A pretrained how old a tree is by rings classifier that sorts an image into one of 10 categories — how old a tree is based on its rings. Use the how old a tree is by rings 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 22 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": "1-5 Years",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 how old a tree is by rings 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.
This function can aid forest managers in determining the age of trees in a forest ecosystem. By classifying tree rings, managers can make informed decisions about sustainable harvesting and ensuring the health of the forest.
Organizations engaging in carbon credit programs can use this identification function to verify the age and biomass of trees. Accurate age data can support claims for carbon offsets, promoting transparency and accountability in carbon trading markets.
Researchers in ecology can utilize this tool to study historical growth patterns of trees. By analyzing tree rings, they can glean insights into past climate conditions and ecological changes over time, contributing to a better understanding of environmental sustainability.
City planners can employ this identification tool to assess the age of trees in urban environments. This information is valuable for planning urban green spaces, ensuring that there is a balance between new plantings and the preservation of mature trees.
Real estate developers can use the function to evaluate the age of trees on potential development sites. Understanding the age and health of existing trees can inform decisions about landscaping, conservation requirements, and compliance with local regulations.
Educational institutions can implement this technology in programs focused on environmental science and forestry. By using tree ring analysis, students can learn about dendrochronology, ecology, and the impact of age on biodiversity.
The timber industry can benefit from this identification function when assessing the maturity of trees before harvesting. Accurately classifying tree age ensures sustainable logging practices and helps in determining the quality and value of timber products.
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 how old a tree is by rings 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.