Artificial Intelligence Application in the Food Industry
The food industry is a very large and complex network of farmers, chefs, truck drivers, and more recently AI. It’s not an outrageous idea, computer advancements are improving the lives of workers across many different fields of work. When technology advances it usually creates opportunities and change that needs to be looked at. In this case, it’s best to not only look at what AI is doing for food industry giants but startups too.
As of right now, AI algorithms are at a level where they can revolutionize the food industry. They are being designed to pinpoint where issues are occurring and find the best ways to fix them. Luckily start-up businesses are at a unique advantage, if they have the ability to embrace these new changes they could go into the future possibly stronger than ever before.
One of the major issues any business tackles is waste. In the case of the food industry, it can take many forms. The UN has reported that one-third of all food is wasted worldwide. Whether it’s ordering too much product, inefficient delivery routes, or just creating dishes too large to eat these are all factors to consider. Luckily one of the leading functions of AI is improving efficiency through the removal of waste.
This is where the AI-enabled food waste tracker Winow can make a huge difference. The basic design of the Winow is to use cameras to track what gets thrown out in restaurants. It tracks food waste due to over portioning, food going bad due to over ordering, and even helps pinpoint what parts of meal customers don’t eat. It was stated by Winow’s creators that in the span of 12 months customers can typically see a return on investments. Going on to state that, “with less waste customers typically save 3-8% on food cost.”
For most companies, a reduction in that size can mean the quality of life improvements, but for many startups, that type of savings can mean making a profit that much sooner. However, it’s not just food waste from restaurants that are being aided by AI, food manufacturing plants are getting some aid too.
When it comes to factories dealing with raw produce the sorting process is one of its most time consuming and important factors. The idea of sorting out produce depending on size, shape, and colour can be a hassle. Add to that the idea that some vegetable sizes make them more suitable for different products and suddenly sorting has gotten a lot more complex.
Luckily a Norway based company called Tomra Sorting Food has created an AI to help employees identify sorting problems faster than ever before. The idea behind the program according to Tomra’s developers is to use sensors to, “ensure high-speed processing and provide information on material, shape, size, geometry, colour, defect and damage characteristics, and the location of objects.” The program learns what is and isn’t acceptable and points out any items that need to be removed. This in turn makes the job of those sorting produce that much easier. For any business start up or not making life quicker and easier for employees is a game changer.
One integral issue of the food industry will always be cleanliness. Not just of kitchens, but manufacturing plants, and farm equipment. Unfortunately for many of the larger industry standard machines it’s an all or nothing approach. Currently the easiest way to clean large food manufacturing machines is to treat them all the same regardless of how dirty they really are. Equipment that only needs a quick sanitizing compared to one that needs a deep scrub are treated the same way. The same amount of water, cleaning supplies and time goes into each wash oftentimes wasting resources and time that can be spent somewhere else.
A UK based tech company Martec of Whitwell Ltd and its SOCIP has plans to stop this. The Self-Optimising Clean-in Place or SOCIP is designed by its creators to, “use artificially intelligent, multi-sensor technology to monitor and assess the amount of food and microbial debris that is present inside food manufacturing equipment during the detergent phase of the cleaning process.” Basically the program will determine the cleanliness of each piece of equipment and decide the best way to go about cleaning. It’s done more or less the way a human would clean something but much faster, precise, and on a larger scale.
Unfortunately, the SOCIP has not been made available to the public yet. Martec gone on to report that in time it will be ready but is still looking to make improvements. SOCIP is a prime example of what types of problems AI can fix.
One of the most common issues with raising efficiency due to AI is job loss. This is an unfortunate factor and should be taken seriously. However, the most recent breakthroughs in the food industry are focused on waste reduction and making things easier on employees rather than outright replacing them. There may be a time where AI assisted machines will replace food industry workers and chefs but it certainly won’t be for a while.
The other less glaring issue behind AI development is cost. For larger companies or franchises adding in a Winow waste tracker, or larger farm reserving an SOCIP for the future cleaning procedures isn’t as much of a risk as it would be for a startup. When it comes to AI in food the initial investment usually pays off, but taking the risk in economic uncertain times may not be possible for everyone.
What is certain is that advancements in AI won’t stop, the efficiency they provide is making a difference across too many fields to be brushed away. Companies both new and old now have to decide what is best for their company and the people who work there.