Artificial intelligence can save the Food Industry.
Examples of the use of AI in the food industry and how it has changed their business and accelerated their growth
Rarely has a crisis accelerated the adoption of a technology in the manner that is occurring today with AI in the food industry. The business of selling food to consumers is being disrupted to a degree not since the last pandemic, over 100 years ago. It is increasingly apparent that our food system was ill prepared ('anti-fragile') for this Covid-19 induced crisis. With restaurants shuttered, a dramatic return to home cooking, a re-ignition in the meal-kit movement, shut-downs of meat factories and office canteens, and explosion of home delivery it may seem as though the world will never be the same again. This too, of course, will pass, but instead of being a 6 month blip, the continued deconstruction and automation of the food supply process makes it clear that we are entering a new norm, and that returning to the world as we knew it won’t be possible. 8 digital technologies are transforming the food business (Robots, AR, VR, 3 Printers, Sensors, Machine Vision, Drones, Blockchain, IoT), but they all have one thing in common, artificial intelligence is the secret code or sauce behind them all.
Artificial intelligence (AI) refers to the collection of data from sensors and its conversion to comprehensible information. AI machines can mimic human cognitive functions such as learning and problem solving, and interpret information more efficiently than humans, reducing their need to be involved. As it is being developed, it is clear that AI can also be self-learning, and progress beyond human abilities. The use of AI to advance food production is accelerating as the world progresses post-Covid and expectations of speed, efficiency as well as sustainability are ever-increasing alongside the rapidly growing population.
Here are six examples of actors in the
1) Processing
The processing of food is a labor intensive business, but one where AI can maximize output and reduce waste, by replacing people on the line whose only jobs are to distinguish identify items unsuitable for processing. Decision making of this type at speed requires the senses of sight, smell, and their adaptability to adapt to changing circumstances. AI brings even more to the table through augmented vision, analyzing data streams either unavailable through human senses, or where the quantities of data are overwhelming. Organizations such as TOMRA have already begun to incorporate AI technology into their production processes, including innovative sensor based sorting machines, detecting and removing any types of foreign materials from their lines of produce, reacting to changes in moisture levels, colors, smells, and tastes of foods. They claim the recovery of “5-10 per cent of produce through higher yields and better utilization” equivalent to “25,000 trucks of potatoes per year”. Along the same lines is Aromyx, who use AI to quantify taste and smell to increase efficiency in production in order to create “a new quantitative and visual standard to represent... senses of taste and smell as actionable data”. Another similar technology are electric noses, which as the name suggests, can be used as replacements to human noses to distinguish various foods’ odors and aromas using AI sensors. Another example is how Japan's Kewpie use Google's Tensorflow AI for the detection of defective ingredients during processing. It was originally used for the sorting of foods, and gradually developed into an anomaly detector, which could then be used for unsupervised learning, saving both a large amount of time and money. While focused on diced potatoes as of yet, the company plans to “expand to eggs, grains, and so many others”. Dutch company Qcify uses machine vision systems to classify almonds, pistachios and more. Gamaya has targeted agrochemicals to lower environmental impacts and costs during food production, through the use of AI to create maps which analyze various crops’ phenological and physiological traits. Eventually this is used to “provide targeted and tailored recommendations for the optimum management and treatment of [their] land and crops”. A myriad of other agri-tech startups are focused on using AI to detect early warning signs of poor health in crops which may have otherwise been overlooked, which can further reduce the waste in food production, on top of increased transparency. For example, CAMP3, a small American company, uses AI to spot plant spots and diseases at an early stage, and reduce the chance of contamination further along the production line. These field applications have been covered in the Crop Farmers digital dilemma.
