Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning
This addresses the need for efficient monitoring of harmful phytoplankton for environmental protection, though it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of classifying harmful phytoplankton from microscopic images by evaluating several CNN models with transfer learning, achieving 96.97% accuracy with ResNet-50 and fine-tuning, but noted difficulties in distinguishing four similar types.
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.