Universe

leaf_detection Computer Vision Dataset

byHyeokikiTask:
Object Detection
License:CC BY 4.078 views1 download
See all 1.1k imagesleaf_detection leaf sampleleaf_detection leaf sample showing 0leaf_detection leaf sample showing 0leaf_detection leaf sample showing 0leaf_detection leaf sample showing 0leaf_detection leaf sampleleaf_detection leaf sampleleaf_detection leaf sampleleaf_detection leaf sample showing 0leaf_detection leaf sampleleaf_detection leaf sample showing 0leaf_detection leaf sample showing 0

About leaf_detection Dataset

Here are a few use cases for this project:

  1. Agriculture Health Analysis: This model can be applied to monitor the health of crops in agriculture sectors. The insight could detect signs of plant disease, pest damage, or nutrient deficiencies, informing early detection measures and control strategies.

  2. Botanical Research and Classification: Scientists in the field of botany could use the "leaf_detection" model to classify different species of plants based on their leaves, speeding up their research work and boosting the accuracy of plant identification.

  3. Forest Conservation and Biodiversity: Conservationists could utilize this computer vision model to analyze leaf species in a forest remotely. Detecting changes in leaf type could help monitor biodiversity or invasive species spreading.

  4. Urban Planning and Landscaping: For public parks or city green spaces, urban planners could use the model to identify and plan for appropriate plant species based on their leaf type and their requirements.

  5. Educational Tools: The model could be used in the development of educational applications or interactive games focused on teaching users about different types of plants or trees. By recognizing and classifying different leaf types, students could learn more about plant diversity easily.

Use Free ' and 0 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"
  },
  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{ leaf_detection-rttxg_dataset,
  title = { leaf_detection Dataset },
  type = { Open Source Dataset },
  author = { Hyeokiki },
  howpublished = { \url{ https://universe.roboflow.com/hyeokiki/leaf_detection-rttxg } },
  url = { https://universe.roboflow.com/hyeokiki/leaf_detection-rttxg },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { aug },
  note = { visited on 2026-07-29 },
}

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