A pretrained tree health classifier that sorts an image into one of 10 categories — the health status of a tree species. Use the tree health 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 29 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": "Bark Damage",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 tree health 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.
The tree health identifier can be used by arborists to assess the condition of trees before undertaking any maintenance or removal. By using the function to classify images, arborists can quickly identify signs of disease or pest infestation, allowing for informed decision-making.
Municipalities can utilize the tree health identifier for urban forestry management programs. By analyzing images of street trees and parks, they can monitor overall tree health and prioritize care for those in poor condition, improving urban green spaces and public safety.
Farmers and agricultural consultants can employ the identifier to monitor orchards and wooded areas. By regularly assessing tree health through images, they can detect early signs of stress or disease, optimizing yield and minimizing potential losses.
Researchers studying ecosystems can use the tree health identifier to gather data on forest health and biodiversity. By classifying images of various tree species, they can analyze the impact of environmental factors on tree vitality, contributing to conservation efforts.
Insurance companies can implement the identifier to assess claims related to tree damage or health. By quickly evaluating the condition of trees in photographs submitted by policyholders, insurers can expedite claims processing and determine coverage.
Landscaping companies can use the tree health identifier to provide assessments to clients about existing trees on their properties. This functionality allows them to offer tailored recommendations for tree care, replacements, and landscaping designs that enhance property aesthetics and health.
Schools and educational organizations can implement this technology for environmental science curricula. By using the tree health identifier in hands-on activities, students can learn about botany, ecology, and the importance of tree health while engaging with real-world applications.
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 tree health 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.