Jellyfish Species Identification: A CNN Based Artificial Neural Network Approach

2025/07/15 by Md. Sabbir Hossen, Md. Saiduzzaman, Hossen, Md. Sabbir +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Identification and Quantification in Food #Marine Invertebrate Physiology and Ecology #Venomous Animal Envenomation and Studies

paper · pdf · doi:10.48550/arxiv.2507.11116

openalex publication_date 2025/07/15 · openalex created_date 2025/10/08 · openalex updated_date 2026/07/28

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.

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