Promise is seductive: Snap a picture of your food, and artificial intelligence will immediately tell you how many calories you are consuming. No more tedious manual logging, no more estimate on the size of the part, no more human error. Cal AI, Loose It!
But as a long, complex history with a calorie count – and it is believed that it has some extent cursed expertise – I can tell you that the calorie count with a photo is as stupid as it seems.
How to work to work AI-Interested Calorie
Calorie counting apps have been promised that developers claim that the biggest problem with calorie tracking is: Human error. The pitch is compelling – when your phone can analyze your plate immediately, why spend time to measure databases and measurements?
Apps such as Cal AI or Snapcalorie AI use visual signals such as color, texture, and relative size, which you are eating and how much it is in it to make educated estimates about it.
They claim that AI methods can solve the pesky problem of human accuracy in calorie estimates – which is easy to be fair, wrong. Cal AI bring himself to the market as one of the more sophisticated options in this place, so I decided to see myself. The app was free for the first three days, then $ 29.99/year.
The setup process is straight: Download the app, create an account, input basic demographic information, and set your goals. Here I had to face my first red flag. The app happily informed me that “Losing 10 LBS is a realistic goal” – it suggests that losing 10 pounds would actually be pushed to me into the low weight BMI sector. The statement of such a blanket reveals the lack of nuances about personal health requirements.
Cal AI’s photo logging process follows these steps:
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Take a clear picture of your food, ideally against a plain background.
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Ensure that all the materials are visible and well burnt.
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Include a reference object (such as coin or your hand) for the scale.
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Upload the image and wait for AI analysis.
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Review and correct the estimates of the app and correct the estimates of the app.
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Save the entry in your daily log.
The app provides detailed suggestions to achieve better results: use natural light, avoid shade, keep the camera parallel to the plate, and make sure that no material is hidden. These guidelines seem appropriate in principle, but they indicate the fundamental challenge of these apps-the complexity of the world’s food.
Reality is wildly disappointing
I started my test with something simple: a pink female apple weighs 222 grams. Certainly it will be an easy win for AI – with a specific shape and color, one of the most photographed foods on Earth that must be immediately recognizable.
Kaili confidently recognized my apple as Tikka Masala.
Gorgeous Tikka Masala, Yes?
Credit: Meredith Diets
I gave it another chance, this time seated on the kitchen scale that photographed with apples with my barcode and displaying its exact weight. The app recognized it as an Apple this time, but when it was estimated at 80 calories Actual calculation It should have been close to 120. It is underestimated by reducing a 33% – not exactly the accuracy that you want if you are trying to track intake.
The actual test came up with a more complex food: my current meal-lunch was prepared with a fried tofu, onion, cucumbers, tomatoes, feta cheese and chickpeas, all generously prepared with an oil-based homemade vinigrate. It is a mixed dish that possibly displays the advantage of AI on manual logging – no need to discover or estimate their quantity.
The results were a masterclass in algorithm overconfidence. The app recognized the Golden-Brown Fried Tofu as Crauton, which I had to correct manually. This did a good job to identify vegetables and feta, but completely emphasized the oil content. Despite the visually glowing the salad with dressing, the app estimated the entire dish on 450 calories.
This estimate was less than laughter. Haples in a single can About 400 caloriesAnd my part included broadly that amount as well as significant amounts of feta cheese and several tablespoons of olive-oil-based dressing. The count of a realistic calories for this food would have been close to 800 to 900 calories.
The estimate of the app was more problematic than its component identity. When I took a picture of a small serving – CAL AI estimated 250 calories by a quarter of the original salad. According to its argument of the app, less than 25% of the meal was more than 55% of its calories. Mathematics simply does not work.
Cal AI was the way, the path was closed.
Credit: Meredith Diets
This photo-based calorie highlights a fundamental range of counting: cameras capture two-dimensional images of three-dimensional objects. Without frequent reference points or refined depth analysis, anticipation of volume from photos is largely estimated. Even humans struggle with this task, which is why nutrition professionals usually recommend weighing foods for accuracy.
To get a full picture of AI Calori counting landscape, I also tested two other popular apps: Snappelori and Calorie Mama.
Snapcalorie: Better Number, Equal Problems
Snaplori Cal AI immediately accepted some doubts by suggesting a much more appropriate daily calorie target of 1,900 calories, compared to messaging of Cal AI. However, this accuracy only comes at a price of $ 79.99 per year after a week’s free testing, which is the most expensive option I tested.
