Deep-Learning-Based Automated Tracking and Counting of Living Plankton in Natural Aquatic Environments-Reference-Cited by-同舟云学术

Deep-Learning-Based Automated Tracking and Counting of Living Plankton in Natural Aquatic Environments

Published:2023-05-19 Issue:46 Volume:57 Page:18048-18057
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Chen Zhuo¹,Du Meng¹,Yang Xu-Dan¹,Chen Wei²^ORCID,Li Yu-Sheng¹³,Qian Chen¹^ORCID,Yu Han-Qing¹^ORCID

Affiliation:

1. CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, People’s Republic of China

2. School of Metallurgy and Environment, Central South University, Changsha 410083, People’s Republic of China

3. Institute of Advanced Technology, University of Science and Technology of China, Hefei, 230031, People’s Republic of China

Funder

Central University Basic Research Fund of China

Program for Changjiang Scholars and Innovative Research Team in University

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.3c00253

Reference39 articles.

1. In the other 90%: phytoplankton responses to enhanced nutrient availability in the Great Barrier Reef Lagoon

2. Ocean Science: The power of plankton

3. Photo-Reactivity and Photo-Transformation of Algal Dissolved Organic Matter Unraveled by Optical Spectroscopy and High-Resolution Mass Spectrometry Analysis

4. Water monitoring by means of digital microscopy identification and classification of microalgae

5. Microplastic Ingestion by Zooplankton

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