Universe

persimmon Computer Vision Dataset

byAIOFarmpersimmonTask:
Object Detection
License:CC BY 4.084 views2 downloads
See all 3.8k imagespersimmon AIOFarm sample showing jjikpersimmon AIOFarm sample showing tanpersimmon AIOFarm sample showing jjikpersimmon AIOFarm samplepersimmon AIOFarm samplepersimmon AIOFarm samplepersimmon AIOFarm sample showing ggokpersimmon AIOFarm sample showing bruise, ggok, jjik, tanpersimmon AIOFarm sample showing bruise, jjikpersimmon AIOFarm samplepersimmon AIOFarm samplepersimmon AIOFarm sample showing gallazim, jjik

About persimmon Dataset

Here are a few use cases for this project:

  1. Agriculture Management: The model can be used in farms or greenhouses to identify different persimmon fruits based on their conditions - such as identifying whether they are rotten, bruised, or dirty. This will help streamline the picking process and improve the overall yield quality.

  2. Retail Quality Control: Grocery stores or fruit retailers can use this model to sort and categorize the quality of persimmon fruits. This can aid in removing damaged or rotten fruits, ensuring high-quality produce for their customers.

  3. Food Processing and Packaging: Companies involved in the processing and packaging of persimmon fruits can utilize this model to automate the process of checking for defects, sorting, and packaging, thus reducing labour costs and increasing efficiency.

  4. Education and Research: Institutions engaged in botanical studies and agricultural research can use this AI model to study the pathology of persimmon fruits, recognizing signs of diseases or pests, as they might be indicated by classes such as gallazim and noolim.

  5. Food Waste Management: Organisations focused on reducing food waste can utilize this model to identify and separate rotten or unusable persimmons. This will aid in effective management of waste in both residential and commercial settings.

Use Free Top, Dirty and . 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": "top, dirty, ., \, bruise"
  },
  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{ persimmon_dataset,
  title = { persimmon Dataset },
  type = { Open Source Dataset },
  author = { AIOFarmpersimmon },
  howpublished = { \url{ https://universe.roboflow.com/aiofarmpersimmon/persimmon } },
  url = { https://universe.roboflow.com/aiofarmpersimmon/persimmon },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2021 },
  month = { nov },
  note = { visited on 2026-07-29 },
}

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