Universe

merge_field_crop Computer Vision Dataset

bysensorTask:
Object Detection
License:CC BY 4.060 views2 downloads
See all 1.5k imagesmerge_field_crop crop sample showing Cabbagemerge_field_crop crop sample showing cabbagemerge_field_crop crop sample showing cabbage, radish-1merge_field_crop crop sample showing Cabbage, radish_1merge_field_crop crop sample showing cabbage, radish_1merge_field_crop crop sample showing cabbage, radish-1merge_field_crop crop sample showing Radish_1, Radish_2merge_field_crop crop sample showing cabbagemerge_field_crop crop sample showing cabbagemerge_field_crop crop sample showing radish_2merge_field_crop crop sample showing cabbagemerge_field_crop crop sample showing cabbage

About merge_field_crop Dataset

Here are a few use cases for this project:

  1. Precision Agriculture: Farmers can use the "merge_field_crop" model to identify and differentiate between various types of cabbage and radish crops growing in their field. This can help in estimating crop yield and planning effective harvesting and maintenance strategies.

  2. Agricultural Research: Researchers studying different crop variation patterns can use this model to accurately identify different crop classes in their field experiments. It can aid in a deeper understanding of growth and adaptation characteristics for each crop class.

  3. Agricultural Databases: Agricultural enterprises can use this model to populate their image databases with accurate classification of particular varieties of cabbage and radish. It can be used to enhance the search and filter options within such databases, which could also serve as lease/lending marketplaces for growers worldwide.

  4. Drone-Based Crop Monitoring: Using this model combined with drone technology, companies can provide services to farmers for field monitoring. This could identify different crop classes from aerial images, allowing large-scale farmers to effectively manage their crop fields.

  5. Agricultural Education and Training: Educational institutions specializing in agriculture can use this model as a teaching tool for students learning about different crops. The system can enable training on crop differentiation, growth stages, and creation of digital portfolios associated with specific field assignments.

Use Free Cabbage, Cabbage and Radish_1 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": "cabbage, Cabbage, radish_1, Radish_1, radish_2"
  },
  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{ merge_field_crop_dataset,
  title = { merge_field_crop Dataset },
  type = { Open Source Dataset },
  author = { sensor },
  howpublished = { \url{ https://universe.roboflow.com/sensor-hq923/merge_field_crop } },
  url = { https://universe.roboflow.com/sensor-hq923/merge_field_crop },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { nov },
  note = { visited on 2026-07-29 },
}

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