2) Food Safety
Reducing the presence of pathogens and detection toxins in food production is a key avenue for AI. The Luminous Group, a Newcastle-based software firm, is developing AI to help prevent outbreaks of pathogens in food manufacturing plants, limiting consumer illness or recalls. Additionally, AI offers the opportunity to increase traceability and consequently, consumer confidence. For example, a KanKan subsidiary consisting of AI-enabled cameras in Shanghai’s municipal health agency checks that workers are complying with the safety regulations[1]. This algorithm-based machine learning technology includes facial and object recognition, and “sets the foundation (...) to potentially triple [their] business with the city of Shanghai”[2]. More recently the company added improved facial recognition abilities to account for the mandatory use of a mask, and new body temperature detection, in line with effects of Covid-, as detecting increased body temperatures could help in the early detection of a Covid case.[3] This everchanging project shows an ability to constantly grow and develop, a flexibility required today in the world of technology. Dragontail Systems Limited is another software company which has taken up many of the above-mentioned new measures during the fight with Covid. Additionally, Japanese company Fujitsu has developed an AI-based model which is used to monitor hand washing in food kitchens following strict regulations set by the Japanese health ministry. This technology will reduce the need for visual checks (critical during Covid) where food safety must be increased. Next generation sequencing (NGS) is also another attempt made by food safety associations to increase the accuracy and speed in which any threats to food safety are identified and dealt with in a production chain. Thirdly, AI can be used for Cleaning In Place (CIP) projects, which aim to use AI to clean production systems for cheaper, and using more environmentally friendly methods. In Germany, a project by Industrial Community Research strives to "develop a self-learning automation system for resource-efficient cleaning processes”. This makes up a cleaning process without a need for the disassembly of equipment. This could cut labour costs and time spent on it, as well as increasing the safety of food production in the plant in question by removing the opportunity for human errors. The University of Nottingham has also been working to construct a Self-Optimising Clean-in Place system, which uses AI “to monitor the amount of food and microbial debris in the equipment”.
3) Supply Chain efficiencies
UberEATS, which now sits at “a $6 billion bookings run rate, growing over 200 percent”, riding the wave of popularity of food delivery, is now incorporating AI to make “recommendations for restaurants and menu items, optimise deliveries”, as well as looking into the use of drones. They use Michelangelo, a machine learning platform, for various different tasks. For example, this can predict meal estimated time of delivery (ETD) to reduce waste and improve efficiency throughout its delivery process. While this application is post-food production once food has been produced there are many more ways to implement this up and down the chain. Covid-19 has accelerated the applications of technology to replace human labor and while smart device Food apps, drone and robot delivery, and driverless vehicles all provide new ways to get information and food to the consumer, all of them depend upon AI. Dallas-based technology company Symphony RetailAI uses AI in the food supply chain to “boost productivity, and greatly improve the accuracy of information for better decisions”[4]. Innovative uses of AI are crucial in moving towards reducing the quantity of food wasted in order to feed the growing world population as efficiently as possible, as well as falling in line with increasingly specific consumer demands and expectations. Shelf Engine offers AI to remove human error from the purchasing function, and make more informed decisions about order sizes and types, in hundreds of US stores has saved thousands of dollars in food waste. Wasteless have been even more aggressive, allowing retailers to use dynamic pricing to discount produce before it goes past its sell by date.
4) Predicting Consumer trends and patterns.
AI allows companies to stay competitive within the market, by adapting based on different popular waves of various trends, making predictions about the market. Michelangelo is again one of the most innovative examples of predicting trends, it’s detailed method being explained in greater detail on the hotlink. Tastewise is US startup using AI-based intelligence for food retail. Their data collected includes “up-to-the-minute industry insights, predictions, and emerging food trends based on analysis of billions of social media posts and photos, US restaurant menus, reviews, and recipes”[5]. McCormick & Company and IBM is funding research and development of “flavor and food product development” through the use of AI. As previously mentioned, Aromyx enables “clear, reproducible digital representations of smell and taste”. Their website claims to “accelerate new product development”, speeding the process of adapting to new trends or predicting future ones. Carlsberg Research Laboratory has also jumped on the bandwagon, announcing its participation in ‘The Beer Fingerprinting Project’, including a large investment in a “research study with the purpose of measuring and sensing flavours and aromas in beer”, to further cater to their consumers’ demands, satisfy them and increase sales.
5) Restaurants
The future of restaurants is in peril following this year’s Covid-19 outbreak. The explosion of online-based food delivery systems has decreased the focus on the physical experience of restaurants. For example, chat boxes can allow communication with your favorite restaurant without leaving the comfort of your home, all powered by AI. Voice search is another tool useful to allow people to place restaurant orders simply by talking to their screen. AI analytical solutions such as these lead to better consumer experiences and likely to increase sales for restaurants due to the ease with which food orders can be placed. With the previously mentioned example of UberEats, AI is used to increase efficiency and lower costs during the process of food delivery, encouraging restaurants to partner with these companies to ensure the delivery of their food. Automated customer service and segmentation will likely lead to increased accuracy in “creating reports, placing orders, dispatching crews, and formulating new tasks[6]” in a restaurant. For reasons of added safety and precaution, restaurants are also likely to have to adapt new health-cautious AI technology systems in their kitchens, as mentioned in paragraph 3, including Dragontail and Fujitsu.
