BRIEF RESEARCH REPORT article
Front. Mar. Sci.
Sec. Marine Ecosystem Ecology
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1496831
This article is part of the Research TopicBiological Invasions in Aquatic Ecosystems: Detection, Assessment and CountermeasuresView all 13 articles
Development of A Multi-modal Deep-Learning-Based Web Application for Image Classification of Marine Decapods in Conservation Practice
Provisionally accepted
- 1Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
- 2Shanghai Jiao Tong University, Shanghai, Shanghai Municipality, China
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Marine invasive Decapoda species have caused huge losses to biodiversity and world fisheries. Early awareness of non-indigenous species (NIS) is critical to prompt response and mitigate impacts. Citizen support has emerged as a valuable tool for the early detection of NIS worldwide. However, the great biodiversity of Decapoda species in global oceans poses challenges for the public to the recognize marine Decapoda species, especially for the uncommon or unfamiliar specimens, which sometimes might be NIS. However, despite remarkable performance of Deep learning (DL) techniques in automated image analysis, there remains a scarcity of professional tools tailored specifically for the image classification of diverse decapods. To tackle this challenge, a web application for automated image classification of marine Decapoda species, termed DecapodAI, was developed by training the Fine-tuning Contrastive Language–Image Pretraining model with the images from World Register of Marine Species. For the test dataset, DecapodAI achieved an average accuracy at 0.717 (family), 0.719 (genus), 0.773 (species), respectively. Online service is provided at http://www.csbio.sjtu.edu.cn/bioinf/DecapodAI/. DecapodAI can help alleviate the burden of manually analyzing images. It is expected to promote public participation and has promising application prospects in exploring and monitoring the biodiversity of decapods in global oceans, including early awareness of NIS.
Keywords: Decapoda, Biodiversity, non-indigenous species, invasive species, deep learning, Automated image classification
Received: 15 Sep 2024; Accepted: 03 Apr 2025.
Copyright: © 2025 Zhou, Zhou, Bu, Wang, Shen and Pan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Peng Zhou, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
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