RLDDv2 Computer Vision Dataset
How to use the RLDDv2 Detection API
Try This Model
Drop an image here or click to upload
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="nrn-space/rlddv2-instant-1")Or, Use Free Healthy, Brownspot and Rice blast Detection API
Powered by general detection model
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": "healthy, brownspot, rice blast"
},
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
About RLDDv2 Model
Dataset used for my Final Year Project: Real-time Rice Leaf Diseases Detection using YOLOv8. A YOLOv8-based prototype was developed to detect mainly 2 diseases which are rice blast and brown spot, additional class - healthy leaves.
Raw dataset sourced from Mendeley URL: https://data.mendeley.com/datasets/hx6f852hw4/2. Citation: Hasan, M., Khatun, S., Raihan, M. A., & Uddin, A. H. (2023). Rice leaf bacterial and fungal disease dataset (Version 2)[Data set]. Mendeley Data. https://doi.org/10.1763
Then, it underwent a cleaning process, reducing it to 1,807 images. Using Roboflow platform, these images were then manually annotated and subjected to auto-augmentation steps, expanding the dataset to a total of 5,421 images.
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{ rlddv2_dataset,
title = { RLDDv2 Dataset },
type = { Open Source Dataset },
author = { nrn space },
howpublished = { \url{ https://universe.roboflow.com/nrn-space/rlddv2 } },
url = { https://universe.roboflow.com/nrn-space/rlddv2 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { jul },
note = { visited on 2026-07-29 },
}