A pretrained tomato species classifier that sorts an image into one of 10 categories — what species of tomato it is. Use the tomato 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": "Beefsteak Tomato",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 tomato 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.
The 'tomato species' identifier can be used by agricultural producers to ensure that the correct species of tomatoes are being harvested and sold. By classifying images of growing tomatoes, farmers can identify and segregate species that do not meet market standards, reducing waste and improving product quality.
Suppliers can utilize the classification function to ensure that the tomatoes being shipped match customer specifications. By verifying tomato species at the packing stage, businesses can streamline logistics and improve inventory management, ultimately enhancing customer satisfaction.
Researchers in plant sciences can leverage the identifier to classify and catalog different tomato species for academic or horticultural studies. This can aid in biodiversity studies and assist in breeding programs aimed at developing new species or hybrids with desired traits.
Grocery retailers can use the 'tomato species' identifier to verify that the tomatoes being sold to customers are correctly labeled. This function can prevent misrepresentation of products and help maintain trust with consumers regarding the freshness and types of tomatoes offered for sale.
Educational institutions or online platforms can integrate the identifying function into their agriculture and botany courses. This would provide students with practical tools to learn about tomato species and enhance their understanding of plant classification and species identification.
Food manufacturers can use the classification function to ensure the proper use of tomato varieties in food products such as sauces or canned goods. This helps in maintaining flavor profiles and quality assurance for products that rely on specific tomato characteristics.
Environmental organizations can leverage the identifier to promote sustainable practices by helping farmers recognize and classify heirloom and organic tomato species. By encouraging the cultivation of diverse and native species, businesses can contribute to sustainability efforts and biodiversity conservation.
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 tomato 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.