6) Designing better foods
Food is health has been a mantra for many for a long time, but now with a greater understanding of both human, plant and animal genomes it is becoming a reality. Changes in consumer preferences are creating opportunities for AI in food; an example is the growing demand for plant-based alternatives to meat protein, as the world moves towards precision nutrition. Challenges such as achieving consumer acceptable taste and texture qualities has led to creative AI applications. NotCo is a plant-based start-up company located in Chile which has been developing its own software company ‘Giuseppe’ a tool used to “predict how to make plant-based materials taste like animal-based products”[7]. This AI system uses machine learning and genomics to find similarities in plant and animal protein products. The goal of the company is to move towards healthier and more sustainable food sources at lower costs. Brightseed, a bioscience tech company, makes use of AI to further understand the health benefits of plant crops, in order to better human health with their food produce. This includes the acknowledgement of any helpful antioxidants which may be present in their foods, ensuring that these are carried throughout all the stages of production and delivery. For milk, meat and eggs remote observation through sensors and cameras can improve productivity but Cainthus has shown how it addresses animal welfare and concerns about the environment also. Gastrograph is a food technology app which collects data from its consumers and uses AI to convert it into new, comprehensible, and helpful data. It helps people figure out what they are tasting and helps provide tailored recommendations for what they should try next. This type of application is very appealing to consumers as it is easy to use and is very personalized Foodpairing is another platform which uses AI to create various combinations of foods which can potentially be used together to create meals, an app aimed to target chefs and bartenders. The application of artificial intelligence can be witnessed right across the food production spectrum. Leading taste and nutrition company Kerry have developed Trendspotter – a tool that automatically reads and processes millions of consumer-generated social media posts, extracting food items and cataloguing food-related combinations. Using an algorithm Trendspotter calculates and predicts the ingredients, flavors, foods and products most likely to match consumer’s evolving tastes. These insights are used in tandem with other industry-leading primary research capabilities to make targeted and on-trend recommendations.
With all positive aspects of AI some see it as a technology with the goal of taking over human jobs, and that creates controversy. The fear of the unknown is creating a pushback against the use of AI in many businesses. Additionally, AI requires skilled IT professionals, which are high in demand and difficult to recruit.[8] Clearly there are costs to retraining programs to adapt to the change in skills required. Finally, the cost of implementing and maintaining AI is very high, which may limit the opportunities for smaller or start-up business to compete with already established larger ones. This may lead to a reduction the numbers of smaller businesses, who would likely not be able to compete with the investments required and would be forced out of the market. Downsides such as these could possibly slow down the speed with which AI transforms food production, but given the absolute power unleashed by AI in a post pandemic food world, it is unlikely to be more than a speed bump on its eventual universal acceptance.
Thanks to Saoirse Boret for research and writing for this article.
[1] https://www.prnewswire.com/news-releases/remark-holdings-announces-seven-figure-artificial-intelligence-contract-for-facial-and-object-recognition-technology-to-ensure-food-safety-in-shanghai-china-300526557.html
[2] https://www.fastcasual.com/news/restaurant-safety-check-new-ai-platform-watches-reports-violators/
[3] https://www.prnewswire.com/news-releases/kankan-ai-upgrades-its-product-technology-to-provide-for-touch-free-temperature-measurement-for-mass-screening-of-high-traffic-areas-301015377.html
[4] https://progressivegrocer.com/grocers-embrace-ai-optimize-supply-chain
[5] https://www.foodanddrinktechnology.com/news/22216/predicting-food-trends-with-ai/
[6] https://spd.group/machine-learning/machine-learning-and-ai-in-food-industry/#Cleaning_equipment_that_does_not_need_disassembling_CIP
[7] https://golden.com/wiki/NotCo
[8] https://www.foodmanufacturing.com/home/article/13245042/artificial-intelligence-is-redefining-food-beverage-manufacturing
