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李鑫星,朱晨光,周婧,孙龙清,曹霞敏,张小栓.光谱技术在水产养殖水质监测中的应用进展及趋势[J].农业工程学报,2018,34(19):184-194.DOI:10.11975/j.issn.1002-6819.2018.19.024
光谱技术在水产养殖水质监测中的应用进展及趋势
投稿时间:2018-05-04  修订日期:2018-09-03
中文关键词:  光谱技术  水产养殖  水质  监测模型
基金项目:国家重点研发计划项目(2017YFE0111200);农村领域国家科技计划资助项目(2015BAD7B-5)
作者单位
李鑫星 1. 中国农业大学信息与电气工程学院北京 1000832. 食品质量与安全北京实验室北京 100083 
朱晨光 1. 中国农业大学信息与电气工程学院北京 100083 
周婧 1. 中国农业大学信息与电气工程学院北京 100083 
孙龙清 1. 中国农业大学信息与电气工程学院北京 100083 
曹霞敏 3. 苏州大学基础医学与生物科学学院苏州 215200 
张小栓 2. 食品质量与安全北京实验室北京 1000834. 中国农业大学工学院北京 100083 
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中文摘要:水产养殖的水质是关乎水产养殖经济效益和水产品品质的关键因素,与传统的水质检测方法相比,光谱技术具有无创性、快速性、可重复性、准确性等优点,已成为水质监测的重要发展方向。 该文总结和整理现有国内外研究文献,对基于光谱技术的水质重要参数监测、数据预处理方法、特征波段提取、预测模型算法进行了系统的分析与讨论。综述结果表明,实时在线的水产养殖水质监测将成为重点研究方向;多源光谱融合、多参数的水产养殖水质监测将会成为新的发展方向;对于光谱数据的处理,将多种数据处理算法相结合,仍将占据主导;而非线性建模将成为水产养殖水质数据分析的主流方法非线性数据建模,将成为光谱技术应用于水产养殖水质监测的主流建模发方法。
Li Xinxing,Zhu Chenguang,Zhou Jing,Sun Longqing,Cao Xiamin,Zhang Xiaoshuan.Review and trend of water quality detection in aquaculture by spectroscopy technique[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(19):184-194.DOI:10.11975/j.issn.1002-6819.2018.19.024
Review and trend of water quality detection in aquaculture by spectroscopy technique
Author NameAffiliation
Li Xinxing 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Beijing Laboratory of Food Quality and Safety, Beijing 100083, China
 
Zhu Chenguang 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
 
Zhou Jing 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
 
Sun Longqing 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
 
Cao Xiamin 3. School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215200, China
 
Zhang Xiaoshuan 2. Beijing Laboratory of Food Quality and Safety, Beijing 100083, China
4. College of Engineering, China Agricultural University, Beijing 100083, China 
Key words:spectroscopy  aquaculture  water quality  monitoring model
Abstract: The water quality of aquaculture is a key factor concerning the economic benefits of aquaculture and the quality of aquatic products. In recent years, with the development of economy, the discharge of industrial wastewater and domestic sewage has greatly increased, resulting in environmental pollution, for example, the water quality of aquaculture ponds has been polluted. In order to achieve the goal of high yield and safe breeding at the same time of environmental protection and energy conservation, scholars have paid attention to the rapid and accurate acquisition of aquaculture water quality information, which was the important research content of the smart agriculture and agricultural Internet of Things. Water quality monitoring technology based on spectral analysis is an important development direction of aquaculture water quality monitoring. Compared with traditional chemical analysis, electrochemical analysis and chromatographic analysis methods, spectral analysis technology is more simple and convenient, consumes a small quantity of reagents, and is reproducible. This article summarizes and sorts the existing domestic and foreign research literatures, and systematically analyzes and discusses the important parameters of water quality monitoring, data preprocessing methods, feature band extraction, and detection model algorithms based on spectroscopy. This article reviews the COD (chemical oxygen demand) water quality monitoring methods, total nitrogen water quality monitoring methods, total phosphorus water quality monitoring methods, heavy metal water quality monitoring methods, covering traditional chemical methods and spectral analysis methods of these parameters. This article compares and analyzes the spectral method and the traditional methods. We find that compared with the traditional water quality monitoring methods, the spectral technology is non-invasive, rapid rapid?monitoring, repeatable and accurate. The sensitive spectral bands of the above parameters are summarized. The data preprocessing algorithm includes Savitzky-Golay smoothing, wavelet analysis, and multivariate scatter correction, the feature band extraction algorithm includes continuous projection algorithm, no-information variable elimination algorithm, and principal component analysis, and the model includes partial least squares algorithm, least squares algorithm, and artificial neural network. The advantages, disadvantages and scopes of application of these algorithms are summarized and compared. The spectrum detection process of these algorithms is analyzed. Among them, a detailed review of the application of model algorithms in water quality monitoring is conducted, and the prediction results of each water quality prediction model algorithm are statistically analyzed. The results show that online aquaculture water quality testing will be the focus of research. Multi-parameter monitoring is the development direction of aquaculture water quality monitoring. For the processing of spectral data, the combination of multiple data processing algorithms will still dominate. Nonlinear modeling will become the mainstream method for water quality data analysis of aquaculture and will become the mainstream method for the application of spectral technology to water quality detection of aquaculture.
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