The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phyto- and zooplankton images, foraging and swimming behaviour analysis, and finally ecological modelling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.

Department of Marine Sciences, University of Gothenburg, Sweden (Dated: February 21, 2022) The marine microbial food web plays a central role in the global carbon cycle. Our mechanistic understanding of the ocean, however, is biased towards its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the oceanic food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three dimensional position and dry mass. The deep learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates and handling times for individual microplanktons. This method is particularly useful to explore the flux of carbon through micro-zooplankton, the most important and least known group of primary consumers in the global oceans. Moreover, indirect measurements is well established in terrestrial ecology.

Researchers on the OcéanIA project are developing new artificial intelligence and mathematical modelling tools to contribute to the understanding of the oceans and their role in regulating and sustaining the biosphere, and tackling climate change. You may have seen our recent interview with the director of the project, and of Inria Chile, Nayat Sánchez-Pi. She explained the challenges of research in the field, what they are working on as part of the project, and the role that AI methods play. A key part of the project is data, and much of this is being collected by the Tara Microbiome-CEODOS expedition. The objective of this expedition is to study the marine microorganisms which play a fundamental role in ocean ecosystems.

Oceans are vast--and vastly complicated. They're difficult to study, as even the most technologically advanced sensors struggle to analyze such a complex ecosystem. But over the next five years, IBM is working to tap the help of tiny little creatures--plankton--whose changes in behavior can point to hard-to-spot changes in the environment. IBM researchers are building a real-time network of autonomous microscopes that will monitor ocean plankton--the more data they collect, the better they'll be able to predict threats like red tides. And in the future, AI technology can equip these microscopes to report abnormalities in real time.

IBM's mission is to help their clients change the way the world works. Each year, we showcase some of the biggest breakthroughs coming out of IBM Research's global labs – five technologies that they believe will fundamentally reshape business and society in the next five years. This innovation is informed by research taking place at IBM Labs, leading edge work taking place with our clients, and trends we see in the tech/business landscape. Later today, they'll introduce the scientists behind this year's 5 in 5 at a Science Slam held at the site of IBM's biggest client event of the year: Think 2018 in Las Vegas. Watch it live or catch the replay here.

Do you like a planet that hasn't yet melted? Then you're secretly in love with plankton, tiny marine organisms that float around at the mercy of currents. They sequester carbon dioxide and provide two thirds of the oxygen in our atmosphere and sacrifice themselves as baby food for the young fish that eventually end up on your plate. Yet science knows little about the complex dynamics of plankton on ocean-wide scales. So researchers are asking the machines for help, developing clever robots that use AI to examine and classify plankton, the pivotal organisms at the base of our oceanic food chain.

A swarm of seafaring robots unleashed by researchers at UC San Diego has discovered how plankton might get together to have sex: by harnessing the motion of the ocean. The robots, described in the journal Nature Communications, shed fresh light on the mysterious behaviors of the tiny but legion creatures that help form the foundation of marine food chains. Scientists have long tried to study plankton, a catchall term for a diverse array of living things that reside in large bodies of water. Plankton can include bacteria, algae and even tiny animals, but these disparate groups and species share at least one characteristic: an inability to swim against a current. As you can imagine, this has the potential to put a big crimp in a little plankter's love life.