Barcode meal logging reads a packaged food label and pulls nutrition from a database; photo meal logging estimates items and portions from an image. Barcodes win on yogurt, bars, and frozen meals with accurate serving sizes; photos win on home-cooked plates, cafeterias, and dining out where no barcode exists.
Most realistic weeks mix both—scan breakfast packaging, photograph lunch, reuse a saved recipe for dinner.
Barcode strengths and limits
- Strengths: Fast on packaged goods, stable macros when the label matches what you ate.
- Limits: Wrong serving multiplier (logged whole bag vs one handful), outdated database entries, no barcode on produce or bulk bins.
Photo strengths and limits
- Strengths: Mixed plates, restaurant portions, meal prep containers without scanning each ingredient.
- Limits: Hidden oils, sauces, and depth in bowls—always review the AI draft portion.
Decision shortcut
If the food has a scannable label you actually consumed as labeled, scan. If food is plated or cooked, photograph or use a saved template. Full guide: how AI meal photo logging works. Ohga Copilot supports photo-first logging with goal-aware portion suggestions after you confirm.
