Deep Learning Approach for Seagrass Species Classification-Reference-Cited by-同舟云学术

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Deep Learning Approach for Seagrass Species Classification

Published:2024-01-17 Issue: Volume: Page:250-254
ISSN:
Container-title:2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST)
language:
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Author:

Ozaeta Mark Anthony A.¹,Fajardo Arnel C.²,Brazas Felimon P.³,Cantal Jed Allan M.¹

Affiliation:

1. Graduate Programs Technological, Institute of the Philippines-Manila,Metro Manila,Philippines

2. College of Computing Studies, Information and Communication Technology, Isabela State University-Cauayan,Isabela,Philippines

3. College of Science and Computer Studies, De La Salle University-Dasmari ñas,Cavite,Philippines

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10474595/10474607/10474987.pdf?arnumber=10474987

Reference17 articles.

1. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning

2. Deep learning for cellular image analysis

3. A Comparative Assessment of Ensemble-Based Machine Learning and Maximum Likelihood Methods for Mapping Seagrass Using Sentinel-2 Imagery in Tauranga Harbor, New Zealand

4. Real-time and Embedded Compact Deep Neural Networks for Seagrass Monitoring

5. Diversity, distribution, and habitat association of seagrass in Calatagan, Batangas, Philippines

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Age-based clustering of seagrass blades using AI models;Real-Time Image Processing and Deep Learning 2024;2024-06-07

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