Identify presence of tobacco leaves
using AI
Below is a free classifier to identify presence of tobacco leaves. Just upload your image, and our AI will predict if there are tobacco leaves present - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("presence-of-tobacco-leaves", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/presence-of-tobacco-leaves/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/presence-of-tobacco-leaves/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict if there are tobacco leaves present.
This pretrained image model uses a Nyckel-created dataset and has 2 labels, including Tobacco Leaves Absent and Tobacco Leaves Present.
We'll also show a confidence score (the higher the number, the more confident the AI model is around if there are tobacco leaves present).
Whether you're just curious or building presence of tobacco leaves detection into your application, we hope our classifier proves helpful.
Need to identify presence of tobacco leaves at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Agricultural Monitoring: This function can be used by agricultural companies to monitor fields for the presence of tobacco plants accurately. By using image classification, farmers can optimize their crop management strategies, ensuring healthy growth and timely harvesting.
- Compliance Verification: Regulatory agencies can employ this identifier to ensure that tobacco farms comply with government regulations. By analyzing images of agricultural lands, they can monitor unauthorized cultivation and enforce regulations effectively.
- Supply Chain Optimization: Tobacco suppliers can utilize this function to track the sourcing of raw materials in their supply chain. By identifying fields growing tobacco leaves through imagery, suppliers can assess the quality and authenticity of their crop sources.
- Environmental Impact Assessment: Environmental organizations can leverage this technology to assess the spread of tobacco cultivation in sensitive ecosystems. By monitoring land use changes, they can evaluate potential ecological impacts and advocate for sustainable practices.
- Smart Agriculture Solutions: Companies developing precision farming tools can integrate this image classification function into their platforms. This enables real-time monitoring of tobacco crops, allowing farmers to apply targeted treatments and enhance yields.
- Research and Development: Academic institutions and research organizations involved in agricultural studies can use this function to streamline data collection. By efficiently identifying tobacco leaves in images, researchers can focus on analyzing plant genetics, disease resistance, and growth patterns.
- Market Analysis: Businesses in the tobacco industry can utilize image classification to conduct market analysis and trends. By analyzing images from various regions, companies can assess the prevalence of tobacco cultivation and adjust their marketing strategies accordingly.