ancho-polygon-pakan-segment Computer Vision Project
Updated 2 years ago
Here are a few use cases for this project:
-
Sustainable Aquaculture: The "ancho-polygon-pakan-segment" model can be utilized in monitoring the shrimp farming industry to oversee the growth and health of shrimps, ensuring sustainable aquaculture practices and enhancing harvest efficiency.
-
Fisheries Management: The model can support the management of pakan fish species by providing crucial data on population density, distribution, and net catch, enabling more informed decision-making in conservation efforts and fishing quotas.
-
Environmental Impact Assessment: Researchers and environmental agencies can use the computer vision model to study the impact of human activities (such as pollution, habitat destruction, or overfishing) on the pakan-segment aquatic species, contributing to the development of targeted conservation strategies.
-
Quality Control in Seafood Processing: The model can be adapted to function within seafood processing facilities for automating the identification, sorting, and grading of shrimp and pakan fish, thereby ensuring product quality and reducing the chances of contamination or mislabeling.
-
Ecosystem Monitoring: Utilizing the "ancho-polygon-pakan-segment" model, ecologists can monitor the natural habitats of shrimp and pakan to understand the dynamics and health of aquatic ecosystems, using the information to identify potential threats and drive targeted conservation measures.
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{
ancho-polygon-pakan-segment_dataset,
title = { ancho-polygon-pakan-segment Dataset },
type = { Open Source Dataset },
author = { testing },
howpublished = { \url{ https://universe.roboflow.com/testing-dgymd/ancho-polygon-pakan-segment } },
url = { https://universe.roboflow.com/testing-dgymd/ancho-polygon-pakan-segment },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { feb },
note = { visited on 2024-12-22 },
}