The application provides an interesting feature: a “Note Ed” function that allows you to provide additional references about ingredients that cannot see the camera. In theory, it addresses one of the fundamental boundaries of photo-based tracking.
Snapcalorie has a useful “Add Note” feature and more accurate results.
Credit: Meredith Diets
When I tested the snapleori with the same pink female apple, it performed much better than the Cal AI, estimating 115 calories. But familiar problems were detected in Greek Salad test. The initial estimate of Snaplori was an absurd low 257 calories. When I photographed a small portion-a sub-fourth serving that estimated the Kal AI-Snaplar that 184 calories were estimated. Mathematics still did not work; This small portion should have been about 25% of the large serving, not 70%.
The app was determined to give a fair shot, I used the note feature to specify “Tofu, Feta, Chole and Olive oil full container”. With this human intervention, Snapaklori collided with 761 calories – much more appropriate and accurate, although still on the low side.
But this enhances the clear question: If I need to manually input a wide component information to get accurate results, what is the really photo to complete? I am essentially passing through the movements of taking pictures and counting traditional calories.
What do you think so far?
Calorie Mama: When AI doesn’t even try
Calorie maternal uncle provided the most disappointing and laughter experience of three apps. The interface seems underdeveloped, and the performance of AI is so poor that the app essentially leaves the basis of automated photo analysis.
After uploading a photo, calorie maternal uncle needs you to confirm not only foods but also their share size. This photo-based logging defeats the entire purpose of logging-you are doing all the work that the manual entry will need anyway.
When I uploaded a picture of my Greek salad, the calorie maternal uncle recognized it as “tofu” -friends, fetta cheese, chickpeas and dressing completely. The app then asked me to manually adjust the shape of the part and complete the logging, such as a complex mixed dish had nothing but plain tofu.
It was not just wrong; It was useless. At least Cal AI and Snaplarry attempted to identify many materials, even if their calories were stopped. Calorie uncle appeared completely on the core challenge, assuming AI again on a gimmick photo storage system.
The count of AI-operated calorie wasted my time
The promise of AI-Interested Calorie count is efficiency- Snap and Go, no manual entry is required. But my experience revealed a different reality. I spent a lot of time to correct the identity identity, adjusting the shape of the part and another estimate of the app estimates. In many cases, I would be faster using traditional manual logging with a food scale and database search.
This creates a disappointing coincidence: if you do not check the results of AI, you will get wildly wrong data. But if you verify every entry, you lose the time saving profit that is appropriate by using technology at first. It is the worst in both worlds – attempted manual tracking joint manual tracking with uncertainty to automatic estimates.
Perhaps what is the most when users do not have a background to identify incorrect estimates. My years of Calorie count experience – that history may be possible – when I came to know about knowledge when the number of Cal AI was closed. But what about users who rely on technology?
Calorie can be particularly harmful for people trying to lose weight by reducing systematic reduction, as they may believe that they are actually eating at least. In contrast, overestimation can cause unnecessary restrictions or concern around food. In any way, the wrong data reduces the entire purpose of tracking.
The fundamental issue with AI calorie counting apps is not just technical – it is philosophical. These devices come out of the idea and strengthen the idea that the exact calorie tracking is both necessary and beneficial for health. But research suggests This obsessive calorie count can cause more harm to many people.
Spontaneous food, which focuses on internal hunger and satisfaction signs rather than external matrix, has shown a promise as a more durable and psychological approach to nutrition. This structure emphasizes developing a healthy relationship with food how it makes you feel instead of killing specific numeric goals.
For most people, understanding the general principles of balanced nutrition-reducing all the vegetables, choosing whole grains on sophisticated people, including enough proteins-provides better long-term results than careful calorie tracking.
Bottom line
AI-operated calorie counting apps promise to solve human error in dietary tracking, but they introduce new forms of inaccuracy while maintaining many chronic problems. If your goal only estimates how many calories are in normal foods, these apps can provide some value. But to seek accuracy in their intake trekking, traditional methods combined with food scales are more reliable.
More importantly, I will question whether the exact calorie counting meets your health goals. For many people, developing a more spontaneous relationship with food-a satisfaction, the level of energy and numerical goals, for the physical and mental health based on overall well-being. It may be that the old -fashioned approach to listening to our body works better than any algorithm.
