A pretrained olive tree species classifier that sorts an image into one of 10 categories — what species of olive tree it is. Use the olive 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 25 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": "Amfissa",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 olive 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.
This function can assist agricultural researchers in identifying and classifying different olive tree species when analyzing their growth characteristics and resistance to diseases. By using accurate species classification, researchers can tailor their studies to focus on specific traits or resilience factors unique to certain olive tree species.
Farmers can leverage this function to accurately identify olive tree species in their orchards, enabling targeted cultivation practices. By understanding which species thrive best in their local conditions, farmers can optimize their planting strategies and improve yield.
Eco-tourism operators can implement this function to provide educational resources for tourists interested in olive cultivation. By correctly identifying olive tree species, operators can offer guided tours that promote awareness of biodiversity and sustainable agriculture practices.
Producers of olive oil can utilize this function to ensure their raw materials consist of the intended olive tree species known for specific flavor profiles and quality. Correct identification can lead to better quality control and enhanced marketing strategies based on species distinctions.
Conservation agencies can utilize the species identifier to monitor olive tree diversity in existing ecosystems. Accurate identification helps in developing conservation strategies that maintain or restore biodiversity in olive-growing regions.
Culinary experts and food scientists can use this function to identify various olive tree species that contribute unique flavors and qualities to dishes. Understanding species differences can lead to innovative recipes and the promotion of heritage varieties in gourmet cooking.
Businesses in the agricultural supply chain, including nurseries and seed distributors, can apply this function to accurately identify and market different olive tree species. Better understanding and classification can enhance product offerings and align with consumer preferences for specific varieties and types of olives.
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 olive 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.