Identify tobacco plant health using AI

Below is a free classifier to identify tobacco plant health. Just upload your image, and our AI will predict if the tobacco plant is healthy or unhealthy - in just seconds.

API Access


import nyckel

credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("tobacco-plant-health", "your_image_url", credentials)


fetch('https://www.nyckel.com/v1/functions/tobacco-plant-health/invoke', {
    method: 'POST',
    headers: {
        'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
        'Content-Type': 'application/json',
    },
    body: JSON.stringify(
        {"data": "your_image_url"}
    )
})
.then(response => response.json())
.then(data => console.log(data));


curl -X POST \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer YOUR_BEARER_TOKEN" \
    -d '{"data": "your_image_url"}' \
    https://www.nyckel.com/v1/functions/tobacco-plant-health/invoke

How this classifier works

To start, upload your image. Our AI tool will then predict if the tobacco plant is healthy or unhealthy.

This pretrained image model uses a Nyckel-created dataset and has 2 labels, including Healthy and Unhealthy.

We'll also show a confidence score (the higher the number, the more confident the AI model is around if the tobacco plant is healthy or unhealthy).

Whether you're just curious or building tobacco plant health detection into your application, we hope our classifier proves helpful.

Need to identify tobacco plant health at scale?

Get API or Zapier access to this classifier for free. It's perfect for:

Disease Detection: Utilize the tobacco plant health identifier to identify and classify diseases affecting the tobacco crop. This function can enable farmers to detect early signs of diseases like bacterial wilt or root rot, allowing for timely intervention and treatment.

Pest Management: Implement the classification function to recognize pest infestations in tobacco plants. By accurately identifying the presence of pests such as aphids or whiteflies, farmers can apply targeted pest control measures, reducing pesticide use and increasing crop yield.

Nutritional Deficiency Diagnosis: Leverage the image classification tool to assess and identify nutritional deficiencies in tobacco plants. This capability can guide fertilizer application strategies, ensuring that plants receive the correct nutrients to maximize health and production.

Harvest Readiness Assessment: Use the tobacco plant health identifier to evaluate plant health and readiness for harvest. This allows farmers to optimize harvesting schedules, ensuring that crops are harvested at their peak quality for improved market value.

Field Monitoring: Integrate the classification function into field monitoring systems to provide ongoing assessments of plant health across large areas. This enables agronomists and farm managers to monitor crop health trends over time, aiding in proactive management decisions.

Precision Agriculture Insights: Employ the tobacco plant health identifier as part of a precision agriculture toolkit. By analyzing individual plant health images, farmers can gain insights into specific areas of the field requiring different management practices, promoting efficient resource use.

Research and Development: Utilize the classification technology for research purposes in developing improved tobacco varieties. Researchers can analyze health data from different plant types, helping to create more resilient crops capable of withstanding diseases and environmental stressors.