ReviewLimnology and oceanography2022
Machine learning techniques to characterize functional traits of plankton from image data.
Review in Limnology and oceanography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The Predator-Prey Encounter Detection (PrED) Model: Detecting Interactions Between Rotifers and Algae Using Computer Vision and Machine Learning.Ecology and evolution · 2026Article
- Key link between iron and the size structure of three major mesoplanktonic groups in the upper ocean.Nature communications · 2026Article
- Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision.Journal of imaging · 2026Review
- Article
- Annotated IFCB plankton images from the Mediterranean Sea.Scientific data · 2025Article
- MLDAAPP: machine learning data acquisition for assessing population phenotypes.G3 (Bethesda, Md.) · 2025Article
- From Pixels to Patterns: Trait Plasticity and Species Overlap ofEcology and evolution · 2025Article
- Emergent Relationships Between the Functional Diversity of Marine Planktonic Copepods and Ecosystem Functioning in the Global Ocean.Global change biology · 2025Article
- High-frequency diatom dynamics seen in an ice- and snow-covered temperate lake using an imaging-in-flow cytometer.Hydrobiologia · 2025Article
- Distinct phytoplankton size classes respond differently to biotic and abiotic factors.ISME communications · 2025Article
- Introducing ARTiMiS: A Low-Cost Flow Imaging Microscope for Microalgal Monitoring.Environmental science & technology · 2024Article
- "UDE DIATOMS in the Wild 2024": a new image dataset of freshwater diatoms for training deep learning models.GigaScience · 2024Article
- Morphological diversity increases with decreasing resources along a zooplankton time series.Proceedings. Biological sciences · 2023Article
- Temperature dependence of parasitoid infection and abundance of a diatom revealed by automated imaging and classification.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Authors and funding
37 authors.
Funding
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Abstract
Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
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Registered trials
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