A pretrained edible plants classifier that sorts an image into one of 2 categories. Use the edible plants 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 2 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": "Edible",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 edible plants 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.
Farmers can utilize the edible plants identifier to ensure that their crops are indeed safe for consumption. This tool can help in cross-referencing plants during harvest to avoid accidental collection of inedible or toxic varieties.
Cooking schools and culinary programs can integrate the edible plants identifier into their curriculums to teach students about safe foraging and plant identification. This enhances students' knowledge of local flora and promotes sustainable culinary practices.
Conservationists can use the identifier to monitor edible plant populations in various ecosystems, contributing to biodiversity studies. Understanding which edible plants thrive can inform conservation strategies and help maintain healthy habitats.
Health apps can implement this function to help users identify nutritious wild plants that can be foraged for their diets. By providing users with information on edible plants, these apps encourage healthier eating habits and promote the consumption of local foods.
Survival schools and outdoor education programs can use the edible plants identifier to teach students how to safely identify food sources in the wild. This practical knowledge empowers individuals with essential survival skills while fostering a connection to nature.
Businesses in the food industry can employ the identifier for sourcing local and seasonal ingredients directly from foragers or producers. This approach not only enhances transparency in the supply chain but also supports local economies and promotes sustainability.
Eco-tourism operators can offer guided tours where the edible plants identifier is used to engage tourists in plant foraging experiences. This promotes an appreciation of local biodiversity and encourages sustainable practices while providing an educational and enjoyable outdoor activity.
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 edible plants 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.