Effect of Turbidity on Semi-Automatic Analysis of Copepod Size and Abundance Distribution in the Water Column  

作　　者：Thiago da Silva Matos Carolina Siqueira dos Reis Laura de Andrade Moura Márcio Abreu Ricardo Coutinho Lohengrin Fernandes Thiago da Silva Matos;Carolina Siqueira dos Reis;Laura de Andrade Moura;Márcio Abreu;Ricardo Coutinho;Lohengrin Fernandes(IEAPM/UFF Marine Biotechnology Post-Graduate Program, Arraial do Cabo, Brazil;Instituto de Estudos do Mar Almirante Paulo Moreira, Arraial do Cabo, Brazil)

机构地区：[1]IEAPM/UFF Marine Biotechnology Post-Graduate Program, Arraial do Cabo, Brazil [2]Instituto de Estudos do Mar Almirante Paulo Moreira, Arraial do Cabo, Brazil

出　　处：《Advances in Bioscience and Biotechnology》2024年第6期380-395,共16页生命科学与技术进展（英文）

摘　　要：Automated image systems to characterize aquatic organisms improve research and enable fast response to environmental risk situations. In November 2015, a dam in Mariana City-MG (Brazil) collapsed and led to the disposal of mud tailings from the mining process to the Doce River. The accident resulted in several casualties and incalculable damage to surrounding communities and the environment. The mud increased water turbidity, an essential condition to the functioning of the image analysis systems, and directly affected the characterization of the organisms, making it impossible to distinguish copepods in the mud, due to the blurred outline. To get a quick response evaluating environmental situations, this work aimed to develop and test different algorithms characterizing and classifying copepods by their size (length and area) using in situ images acquired by the Lightframe On-Sight Keyspecies Investigation device. Field tests were carried out under different turbidity levels throughout the gradient observed in the coastal zone adjacent to the Doce River. The best algorithm reduced nearly 50% of the noise in some images when compared with manual treatment and led to 96% accuracy in measurement and counting. Semi-automated devices that perform post-processing corrections are suitable for fast environmental evaluation under high turbidity scenarios.Automated image systems to characterize aquatic organisms improve research and enable fast response to environmental risk situations. In November 2015, a dam in Mariana City-MG (Brazil) collapsed and led to the disposal of mud tailings from the mining process to the Doce River. The accident resulted in several casualties and incalculable damage to surrounding communities and the environment. The mud increased water turbidity, an essential condition to the functioning of the image analysis systems, and directly affected the characterization of the organisms, making it impossible to distinguish copepods in the mud, due to the blurred outline. To get a quick response evaluating environmental situations, this work aimed to develop and test different algorithms characterizing and classifying copepods by their size (length and area) using in situ images acquired by the Lightframe On-Sight Keyspecies Investigation device. Field tests were carried out under different turbidity levels throughout the gradient observed in the coastal zone adjacent to the Doce River. The best algorithm reduced nearly 50% of the noise in some images when compared with manual treatment and led to 96% accuracy in measurement and counting. Semi-automated devices that perform post-processing corrections are suitable for fast environmental evaluation under high turbidity scenarios.

关 键 词：Imaging ZOOPLANKTON Doce River LOKI Copepod 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]