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   Hening, Alexandru; Hieu, Nguyen Trong; Nguyen, Dang Hai; Nguyen, Nhu Ngoc (December 2023, Journal of Differential Equations)
2. Automatic plankton quantification using deep features

   https://doi.org/10.1093/plankt/fbz023  

   González, Pablo; Castaño, Alberto; Peacock, Emily E; Díez, Jorge; Del Coz, Juan José; Sosik, Heidi M (July 2019, Journal of Plankton Research)

   Abstract The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with hand-designed local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using convolutional neural networks trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms. 

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3. The Marine Biodiversity Observation Network Plankton Workshops: Plankton Ecosystem Function, Biodiversity, and Forecasting—Research Requirements and Applications

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5. Improving plankton image classification using context metadata

   https://doi.org/10.1002/lom3.10324  

   Ellen, Jeffrey S.; Graff, Casey A.; Ohman, Mark D. (July 2019, Limnology and Oceanography: Methods)

   Abstract Advances in both hardware and software are enabling rapid proliferation of in situ plankton imaging methods, requiring more effective machine learning approaches to image classification. Deep Learning methods, such as convolutional neural networks (CNNs), show marked improvement over traditional feature‐based supervised machine learning algorithms, but require careful optimization of hyperparameters and adequate training sets. Here, we document some best practices in applying CNNs to zooplankton and marine snow images and note where our results differ from contemporary Deep Learning findings in other domains. We boost the performance of CNN classifiers by incorporating metadata of different types and illustrate how to assimilate metadata beyond simple concatenation. We utilize both geotemporal (e.g., sample depth, location, time of day) and hydrographic (e.g., temperature, salinity, chlorophylla) metadata and show that either type by itself, or both combined, can substantially reduce error rates. Incorporation of context metadata also boosts performance of the feature‐based classifiers we evaluated: Random Forest, Extremely Randomized Trees, Gradient Boosted Classifier, Support Vector Machines, and Multilayer Perceptron. For our assessments, we use an original data set of 350,000 in situ images (roughly 50% marine snow and 50% non‐snow sorted into 26 categories) from a novel in situZooglider. We document asymptotically increasing performance with more computationally intensive techniques, such as substantially deeper networks and artificially augmented data sets. Our best model achieves 92.3% accuracy with our 27‐class data set. We provide guidance for further refinements that are likely to provide additional gains in classifier accuracy. 

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