A pretrained apple tree species classifier that sorts an image into one of 10 categories — what species of apple tree it is. Use the apple tree 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 15 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": "Malus Angustifolia",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 apple tree 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.
Researchers can use the apple tree species identifier to study the genetic diversity among various apple tree species. By accurately classifying apple tree species, they can better understand species-specific traits, environmental adaptations, and resistances to diseases.
Apple tree nurseries can implement this tool to ensure that they are cultivating and selling the correct species to their customers. This would help in maintaining inventory accuracy and enhance customer satisfaction by providing the right species for specific needs.
Farmers can leverage the identification function to detect specific apple tree species that may be more vulnerable to certain pests or diseases. This information can guide targeted pest management strategies and improve crop health, ultimately boosting yields.
Food companies and chefs can use the apple tree species identifier to understand the flavor profiles of different apple varieties. This would allow them to choose the most suitable apple species for specific culinary uses, enhancing the quality of their products.
Conservation organizations can use the tool to monitor and conserve native apple tree species in specific regions. By identifying species accurately, these organizations can develop targeted conservation strategies to protect and preserve biodiversity.
Educational institutions can integrate the apple tree species identifier into their botany or horticulture curriculums. This tool can enhance hands-on learning experiences for students, fostering a deeper understanding of plant classification and ecology.
Integrating the apple tree species identifier into precision agriculture platforms can provide farmers with insights into optimizing their cultivation practices. Data generated from accurate species identification can inform irrigation, fertilization, and harvest timing decisions for improved productivity.
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 apple tree 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.