Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning

• URL: http://arxiv.org/abs/2409.12900v1
• Date: Thu, 19 Sep 2024 16:42:53 GMT
• Title: Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning
• Authors: Aymane Khaldi, Rohaifa Khaldi,
• Abstract summary: Monitoring plankton distribution 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. We evaluate several state-of-the-art CNN models, including ResNet, ResNeXt, DenseNet, and EfficientNet, using three transfer learning approaches to classify eleven harmful phytoplankton genera from microscopic images.
• Score: 1.03590082373586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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