By Kateryna Arkhypova, Business Analyst @It-Jim

Surprising but true: according to market research, customers prefer apples with a maximum diameter of 75 to 80 mm 🍏 Now you know 🙂 People would obviously struggle to accurately evaluate fruits’ size with their naked eyes. In contrast, computer vision (CV) systems can measure the precise diameter of an apple in the blink of an eye, literally.

CV systems can collect and process a variety of parameters, including size, weight, shape, texture, color, and much more. So how exactly are these systems used in the food domain today? Let’s find out.

AI-based apple sorting machine – demo source

Where and How Vision Can Help: Use-Cases and Advantages

When it comes to the food and beverage segment, it is more common to hear the term “machine vision” (MV) than computer vision. What is the difference?

Though the essential components of vision-based systems are generally the same (digital cameras and image processing software), CV and MV are different terms for overlapping technologies. MV systems traditionally work in manufacturing and practical applications for quality control, inspection, and guidance. At the same time, CV systems are self-contained and do not require the use of a larger machine system, as they go way beyond image processing. In CV terms, an image doesn’t even have to be a photo or a video; it could be an ‘image’ from a thermal or infrared sensor, motion detector, or other sources. 

The current trends and benefits of using vision systems for the food can be summarized as follows:

As you can see, there is a lot to do. While it may appear that most active development is reserved for industry, smart food technology is becoming increasingly accessible to end users. Let’s focus now on the most popular such examples.

How to Cook This Dish or A few Words about Cross-Modal Recipe Retrieval

The recommendation of recipes along with food might be the next “Shazam” for food, but, unfortunately, it still seems technically challenging. The problem of recipe retrieval comes from two aspects. First, current food recognition technology can only scale up to a few hundreds of categories, making it impractical to recognize tens of thousands of food categories. Second, even within a single food category, recipe variants may differ in ingredient composition. Finding the best-match recipe thus requires ingredient knowledge, which is a fine-grained recognition problem.

A good run-time example is the Vivino app, the label scanner, which can bring up all the information you need about the wine with a simple photo of a bottle. If you’re trying to make a snap decision in a bottle shop or supermarket, you can find out if the bottle you’re holding is a good deal or if it has the type of smoothness or dryness you’re looking for in a wine. Another plus is that it enables price comparison.

Vivino app – source

Creating New Recipes Based on Consumers’ Trends and Preferences

Today, consumers are increasingly looking for a variety of tasty options for healthy eating. To meet these expectations, entire menus must be reinvented, making it challenging to create new recipes constantly. Fortunately, this problem is now solvable.

The Foodpairing application enables analyzing and determining the compatibility of various food ingredients or discovering your flavor and creating new recipes. It has emerged as a result of multi-disciplinary knowledge from flavor science, food science experts, AI/ML domain, and consumer research. Even if you are too far from the art of cooking, try to play with a variety of interesting and tasty combinations for fun 😉

Image source

Food Tracking

Food image recognition apps may help improve your food ration by utilizing AI to tell you exactly the nutritional value of what is on your plate. Simply take a picture of your meal, and a food recognition platform will tell you exactly what it contains, including the main ingredient, side dishes, and even sauces.

Such programs can estimate portion sizes, nutrition, and calories, which is ideal for those who care about their health and keep their bodies in good shape. For example:

Real-time detection mode (left) and nutrition analysis from the local gallery (right)
on the FoodTracker app –

To Sum Up

As it is in many other industries, AI is making huge waves in the food and beverage field. More and more companies recognize the potential of vision-based systems to improve efficiency and profitability, reduce losses, and protect against supply chain disruptions. This has resulted in the increased adoption of smart technologies in food production. And while it is having a significant impact in the industry, we are still in the early stages of its application as the end-users. Due to the costs associated with their implementation, such technologies are currently used primarily by large manufacturers. However, it is unavoidable that AI will one day become ubiquitous throughout the industry and more accessible to everyone.

Computer Vision in the Food Domain