Method and device for identifying fish habitat and predicting resource distribution based on machine learning

By employing machine learning-based methods, utilizing the YOLOv8 model and dynamic clustering algorithm, a multi-scale monitoring network was constructed. This solved the problems of high cost, long cycle, and low accuracy of traditional fish habitat identification methods, achieving efficient fish habitat identification and resource distribution prediction, and supporting scientific decision-making in fishery resource management.

CN122416083APending Publication Date: 2026-07-17GUANGDONG OCEAN UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GUANGDONG OCEAN UNIVERSITY
Filing Date
2026-02-27
Publication Date
2026-07-17

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Abstract

本发明公开了基于机器学习的鱼类栖息识别与资源分布预测方法及装置，通过构建多尺度监测网络采集水下图像与环境参数，采用YOLOv8模型实现鱼类目标检测与特征提取，结合动态聚类算法与灰色关联分析实现栖息概率序列的时空建模。该方法通过自适应聚类半径调整机制优化区域栖息特征的聚类过程，并计算栖息指数，利用双概率均化模型提升栖息概率评估精度，并通过相关性修正实现跨区域栖息影响状态预测。本发明有效解决了传统监测方法精度低、时效性差的问题，为渔业资源管理提供科学决策支持，具有显著的经济与生态价值。
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