Universe

corn Computer Vision Model

byAGRIDRONEINSIGHTTask:
Object Detection
License:CC BY 4.0

How to use the corn Detection API

Try This Model

Drop an image here or click to upload

Or try a test image 

Code Snippets

from inference_sdk import InferenceHTTPClient

CLIENT = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key="API_KEY"
)

result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="corn-2xipv-uadpf/4")
Give your agent everything it needs

Or, Use Free 0, Corn Plant and Z 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": "0, Corn Plant, z"
  },
  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 
See all 78 imagescorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plantcorn corn sample showing Corn Plant

About corn Model

Here are a few use cases for this project:

  1. Precision Agriculture: The "corn" computer vision model can help farmers and agronomists monitor corn growth, detect diseases or nutrient deficiencies early, and optimize their farming practices by identifying different corn classes and health conditions in fields.

  2. Crop Yield Prediction: By analyzing images of cornfields, researchers and agricultural experts can use the model to predict corn yields more accurately by considering the quantity and health of different corn classes, enabling informed decision making for farmers and supply chain stakeholders.

  3. Automated Harvesting and Planting: The "corn" model can assist in the development of autonomous machinery for planting and harvesting, enhancing efficiency and reducing labor costs. The machinery can use the model to identify various corn classes and accurately navigate through the fields.

  4. Drought Monitoring and Irrigation Management: The "corn" computer vision model can help monitor and detect signs of drought-induced stress in cornfields, allowing farmers to adjust irrigation systems more effectively and conserve water resources by applying targeted irrigation to specific corn classes or affected areas.

  5. Biodiversity and Plant Breeding Research: By identifying different corn classes, the "corn" model can aid researchers in assessing and maintaining genetic diversity in cornfields, supporting plant breeding programs, and increasing crop resilience to pests, diseases, and environmental changes.

Cite This Project

LicenseCC BY 4.0

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

@misc{ corn-2xipv-uadpf_dataset,
  title = { corn Dataset },
  type = { Open Source Dataset },
  author = { AGRIDRONEINSIGHT },
  howpublished = { \url{ https://universe.roboflow.com/agridroneinsight/corn-2xipv-uadpf } },
  url = { https://universe.roboflow.com/agridroneinsight/corn-2xipv-uadpf },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2026 },
  month = { jan },
  note = { visited on 2026-07-29 },
}

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