Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/124985
Title: AquaVision : AI-powered marine species identification
Authors: Mifsud Scicluna, Benjamin
Gauci, Adam
Deidun, Alan
Keywords: Image analysis
Machine learning
Neural networks (Computer science)
Science -- Social aspects
Introduced organisms
Issue Date: 2024
Publisher: MDPI
Citation: Mifsud Scicluna, B., Gauci, A., & Deidun, A. (2024). AquaVision : AI-powered marine species identification. Information, 15(8), 437.
Abstract: This study addresses the challenge of accurately identifying fish species by using machine learning and image classification techniques. The primary aim is to develop an innovative algorithm that can dynamically identify the most common (within Maltese coastal waters) invasive Mediterranean fish species based on available images. In particular, these include Fistularia commersonii, Lobotes surinamensis, Pomadasys incisus, Siganus luridus, and Stephanolepis diaspros, which have been adopted as this study’s target species. Through the use of machine-learning models and transfer learning, the proposed solution seeks to enable precise, on-the-spot species recognition. The methodology involved collecting and organising images as well as training the models with consistent datasets to ensure comparable results. After trying a number of models, ResNet18 was found to be the most accurate and reliable, with YOLO v8 following closely behind. While the performance of YOLO was reasonably good, it exhibited less consistency in its results. These results underline the potential of the developed algorithm to significantly aid marine biology research, including citizen science initiatives, and promote environmental management efforts through accurate fish species identification.
URI: https://www.um.edu.mt/library/oar/handle/123456789/124985
Appears in Collections:Scholarly Works - FacSciGeo

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