How Artificial Intelligence Is Used in the Food Industry

Artificial intelligence (AI) is a term used to describe a computer-generated intellect that can learn to think, plan, comprehend, and analyze natural language. It’s the study and development of computer systems capable of doing things that would normally require human intelligence, such as vision, speech recognition, decision making, and language translation.

In other words, it’s an area of computer science that focuses on developing machines to act like humans.

In the food industry, where developing standard, reliable procedures to control product quality is a major goal, the search for new ways to reach and serve customers while keeping costs low has necessitated the use of AI. Today, the food industry uses AI to improve customer experience, supply chain management, operational efficiency, warehouse management, and vehicle activity minimization.

Here, we look at how AI is helping the food industry to better meet consumer expectations.

Artificial Intelligence and the Food Industry

Here are some of the most important ways that AI is helping the food industry to reshape its approach toward consumer expectations.

1. Automation during Food Sorting. Many food processing facilities today use manual sorting to sift and separate food items such as vegetables, resulting in decreased efficiency and higher prices. These facilities can achieve substantial automation during this process with AI, which uses a mix of cameras, scanners, and algorithms to enable more efficient food sorting. For example, by using AI with sensor-based optical sorting technologies, the time-consuming processes for sorting fresh produce can be eliminated, resulting in higher yields with better quality and less waste. The same applies to optimizing portion control cutting in the meat industry: AI assesses the primary product for the optimal yield of cut sizes. The technology is also used to better calibrate machines to manage several product sizes while reducing waste and expenses.
2. Organized and Quick Supply Chain Management. Efficient supply chain management is a critical responsibility for all food producers. Food safety monitoring and testing at every level of the supply chain can help guarantee compliance with industry standards. Now, cost and inventory management can be made much easier with more precise predictions, which is where AI comes in: AI-based picture recognition solutions allow for more efficient and effective product procurement, and many companies have started to adopt them. AI also aids in efficient and transparent product tracking all the way from farms to the consumer, increasing customer confidence in a product.
3. Compliance with Food Safety Protocols. Many food facilities today use AI-enabled cameras to ensure that food employees comply with safety regulations. These cameras use image recognition and object identification algorithms to detect whether workers are following food safety regulations for personal hygiene. If a breach is discovered, the screen pictures are extracted for examination, and the mistake can be corrected in real time. There are also AI-powered applications that allow you to write a HACCP plan.
4. Self-Optimizing Cleaning Systems. Traditional periodic cleaning systems are set up to clean equipment in scheduled cycles, but they operate blindly and are not very resource efficient. With the use of AI-enabled technologies, food processing facilities can clean equipment more efficiently. One example is the self-optimizing clean-in-place system (SOCIP), which uses ultrasonic sensors and optical fluorescence imaging to analyze food residue and microbiological debris in a piece of equipment, enhancing the cleaning process. SOCIP saves water, time, and energy; the cleaning time can be reduced by more than half.
5. Predicting Consumer Preferences. Food producers today also employ AI-based solutions to anticipate and model their target consumers’ flavor preferences, as well as to forecast their reactions to novel flavors. For example, in 2017, Kellogg introduced AI-enabled technology that assists customers in choosing which granola to use from a list of 50 components when creating a personalized product. The AI gives suggestions for what items to use in the granola and tells the consumer whether or not the ingredients will work well together. Customers aren’t the only ones who benefit from this technology. The data generated from flavor combinations, the selections people really make, and the variations they reorder is highly useful to any manufacturer when creating new products. Similarly, Coca-Cola has put self-service soft drink fountains at many restaurants and other venues, allowing customers to create their own beverages. Customers can make hundreds of different soda cocktails using these self-service devices by mixing different flavors into their basic beverages. Thousands of drink fountains, each pouring a multitude of new beverages every day, generate a vast quantity of consumer preference data, which Coca-Cola can then use AI to analyze.
6. AI-Based Revenue Predictions. Predicting sales production is an important aspect of any food business. For greater business growth and profit, food chain or restaurant owners must develop solid business strategies for their future operations. Finding an appropriately fitting algorithm for sales forecasts in the food sector, whether it’s one for five months of sales predictions or one for 14 months, is typically time-consuming work. But in this age of data science, it’s now possible to acquire sales forecasts at the touch of a button. Data science allows businesses to discover the optimal algorithm for predicting sales and achieve the rapid deployment of that algorithm within the organization with the help of an expert AI development team.

How AI Helps Consumers Directly

But what about consumers in the food industry? Can AI help them as well? Here are four ways AI does just that: