Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning
<|reference_start|>Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning: Monitoring plankton distribution, particularly harmful phytoplankton, is vital for preserving aquatic ecosystems, regulating the global climate, and ensuring environmental protection. Traditional methods for monitoring are often time-consuming, expensive, error-prone, and unsuitable for large-scale applications, highlighting the need for accurate and efficient automated systems. In this study, we evaluate several state-of-the-art CNN models, including ResNet, ResNeXt, DenseNet, and EfficientNet, using three transfer learning approaches: linear probing, fine-tuning, and a combined approach, to classify eleven harmful phytoplankton genera from microscopic images. The best performance was achieved by ResNet-50 using the fine-tuning approach, with an accuracy of 96.97%. The results also revealed that the models struggled to differentiate between four harmful phytoplankton types with similar morphological features.<|reference_end|>
@article{khaldi2024recognition,
    title={Recognition of Harmful Phytoplankton from Microscopic Images using Deep
  Learning},
    author={Aymane Khaldi, Rohaifa Khaldi},
    journal={arXiv preprint arXiv:2409.12900},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12900},
    primaryClass={cs.CV cs.AI}
}