A pretrained plant growth stages classifier that sorts an image into one of 7 categories — what stage of plant growth it is. Use the plant growth stages 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 7 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": "Budding",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 7 plant growth stages 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 be used by farmers to monitor the growth stages of their crops. By analyzing images, it can provide insights into plant health, allowing farmers to make timely decisions regarding irrigation, fertilization, and pest control.
Researchers can utilize the classification function to study the growth patterns of various plant species. By categorizing growth stages, they can better understand the factors influencing plant development, leading to improved agricultural practices and sustainability.
Agribusinesses can implement this function to optimize harvest schedules based on the growth stage of crops. By identifying when plants reach their peak maturity, businesses can maximize yield and reduce waste, ensuring timely harvesting.
Agricultural schools and institutions can leverage this classification function to teach students about plant growth stages. By providing real-time analysis, students can gain practical insights and improve their understanding of plant biology and cultivation techniques.
Developers of gardening applications can integrate this function to assist hobbyists and urban gardeners in monitoring plant development. By informing users of the growth stage of their plants, the app can suggest appropriate care tips and treatments for optimal growth.
Agri-market analysts can use this function to gauge the development of crops in various regions. Understanding growth stages helps in predicting market supply and demand dynamics, enabling better planning and pricing strategies for agricultural products.
Environmental scientists can apply this function in studies assessing the impact of climate change on plant growth. By classifying growth stages over time, researchers can identify trends and correlations, thereby contributing to broader climate adaptation strategies in agriculture.
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 plant growth stages 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.