Mangosteen Computer Vision Project
Updated 2 years ago
Metrics
Here are a few use cases for this project:
-
Agricultural Quality Control: The "Mangosteen" model can be deployed to automated systems in fruit production factories or farms to sort mangosteens according to their ripeness level. This ensures only ripe fruits get to the market, improving the quality of produce.
-
Retail Inventory Management: Retailers can use this model to manage their inventory effectively by identifying and removing unripe mangosteens, reducing waste and improving customer satisfaction.
-
Smart Farming Solutions: In precision agriculture, this model could be implemented to analyze drone-captured images of mangosteen orchards, helping farmers monitor fruit ripeness at scale and make informed harvesting decisions.
-
Consumer Apps: A mobile application can be developed with the "Mangosteen" model that allows users to snap a photo of a mangosteen to determine whether it's ripe or not, improving their shopping experience.
-
Food Processing Industry: Industries involved in making mangosteen-based products like juice, canned fruits, etc. can employ this model to select appropriately ripe fruits, thus impacting the quality of their end products.
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{
mangosteen-2grlu_dataset,
title = { Mangosteen Dataset },
type = { Open Source Dataset },
author = { MangCutDuyv1 },
howpublished = { \url{ https://universe.roboflow.com/mangcutduyv1/mangosteen-2grlu } },
url = { https://universe.roboflow.com/mangcutduyv1/mangosteen-2grlu },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { dec },
note = { visited on 2024-12-23 },
}