Phytoplankton Image

Phytoplankton image analysis focuses on automating the identification and classification of phytoplankton species from microscopic images, crucial for monitoring aquatic ecosystems and detecting harmful algal blooms. Current research emphasizes developing robust and generalizable deep learning models, including convolutional neural networks, autoencoders for anomaly detection (like parasite identification), and hybrid quantum-classical approaches for improved efficiency and scalability, often addressing challenges like data imbalance and domain shifts between different imaging instruments. These advancements are vital for improving the speed and accuracy of phytoplankton monitoring, enabling better management of aquatic resources and mitigating the impacts of harmful algal blooms on marine environments and aquaculture. Federated learning techniques are also being explored to address data privacy concerns associated with large-scale collaborative studies.