AI Spice Scanner & Identifier Online Powered by Shelog
Curious about a spice in your kitchen? Upload a clear photo to explore possible matches with Shelog's AI-powered spice scanner.
Get suggested spice names and AI confidence scores, then confirm the ingredient with its label or supplier. A photo can offer a starting point—not a guarantee of identity or food safety.
Upload or Drop a Photo
Take a picture, choose an image, or drop a spice photo below.
Drop a spice photo here, or choose one from your device.
Method & evidence
How this spice identification works
This is AI-assisted visual recognition, not a laboratory test or a botanical identification key. The scanner suggests names from your image. The sources below explain how to interpret uncertainty and safety limits; they do not validate this scanner's accuracy.
01 · PHOTO
Start with a clear image
Photograph one familiar kitchen spice at a time, in natural light. Keep its shape and texture visible; avoid filters and clutter.
02 · AI SUGGESTIONS
Explore possible matches
Your uploaded image is sent to an AI service, which returns up to three suggested spice names with confidence scores.
03 · VERIFICATION
Confirm before cooking
Compare the suggestion with the packaging or ask the supplier. If identity is uncertain, do not use the photo result as a reason to eat it.
A high AI score is not a safety check.
A score of 90/100 does not mean a verified 90% chance of a correct identification. The scanner cannot certify edibility, allergens, purity, or the absence of contamination.
What happens to my photo, and what does the result contain?
The browser may resize and compress supported images before upload. When you select Scan Photo, the image goes through Shelog's server to an AI service; identification does not happen solely on your device.
The current result contains suggested names and numeric scores. It does not measure nutrients, heat level, freshness, or chemical composition. The page rounds scores to whole numbers and limits their display to 0–100; that formatting does not improve their accuracy.
Upload only the ingredient you want to examine. Avoid including faces, documents, or other personal information in the frame.
How should I interpret the confidence score?
The score is supplied by the AI service, not calculated from a published spice-specific accuracy study. For example, “cumin · 90/100” means the service returned that label and score—not that 90 of 100 comparable Shelog scans were verified correct.
Calibration research shows why model confidence and observed correctness need to be evaluated separately. Guo and colleagues studied this distinction in image and document classifiers. That research is context, not an evaluation of Shelog's model. [1]
We do not provide a validated accuracy rate, a minimum safe score, or a calibrated probability for this scanner. Multiple suggestions are alternatives to check, not proof that every named spice is present.
Why can similar spices be confused?
A photograph records appearance, not smell, origin, or chemical composition. Grinding, blending, lighting, blur, and packaging can remove or alter the visual clues available to the service. A similar-looking powder can therefore receive a convincing but incorrect suggestion.
Try a sharper photo with one ingredient against a plain background. Include intact pieces when available. Repeated agreement between scans is still not independent confirmation; use the product label or supplier to establish identity.
What can this tool not tell me about safety?
The FDA's spice risk profile documents microbial hazards and contamination in spices. Its evidence concerns food safety, not AI recognition. A visual name suggestion should therefore never be treated as a contamination test. [2]
Use this tool for exploring ordinary kitchen ingredients, not for identifying wild plants, deciding whether an unknown substance is edible, or checking whether food is safe for an allergy. Verify ingredients and allergen information on the packaging or with the supplier.
Research references & what they support
- Guo C, Pleiss G, Sun Y, Weinberger KQ. On Calibration of Modern Neural Networks. Proceedings of ICML, PMLR. 2017;70:1321–1330.
Supports the distinction between confidence scores and empirical correctness. Does not test this scanner or establish its accuracy.
- U.S. Food and Drug Administration. Risk Profile: Pathogens and Filth in Spices. 2017 update.
Documents spice contamination hazards and controls. Does not endorse Shelog or validate photo-based safety assessment.