Sea_Grass Computer Vision Project
Updated a year ago
Metrics
Here are a few use cases for this project:
-
Marine Ecosystem Study: Researchers or marine biologists can use the Sea_Grass model to analyze various aspects of sea grass habitats such as growth, spreading or die-offs in an ecosystem, ultimately making clearer judgements about marine health.
-
Climate Change Research: By monitoring the growth and health of seagrass, scientists can use this data to study the effects of climate change on underwater plant life and biodiversity.
-
Aquaculture Management: The model could be used to oversee the state of sea grass in aquafeed farms. It would allow for healthier, more sustainable cultivation management practices by identifying and tracking change in sea-grass properties over time.
-
Underwater Image Analysis: The Sea_Grass model can aid in automating the process for organizations involved in creating maps or datasets of underwater imagery by identifying seagrass constituents efficiently.
-
Coastal Management Initiatives: Government organizations can use this model to monitor and manage coastal sea-grass beds, crucial for erosion control and overall coastal ecosystem health.
Use This Trained Model
Try it in your browser, or deploy via our Hosted Inference API and other deployment methods.
Build Computer Vision Applications Faster with Supervision
Visualize and process your model results with our reusable computer vision tools.
Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
sea_grass_dataset,
title = { Sea_Grass Dataset },
type = { Open Source Dataset },
author = { CQUniversitySeaGrass },
howpublished = { \url{ https://universe.roboflow.com/cquniversityseagrass/sea_grass } },
url = { https://universe.roboflow.com/cquniversityseagrass/sea_grass },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jul },
note = { visited on 2024-11-21 },
}