cf_leaf_big_only Computer Vision Dataset
About cf_leaf_big_only Dataset
Early and accurate identification of coffee leaf diseases is critical for timely intervention. Real-world farm images vary widely (lighting, occlusions, backgrounds), making the task challenging. We aim to classify common disease categories and “healthy” leaves under real conditions, minimizing false alarms while maintaining high sensitivity to early symptoms.
Use Free Rust, Cercospora and Corticium 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": "Rust, Cercospora, Corticium, Miner, Phoma"
},
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
Roboflow Agent
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ cf_leaf_big_only-lvof1_dataset,
title = { cf_leaf_big_only Dataset },
type = { Open Source Dataset },
author = { coffee leaf },
howpublished = { \url{ https://universe.roboflow.com/coffee-leaf-wbexw/cf_leaf_big_only-lvof1 } },
url = { https://universe.roboflow.com/coffee-leaf-wbexw/cf_leaf_big_only-lvof1 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { oct },
note = { visited on 2026-07-29 },
}