AI-Powered Tracking for Sustainable Marine Ecosystem Resource Management Projects: A Case of Oyster Detection With Machine Learning

Ecosystems are our planet's life-support systems that facilitate sustainable development. Within the marine ecosystem, oysters serve as a keystone species. Numerous oyster restoration projects have been launched with a crucial element involving precise assessment of oyster population sizes with...

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Bibliographic Details
Published in:International journal of information technology project management 2024-01, Vol.15 (1), p.1-13
Main Authors: Chau, Toby, Zhang, Helen Lv, Gui, Yuyue, Lau, Man Fai
Format: Article
Language:English
Subjects:
Computer vision
Datasets
Machine learning
Marine resources
Object recognition
Oysters
Project management
Resource management
Restoration
Strategic management
Sustainable development
Tracking
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Summary:Ecosystems are our planet's life-support systems that facilitate sustainable development. Within the marine ecosystem, oysters serve as a keystone species. Numerous oyster restoration projects have been launched with a crucial element involving precise assessment of oyster population sizes within specific reef areas. However, the current methods of tracking oyster populations are approximate and lack precision. To address this research gap, the authors developed an AI-empowered project for oyster detection. Specifically, they created a dataset of wild oysters, utilized Roboflow for image annotation, and employed image augmentation techniques to augment the training data. Then, they fine-tuned a YOLOv8 computer vision object detection model using their dataset. The results demonstrated a mean average precision (mAP) of 85.2 percent and an accuracy of 87.7 percent for oyster detection. This approach improved upon previous attempts to detect wild oysters, offering a more effective solution for population assessment, which is a fundamental step toward sustainable oyster restoration project management.
ISSN:1938-0232
1938-0240
DOI:10.4018/IJITPM.334716