Abstract

Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by > 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 % improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 %. This resulted in a 97.4 % overall accuracy score and a 96.2 % F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.

Highlights

  • Marine and freshwater plankton are a taxonomically and morphologically diverse group of organisms that span many phyla and tens of thousands of species [1], [2]

  • Rather than constructing a typical ensemble, here we investigate the possibility of developing a classification system which allows multiple unique deep learning models to collaborate when classifying plankton image data (Fig 1C)

  • We describe the image dataset, the four unique ConvNet models configured within this study and an Multi-layer Perceptron (MLP)

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Summary

Introduction

Marine and freshwater plankton are a taxonomically and morphologically diverse group of organisms that span many phyla and tens of thousands of species [1], [2]. Microscopic analysis has been used to identify and enumerate different types of plankton present within a given environment at a given point in time. This approach is both time-consuming and labour intensive, as it relies on the manual processing and classification of plankton present within water samples. Specialists who are able to discern the often subtle morphological differences that distinguish one taxa from another [3] Given these restrictions, over recent years a considerable amount of effort has gone into automating different steps in the sampling and classification process

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