Jellyfish Species Identification: A CNN Based Artificial Neural Network Approach

TL;DR

This paper presents a deep learning framework utilizing CNNs and hybrid classifiers for accurate jellyfish species identification from underwater images, achieving 98% accuracy and aiding marine biodiversity monitoring.

Contribution

It introduces a novel hybrid deep learning approach combining multiple CNN architectures with traditional classifiers for jellyfish species detection.

Findings

MobileNetV3 with ANN achieved 98% accuracy

Hybrid models outperform individual CNN classifiers

Deep learning effectively enhances marine species identification

Abstract

Jellyfish, a diverse group of gelatinous marine organisms, play a crucial role in maintaining marine ecosystems but pose significant challenges for biodiversity and conservation due to their rapid proliferation and ecological impact. Accurate identification of jellyfish species is essential for ecological monitoring and management. In this study, we proposed a deep learning framework for jellyfish species detection and classification using an underwater image dataset. The framework integrates advanced feature extraction techniques, including MobileNetV3, ResNet50, EfficientNetV2-B0, and VGG16, combined with seven traditional machine learning classifiers and three Feedforward Neural Network classifiers for precise species identification. Additionally, we activated the softmax function to directly classify jellyfish species using the convolutional neural network models. The combination of the Artificial Neural Network with MobileNetV3 is our best-performing model, achieving an exceptional accuracy of 98%, significantly outperforming other feature extractor-classifier combinations. This study demonstrates the efficacy of deep learning and hybrid frameworks in addressing biodiversity challenges and advancing species detection in marine environments.