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Electrical signal measurement in plants using blind source separation with independent component analysis

文献类型： 外文期刊

作者： Huang, Lan 1 ; Wang, Zhong-Yi 1 ; Zhao, Long-Lian 1 ; Zhao, Dong-Jie 1 ; Wang, Cheng 2 ; Xu, Zhi-Long 2 ; Hou, Rui-Fen 1 ;

作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Blind source separation; Independent component analysis; Electrical signal in plant

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2010 年 71 卷

页码：

收录情况： SCI

摘要： Electrical signals of a plant leaf measured using surface recording are mixed signals which involve the electrical activities of the epidermis cells, guard cells, and mesophyll cells. Blind source separation (BSS) is a general signal processing approach, which estimates the source signals independently if the unknown signal sources are made by mixing linearly. The independent component analysis (ICA) method is one technique used to solve the blind source separation (BSS) problem. In contrast with conventional measuring methods used to investigate the electrical signals of plant cells with a complex treatment procedure, the ICA method was provided to achieve separation of the mixed electrical signals to recover the individual signals of each type of cells non-invasively. The proposed method has been tested using simulated signals and real plant electrical signal recordings. The results showed that ICA algorithms provided an efficient tool for the identification of the independent signal components from surface electrode recordings. (C) 2009 Elsevier B.V. All rights reserved.

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