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作 者:Wei XU Jie NIU Wenyu GAN Siyu GOU Shuai ZHANG Han QIU Tianjiu JIANG
机构地区:[1]Research Center of Red Tides and Marine Biology,Jinan University,Guangzhou 510632,China [2]Atmospheric Sciences and Global Change Division,Pacific Northwest National Laboratory,Richland 99354,WA,USA [3]Key Laboratory of Eutrophication and Red Tide Prevention of Harmful Algae and Marine Biology,Jinan University,Guangzhou 510632,China
出 处:《Journal of Oceanology and Limnology》2022年第6期2202-2217,共16页海洋湖沼学报(英文)
基 金:Supported by the National Natural Science Foundation of China(No.41972244);partially supported by the Science and Technology Basic Resources Survey of the Ministry of Science and Technology(No.2018FY100201);the National Key Research and Development Program(No.2019YFC1407900)to Siyu GOU;Shuai ZHANG;Wenyu GAN;and Tianjiu JIANG。
摘 要:Paralytic shellfi sh poisoning(PSP)microalgae,as one of the harmful algal blooms,causes great damage to the of fshore fi shery,marine culture,and marine ecological environment.At present,there is no technique for real-time accurate identifi cation of toxic microalgae,by combining three-dimensional fluorescence with machine learning(ML)and deep learning(DL),we developed methods to classify the PSP and non-PSP microalgae.The average classifi cation accuracies of these two methods for microalgae are above 90%,and the accuracies for discriminating 12 microalgae species in PSP and non-PSP microalgae are above 94%.When the emission wavelength is 650-690 nm,the fl uorescence characteristics bands(excitation wavelength)occur dif ferently at 410-480 nm and 500-560 nm for PSP and non-PSP microalgae,respectively.The identification accuracies of ML models(support vector machine(SVM),and k-nearest neighbor rule(k-NN)),and DL model(convolutional neural network(CNN))to PSP microalgae are 96.25%,96.36%,and 95.88%respectively,indicating that ML and DL are suitable for the classifi cation of toxic microalgae.
关 键 词:paralytic shellfi sh poisoning(PSP) machine learning(ML) deep learning(DL) toxic algal classifi cation
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