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METHODS published: 12 April 2022 doi: 10.3389/fmars.2022.868420Automated Plankton Classication With a Dynamic Optimization and Adaptation Cycle Jan Conradt 1*, Gregor Brner 1, Angel LopezUrrutia 2,
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How to fill out automated plankton classication

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How to fill out automated plankton classication

01
Gather sample data: Collect water samples containing plankton from your study area.
02
Prepare the samples: Preserve the collected samples to prevent degradation and process them for analysis.
03
Image acquisition: Use a digital imaging system to capture high-resolution images of plankton in the samples.
04
Preprocessing: Clean up the images to enhance quality, removing noise and irrelevant elements.
05
Feature extraction: Use algorithms to identify and extract features relevant for classification, such as shape, size, and color.
06
Model selection: Choose a machine learning model suitable for classification tasks, like convolutional neural networks (CNN).
07
Training: Train the model using a labeled dataset to recognize different species of plankton.
08
Validation: Assess the model's performance on a separate validation set to ensure accuracy and reduce overfitting.
09
Classification: Use the trained model to automatically classify the plankton in new, unlabeled samples.
10
Review results: Analyze the classification output and verify the results against known species.

Who needs automated plankton classication?

01
Marine biologists: For research on aquatic ecosystems and biodiversity.
02
Environmental monitoring agencies: To assess water quality and ecosystem health.
03
Fisheries: To understand plankton populations that affect fish stocks.
04
Ecologists: For studies on food webs and nutrient cycling.
05
Educational institutions: For teaching purposes and research in marine sciences.
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Automated plankton classification is a process that uses advanced technologies, such as machine learning and image recognition, to automatically identify and categorize different species of plankton from sample images or videos.
Researchers, oceanographers, and environmental monitoring agencies involved in marine biology and ecological studies are typically required to file automated plankton classification as part of their data collection and reporting.
Filling out automated plankton classification usually involves inputting sample data into software that performs the classification, ensuring the correct metadata is provided, such as sample location, time, and method of collection.
The purpose of automated plankton classification is to enhance the speed and accuracy of identifying plankton species, allowing for better monitoring of marine ecosystems, assessments of biodiversity, and understanding ecological dynamics.
Information reported in automated plankton classification typically includes species identification, abundance, distribution data, sampling conditions, and any relevant environmental parameters such as temperature and salinity.
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