# Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning

arXiv: https://arxiv.org/abs/2409.12900 · HTML: https://mlsanity.com/paper/2409.12900/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 1 of 1)

- [Deep Learning Classification of Lake Zooplankton](https://mlsanity.com/paper/2108.05258/) (2021)

## Abstract (arXiv)

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.