Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China)

文献类型： 外文期刊

第一作者： Zhao, Tong

作者： Zhao, Tong;Zhong, Ping;Shen, Zhencai;Zhong, Ping;Zou, Hui;Zhao, Tong;Shen, Zhencai;Zhong, Ping;Zou, Hui;Shen, Zhencai;Zhong, Ping;Zou, Hui;Shen, Zhencai;Zhong, Ping;Zou, Hui;Han, Mingming

作者机构：

关键词： Pathogenic microorganism prediction; BP neural network; Wavelet denoising; Data augmentation; qPCR technology; Aquaculture

期刊名称：ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH （ 影响因子：5.8； 五年影响因子：5.4 ）

ISSN： 0944-1344

年卷期： 2024 年 31 卷 5 期

页码：

收录情况： SCI

摘要： The detection and prediction of pathogenic microorganisms play a crucial role in the sustainable development of the aquaculture industry. Currently, researchers mainly focus on the prediction of water quality parameters such as dissolved oxygen for early warning. To provide early warning directly from the pathogenic source, this study proposes an innovative approach for the detection and prediction of pathogenic microorganisms based on yellow croaker aquaculture. Specifically, a method based on quantitative polymerase chain reaction (qPCR) is designed to detect the Cryptocaryon irritans (Cri) pathogenic microorganisms. Furthermore, we design a predictive combination model for small samples and high noise data to achieve early warning. After performing wavelet analysis to denoise the data, two data augmentation strategies are used to expand the dataset and then combined with the BP neural network (BPNN) to build the fusion prediction model. To ensure the stability of the detection method, we conduct repeatability and sensitivity tests on the designed qPCR detection technique. To verify the validity of the model, we compare the combined BPNN to long short-term memory (LSTM). The experimental results show that the qPCR method provides accurate quantitative measurement of Cri pathogenic microorganisms, and the combined model achieves a good level. The prediction model demonstrates higher accuracy in predicting Cri pathogenic microorganisms compared to the LSTM method, with evaluation indicators including mean absolute error (MAE), recall rate, and accuracy rate. Especially, the accuracy of early warning is increased by 54.02%.

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