A pretrained succulent species classifier that sorts an image into one of 10 categories — what species of succulent it is. Use the succulent 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": "Adenium",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 succulent 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 home gardeners in identifying succulent species to ensure proper care and maintenance. By uploading an image of a plant, users can receive detailed information about the specific species, including watering needs and sunlight requirements.
Online plant retailers can integrate this image classification function into their websites or apps to enhance customer experience. Customers can take a photo of a succulent they wish to purchase or identify, and the system will provide product recommendations based on the identified species.
Researchers studying plant biology and ecology can utilize this classification function to accurately catalog succulent species. By analyzing large datasets of plant images, researchers can deepen their understanding of biodiversity and assist in conservation efforts.
Educational institutions can use the succulent species identifier as a teaching tool for botany or horticulture classes. Students can engage in hands-on learning by exploring different succulent species through image classification and gain knowledge about plant characteristics and their habitats.
Plant enthusiasts and social media platforms can implement this function to enhance user-generated content. Users can upload images of their succulents to receive instant identification, fostering engagement and community discussions about plant care tips and sharing information related to specific species.
Landscape architects can use this image classification tool to select appropriate succulent species for specific environments. By identifying the succulents in existing landscapes, designers can make informed decisions about new designs that consider drought resistance and aesthetic harmony.
Developers can integrate the succulent species identifier into a mobile gardening application providing users with a comprehensive guide to succulent care. This app would allow users to quickly identify succulents, access care tips, and even set reminders for watering and fertilization based on the specific species they own.
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 succulent 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.