A pretrained blueberry species classifier that sorts an image into one of 10 categories — what species of blueberry it is. Use the blueberry 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 20 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": "Blueberry Breeding",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 blueberry 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.
Implement the blueberry species identifier in agricultural settings to automatically assess the quality of blueberry crops. By identifying different species, farmers can make informed decisions on harvesting, ensuring only the best quality berries reach the market.
Employ the classifier in fruit markets to analyze species diversity and trends in blueberry sales. This can help retailers understand consumer preferences and adjust their inventory accordingly, optimizing both sales and customer satisfaction.
Use the identifier in environmental research to monitor and document the presence of various blueberry species in natural habitats. This information can assist conservationists in assessing the health of ecosystems and making data-driven decisions about habitat preservation.
Integrate the classification function into culinary platforms or apps that suggest recipes based on specific blueberry species. Chefs and home cooks can use this information to select the best species for flavor profiles, enhancing culinary experiences.
Incorporate the blueberry species identifier into health and nutrition apps that aim to provide tailored dietary advice. By classifying blueberry species known for differing nutrient profiles, the app can help users optimize their intake of vitamins and antioxidants.
Use the identifier to support seed banks and agricultural developers in classifying blueberry species for breeding programs. This ensures that genetic diversity is maintained, which is vital for developing disease-resistant and high-yielding blueberry varieties.
Implement the classification function on websites or apps focused on consumer education about fruits. By providing information on the characteristics and uses of different blueberry species, users can make more informed choices in their purchases and consumption habits.
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 blueberry 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.