Convolutional neural networks and vision transformers for Plankton Classification
In this paper, we present a study on plankton classification for automated underwater ecosystems monitoring. The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorith...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S157495412500281X |
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| author | Loris Nanni Alessandra Lumini Leonardo Barcellona Stefano Ghidoni |
| author_facet | Loris Nanni Alessandra Lumini Leonardo Barcellona Stefano Ghidoni |
| author_sort | Loris Nanni |
| collection | DOAJ |
| description | In this paper, we present a study on plankton classification for automated underwater ecosystems monitoring. The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorithms can result in more robust and efficient classification across various plankton datasets. Tests involved different variants of the Adam optimizer and multiple learning rate variation strategies applied to several CNN architectures, building an ensemble of classifiers. Such ensembles were tested together with transformer-based models in a detailed comparative analysis considering feature extraction efficiency, computational cost, and robustness to species imbalances. The study highlights the performance of individual nets and ensembles on multiple plankton datasets, and discusses the potential for generalizing this approach to broader aquatic ecosystems. Experiments demonstrate that combining diverse neural network models in a heterogeneous ensemble significantly improves performance with respect to other state-of-the-art approaches across all the problems considered. Final results show that the ensemble-based approach achieves a remarkable accuracy improvement over individual CNN models and over standalone Vision Transformers. |
| format | Article |
| id | doaj-art-cba121cc69a942ca8343abf4f5e3ebde |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-cba121cc69a942ca8343abf4f5e3ebde2025-08-20T05:05:28ZengElsevierEcological Informatics1574-95412025-12-019010327210.1016/j.ecoinf.2025.103272Convolutional neural networks and vision transformers for Plankton ClassificationLoris Nanni0Alessandra Lumini1Leonardo Barcellona2Stefano Ghidoni3Department of Information Engineering, University of Padova, Via Giovanni Gradenigo, 6b, Padova, 35131, ItalyDepartment of Computer Science and Engineering, University of Bologna, via dell’Università 50, Cesena, 47522, Italy; Corresponding author.Department of Information Engineering, University of Padova, Via Giovanni Gradenigo, 6b, Padova, 35131, ItalyDepartment of Information Engineering, University of Padova, Via Giovanni Gradenigo, 6b, Padova, 35131, ItalyIn this paper, we present a study on plankton classification for automated underwater ecosystems monitoring. The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorithms can result in more robust and efficient classification across various plankton datasets. Tests involved different variants of the Adam optimizer and multiple learning rate variation strategies applied to several CNN architectures, building an ensemble of classifiers. Such ensembles were tested together with transformer-based models in a detailed comparative analysis considering feature extraction efficiency, computational cost, and robustness to species imbalances. The study highlights the performance of individual nets and ensembles on multiple plankton datasets, and discusses the potential for generalizing this approach to broader aquatic ecosystems. Experiments demonstrate that combining diverse neural network models in a heterogeneous ensemble significantly improves performance with respect to other state-of-the-art approaches across all the problems considered. Final results show that the ensemble-based approach achieves a remarkable accuracy improvement over individual CNN models and over standalone Vision Transformers.http://www.sciencedirect.com/science/article/pii/S157495412500281XPlanktonNeural networksEnsembleTransformers |
| spellingShingle | Loris Nanni Alessandra Lumini Leonardo Barcellona Stefano Ghidoni Convolutional neural networks and vision transformers for Plankton Classification Ecological Informatics Plankton Neural networks Ensemble Transformers |
| title | Convolutional neural networks and vision transformers for Plankton Classification |
| title_full | Convolutional neural networks and vision transformers for Plankton Classification |
| title_fullStr | Convolutional neural networks and vision transformers for Plankton Classification |
| title_full_unstemmed | Convolutional neural networks and vision transformers for Plankton Classification |
| title_short | Convolutional neural networks and vision transformers for Plankton Classification |
| title_sort | convolutional neural networks and vision transformers for plankton classification |
| topic | Plankton Neural networks Ensemble Transformers |
| url | http://www.sciencedirect.com/science/article/pii/S157495412500281X |
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