Universe

Beany-train-set Computer Vision Dataset

byTHESISTask:
Object Detection
License:MIT
See all 3k imagesBeany-train-set COCOA sample showing FROSTYPODBeany-train-set COCOA sample showing HEALTHYBeany-train-set COCOA sample showing MIRIDBeany-train-set COCOA sample showing BLACKPODBeany-train-set COCOA sample showing BLACKPODBeany-train-set COCOA sample showing HEALTHYBeany-train-set COCOA sample showing HEALTHYBeany-train-set COCOA sample showing FROSTYPODBeany-train-set COCOA sample showing MIRIDBeany-train-set COCOA sample showing MIRIDBeany-train-set COCOA sample showing MIRIDBeany-train-set COCOA sample showing MIRID

About Beany-train-set Dataset

Here are a few use cases for this project:

  1. Agricultural Consultancy: The "COCOA DISEASE DETECTION" model can be used by agricultural consultancies to remotely monitor cocoa plantations, helping them to provide sound advice to farmers about disease prevention, early detection and appropriate treatment.

  2. Pesticide Companies: This model can serve as a diagnostic tool for companies developing pesticides for specific cocoa diseases. By pinpointing the type of disease afflicting the plant, they can tailor their products more efficiently and effectively.

  3. Greenhouse Farming: In a controlled agricultural environment, such as a greenhouse, the model could be used for real-time monitoring of cocoa plants. This would help in early detection of diseases and swift mitigation.

  4. Agricultural Research Institutions: These organisms could use this model for studies on disease progression and the effectiveness of different treatments or resistant crop varieties.

  5. Mobile App for Cocoa Farmers: The "COCOA DISEASE DETECTION" model could be deployed in a mobile app that would permit farmers to identify diseases simply by taking a picture of their cocoa plants. This would help farmers detect and treat diseases early, increasing their overall productivity.

Use Free HEALTHY, BLACKPOD and FROSTYPOD 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": "HEALTHY, BLACKPOD, FROSTYPOD, MIRID"
  },
  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

LicenseMIT

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

@misc{ beany-train-set_dataset,
  title = { Beany-train-set Dataset },
  type = { Open Source Dataset },
  author = { THESIS },
  howpublished = { \url{ https://universe.roboflow.com/thesis-mzdgq/beany-train-set } },
  url = { https://universe.roboflow.com/thesis-mzdgq/beany-train-set },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2025 },
  month = { apr },
  note = { visited on 2026-07-29 },
}

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