Universe

orangetomato Computer Vision Dataset

bynew-workspace-hwjscTask:
Object Detection
License:CC BY 4.058 views4 downloads
See all 2k imagesorangetomato fruits sample showing fresh_orange751orangetomato fruits sample showing fresh_tomatoorangetomato fruits sample showing fresh_tomatoorangetomato fruits sample showing fresh_tomatoorangetomato fruits sample showing fresh_tomatorotationorangetomato fruits sample showing fresh_tomatoorangetomato fruits sample showing fresh_orange751orangetomato fruits sample showing fresh_tomatoorangetomato fruits sample showing fresh_tomatorotationorangetomato fruits sample showing fresh_orange751orangetomato fruits sample showing stale_orangerotationorangetomato fruits sample showing fresh_orange751

About orangetomato Dataset

Here are a few use cases for this project:

  1. Food Quality Control: The "orangetomato" model can be used in supermarkets and grocery stores to aid in quality control. The model will scan fruits on shelves and identify if they are fresh or stale, helping to maintain freshness and minimize wastage.

  2. Farm Harvest Optimizations: Farmers can use this model to identify the right time to harvest their tomatoes and oranges. The fresh_orangerotation and fresh_tomatorotation classes could give an indication of fruits reaching optimal ripeness.

  3. Automated Sorting in Packaging Industry: The food processing and packaging industry can use this model for segregating the fruits based on their freshness and rotations. It can help automate the process and increase efficiency.

  4. Home Improvement Apps: Apps designed to help users detect rotten fruits in homes can integrate this model. With it, customers can quickly identify rotten tomatoes or oranges on sight.

  5. Pest Control and Research: As the given dataset also includes images of pests (a spider on a rotten tomato), it could be used by pest control companies or researchers to study the relation between fruit rot and pest occurrences. The findings can help develop improved pest control methods.

Use Free Fresh_orange751, Fresh_orangerotation and Fresh_tomato Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "fresh_orange751, fresh_orangerotation, fresh_tomato, fresh_tomatorotation, stale_orangerotation"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicenseCC BY 4.0

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

@misc{ orangetomato_dataset,
  title = { orangetomato Dataset },
  type = { Open Source Dataset },
  author = { new-workspace-hwjsc },
  howpublished = { \url{ https://universe.roboflow.com/new-workspace-hwjsc/orangetomato } },
  url = { https://universe.roboflow.com/new-workspace-hwjsc/orangetomato },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { may },
  note = { visited on 2026-07-29 },
}

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