Peanuts Computer Vision Model
How to use the Peanuts 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="peanuts-mckge-hmlhx/1")Or, Use Free ', With mold and Without mold 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": "', with mold, without mold"
},
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 Peanuts Model
Here are a few use cases for this project:
-
Quality Control in Food Industry: Utilize the "Peanuts" computer vision model in food processing plants to automatically sort and separate peanuts based on their quality (with mold, without mold), ensuring that only high-quality peanuts are used in the production of peanut-based products such as peanut butter, candies, and snacks.
-
Agriculture and Harvesting: Integrate the "Peanuts" model into agricultural machinery or drones to identify and monitor the quality of peanuts during the harvesting process. This would help farmers optimize their yield and reduce mold contamination in their peanut crops.
-
Health and Safety Inspection: Equip health inspectors or food safety officers with the "Peanuts" computer vision model to quickly assess the quality of peanuts stored in warehouses or retail stores, reducing the risk of mold-related foodborne illnesses and improving overall food safety.
-
Research and Analysis: Use the "Peanuts" model as a tool for researchers and analysts studying mold development in peanuts, allowing for easy categorization and examination of peanuts under different environmental conditions to better understand factors that contribute to mold growth.
-
Peanut Allergy Detection Tool: Pair the "Peanuts" model with other computer vision models to create an app that can identify potential allergens in food images by detecting peanuts, assisting individuals with peanut allergies in making informed decisions about the safety of their food choices.
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{ peanuts-mckge-hmlhx_dataset,
title = { Peanuts Dataset },
type = { Open Source Dataset },
author = { solanodz },
howpublished = { \url{ https://universe.roboflow.com/solanodz/peanuts-mckge-hmlhx } },
url = { https://universe.roboflow.com/solanodz/peanuts-mckge-hmlhx },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { jul },
note = { visited on 2026-07-29 },
}