A pretrained grass species classifier that sorts an image into one of 10 categories — what species of grass it is. Use the grass species API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 26 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Bahiagrass",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 grass species categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
Farmers can utilize the grass species identifier to monitor and differentiate between various grass types on their lands. This helps in assessing pasture health, optimizing grazing patterns, and improving overall crop yield by identifying which species thrive better in specific conditions.
Conservationists can use the identification function to track and monitor native grass species in natural habitats. By accurately identifying invasive species, they can implement effective management strategies to protect local ecosystems and maintain biodiversity.
Landscape architects can leverage the grass species identifier to select appropriate grass types for specific projects, ensuring the chosen species match the local climate and soil conditions. This promotes sustainable landscaping and enhances the aesthetic appeal of gardens and parks.
Seed companies can use this classification tool to ensure that they are marketing the correct grass species to their customers. By verifying species in seed mixes, they can provide better quality assurance and targeted recommendations for specific agricultural needs.
Wildlife managers can utilize the identifier to evaluate habitats, ensuring they contain the right types of grass essential for supporting local wildlife. This information assists in devising management plans that foster healthy ecosystems and promote species conservation.
Urban planners can employ the grass species identifier in urban green space development to ensure that plant species chosen for parks and recreational areas are well-suited to the urban environment. This leads to improved resilience against urban heat and enhanced urban biodiversity.
Educational institutions and researchers can implement the grass species identification function for field studies and to augment curricula in botany and ecology. This hands-on tool provides students with practical experience in species identification and promotes a deeper understanding of local flora.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This grass species classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.