迫后虽表现异常,但其特征往往极为微弱,极不稳定。利用处理非稳定信号方法中常用的信号处理方法——小波分析法(Db-5),对水稻的光谱反射率数据进行处理,有效提取光谱信号中受重金属污染胁迫而潜藏的一些“突变”弱信息。利用Db-5小波基进行小波变换,从中选取具有异常光谱特征的奇异点,利用奇异点对应波段(716、745、766nm)的光谱反射率构建反向传播(BP)神经网络模型,对水稻冠层4种重金属含量进行反演。将利用模型得到的预测值与实测值进行相关性分析,结果表明,基于BP神经网络的水稻冠层重金属含量反演模型对于实验区镉、铅、汞、砷4种重金属胁迫,具有良好的反演效果。
LI Xuqing  LI Long  ZHUANG Lianying  LIU Weiqi  LIU Xiangnan  LI Jie
North China Institute of Aerospace Engineering,North China Institute of Aerospace Engineering,North China Institute of Aerospace Engineering,North China Institute of Aerospace Engineering,China University of Geosciences (Beijing)
and North China Institute of Aerospace Engineering
Key Words:wavelet transform  hyperspectra  heavy metal stress  crop pollution  inversion model
Abstract:In the “natural farmland ecosystem”, although the biochemical parameters of crops are abnormal under the stress of heavy metal pollution, their characteristics are often very weak, with small changes and extreme unstability. Wavelet analysis, a common signal processing method in unstable signal processing, was used to process spectral reflectance data of crops (rice) and effectively extract weak information of “mutation” hidden in spectral signals under the stress of heavy metal pollution. Wavelet transform was carried out by using Db-5 wavelet basis, and singular points with abnormal spectral characteristics were selected. Back propagation neural network model (BPNN) was constructed by using spectral reflectance of corresponding bands of singular points (716nm, 745nm and 766nm) to invert the contents of four heavy metals in rice canopy. Correlation analysis was conducted between the predicted and measured values of the model, and the results showed that the inversion model of heavy metal content in rice canopy based on BP neural network had a good inversion effect on the stress of cadmium, lead, mercury and arsenic in the experimental area.

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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