PIsanggg 🍌 Computer Vision Project

Oppai

Updated a year ago

296

views

32

downloads

Metrics

Try This Model
Drop an image or
Description

Here are a few use cases for this project:

  1. Grocery Stock Management: This model could be used by grocery stores and supermarkets to monitor the ripeness of bananas in real-time. With this information, stores can efficiently manage their stock, ensuring rotten produce is disposed of, unripe ones are kept back, and ripe fruits are displayed for purchase.

  2. Smart Agriculture: Farmers, particularly those in large-scale banana production, could use the model to discern the ripeness of their crops. This would facilitate the decision-making process for harvest and reduce waste caused by either premature or belated harvests.

  3. Food Processing Units: This model can help food processing units such as companies that make banana chips, baby food, or smoothie mixes to select the bananas matching their specific ripeness requirements.

  4. Quality Control in Supply Chain: The model can be used in quality control during transportation or packaging of bananas, ensuring that the consignment has the right mix of ripe and unripe bananas.

  5. Consumer application: Finally, this model could be integrated into a mobile app aimed at helping consumers in selecting the right bananas for purchase based on their planned time of consumption. With this tool, they could point their cameras at the bananas and receive real-time feedback on their ripeness state.

Use This Trained Model

Try it in your browser, or deploy via our Hosted Inference API and other deployment methods.

Supervision

Build Computer Vision Applications Faster with Supervision

Visualize and process your model results with our reusable computer vision tools.

Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            pisanggg_dataset,
                            title = { PIsanggg 🍌 Dataset },
                            type = { Open Source Dataset },
                            author = { Oppai },
                            howpublished = { \url{ https://universe.roboflow.com/oppai/pisanggg } },
                            url = { https://universe.roboflow.com/oppai/pisanggg },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-09-22 },
                            }