Universe

wheat2 Computer Vision Dataset

byMostafa KarimiTask:
Object Detection
License:CC BY 4.0228 views5 downloads
See all 843 imageswheat2 wheat sample showing wheatwheat2 wheat sample showing wheatwheat2 wheat sample showing wheatNarynwheat2 wheat sample showing wheatwheat2 wheat sample showing wheatwheat2 wheat sample showing wheatwheat2 wheat sample showing wheatwheat2 wheat sample showing wheatwheat2 wheat sample showing wheatwheat2 wheat sample showing wheatwheat2 wheat sample showing wheatwheat2 wheat sample showing wheat

About wheat2 Dataset

Here are a few use cases for this project:

  1. Agriculture Technology: The "wheat2" computer vision model can be implemented in cutting-edge agriculture tech devices or drones for identifying wheat grains. It can be particularly valuable for quality assessment and classification of wheat and wheatNaryn types directly from the field. The harvested grains can be auto-segregated for improved efficiency and precision.

  2. Food Industry: This model could be useful in food quality control in industries dealing with wheat products, such as flour mills, bakeries, pasta or cereal producers. This technology could ensure that only a specific type of wheat, or the highest quality grains are used in product manufacturing.

  3. Research and Development: This machine learning model could be implemented in agricultural or food science research, providing data for studies on grain quality, wheat variety comparisons, or disease resistance among different wheat types.

  4. Retail: This model could help supermarkets or grain-selling stores assure that they are accurately selling the described kind of wheat, offering more value to the customer.

  5. Education: The "wheat2" model can serve as an educational tool in teaching students or farmers about different types of wheat, demonstrating an application of machine learning and promoting digital literacy in agriculture.

Use Free Wheat and WheatNaryn 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": "wheat, wheatNaryn"
  },
  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{ wheat2_dataset,
  title = { wheat2 Dataset },
  type = { Open Source Dataset },
  author = { Mostafa Karimi },
  howpublished = { \url{ https://universe.roboflow.com/mostafa-karimi/wheat2 } },
  url = { https://universe.roboflow.com/mostafa-karimi/wheat2 },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { feb },
  note = { visited on 2026-07-29 },
}

Similar Projects

See More