10/06/2026
Calorie tracking apps that calculate calories and macros from meal photos are becoming very popular. But they are not accurate yet.
Even under very controlled conditions — standardized plates, lighting, and camera angles — the error rates are large (Fridolfsson et al., 2025):
• Calories: ~36–64% error
• Food weight: ~36–65% error
• Carbohydrates: ~48–73% error
• Protein: ~61–110% error
• Fat: ~42–90% error
Accuracy is worse with larger portion sizes.
To put that into perspective: if a meal actually contains 700 kcal, a 36–64% error means the app could estimate anywhere from roughly 450 to 1,150 kcal.
AI can often recognize foods reasonably well. The biggest limitation is portion size estimation. Hidden ingredients make this even harder. For example, visually it’s almost impossible to tell whether a meal contains one tablespoon of oil or several. This is one of the same reasons people often underestimate calories when tracking food manually.
Accuracy is generally better when foods are simple and separated (Cofré, S., et al., 2025).
If this technology becomes more accurate (and hopefully it will), it would be great. It could eliminate the inconvenience and time consumption of manual tracking — which is one of the main reasons many people don’t want to track or give up after a few days. Taking a photo is far easier than entering food manually and could help more people stay consistent with tracking.
But the technology isn’t there yet. For now, it’s best to treat photo-based calorie estimates as rough guidance, not precise nutrition data.
References
Fridolfsson, J., Sjöberg, E., Thiwång, M. and Pettersson, S. (2025) ‘Performance evaluation of 3 large language models for nutritional content estimation from food images’, Current Developments in Nutrition, 9(10), p. 107556.