The food industry, much like the fashion industry we discussed earlier, is not typically the first thing that springs to mind when discussing AI. Nevertheless, AI’s application in the food industry has significantly revolutionized how we produce, distribute, and consume food. From farming practices and visual recognition tools to the end-consumer experience, non-tech brands are actively employing AI to streamline operational practices. Here we delve deeper into this transformative impact of AI in food industry:
Optimization of Growing Conditions
AI is revolutionizing agriculture, empowering farmers to optimize growing conditions and boost yields. Using AI-powered drones and monitoring systems, farmers can gather crucial data on factors like temperature, UV lights, and salinity, enabling more informed decision-making.
As we recently delved into AI’s impact on poverty reduction, Digital Green and OKO Finance are standout examples of helping smallholder farmers. Digital Green’s responsive farmer advisory service tackles climate change and water security, while OKO Finance delivers essential weather alerts and farming advice. Ignitia also stands out with its deep-tech weather forecasting, producing the world’s first highly accurate tropical forecast model at an impressive 84% accuracy, outperforming global models at 39%, transforming how farmers navigate unpredictable weather patterns.
Crop Management
To ensure consistent crop quality, AI can analyze data to optimize planting times and predict yields. Automated farm equipment, driven by AI, handles tasks such as planting, harvesting, and weeding with precision, reducing the need for extensive human intervention. Take, for example, Semios — an all-in-one crop management platform. By converting elements like weather, pest pressure, water management, and plant stress into actionable insights, Semios empowers cultivators to make informed decisions at the right time.
Pests Detection
AI excels in detecting plant diseases and pests, protecting overall food quality. A vivid example here is CottonAce, which utilizes object detection to help smallholder cotton farmers in protecting crops from pests. Its AI-powered early warning system guides farmers on optimal preventive measures, minimizing crop losses due to pests.
Reducing Environmental Impact
AI also plays a crucial role in enhancing resource efficiency for farmers. Identifying energy-saving opportunities, recommending efficient practices, and optimizing energy distribution contribute to sustainable farming. Leveraging AI algorithms and satellite data, Boomitra (formerly known as ConserWater) predicts soil moisture, nitrogen, phosphorus, and soil carbon content without sensors, showcasing AI’s potential to foster eco-friendly and resource-efficient agricultural practices.
Automation of Sorting Processes
AI eliminates the need for labor-intensive manual sorting by utilizing systems that analyze photos and sensor data to precisely identify flaws, sizes, and quality of food items. Japanese farmers embraced these technologies early on. Back in 2017 Makoto Koike, a former engineer who shifted from an urban lifestyle to manage his family’s cucumber farm, developed a system using Google’s TensorFlow to capture images of each cucumber from various angles. Inspired by AlphaGo’s success, Koike adopted a DIY AI approach, achieving an accuracy rate of seventy percent.
Such technological advancements no longer come as a surprise. At Everypixel, we’ve also ventured into developing similar solutions, particularly focusing on recognizing defective packaging for beverage manufacturers. And the field continues to progress with AI-driven robotics contributing to vegetable sorting based on color, effectively minimizing food waste. A notable player here is TOMRA Food, a Norwegian giant specializing in sensor-based food sorting and steam peeling machines, offering integrated post-harvest solutions that empower food businesses.
Packaging Optimization
By organizing food items for storage and shipment, AI ensures consistent packaging quality, minimizes errors, and enhances process speed. The algorithms embedded in AI models determine the optimal choice of packaging materials, whether it’s plastic, flexible packaging, corrugated cardboard, or padded mailers, based on specific items or delivery requirements. This strategic selection not only improves packaging efficiency but also reduces package weight, enabling more items to be transported in each delivery. This, in turn, lessens the need for eventual recycling and contributes to a lower carbon footprint per item, creating a triple-bottom-line success story. Amazon, for instance, employs an AI model that learns from real-world customer complaints to enhance product protection during shipping. Amazon’s model, applied to hundreds of thousands of Amazon packages, has successfully reduced waste and shipment damage by 24%, while also cutting shipping costs by 5%.
Sustainable Food Development
AI has the potential to introduce nutritious plant-based alternatives that closely match the taste, feel, and nutritional composition of traditional animal-based products. Take Fazenda Futuro, which, with the help of AI, has already successfully created a satisfying blend of soy, pea protein, and chickpeas that authentically replicates the taste and texture of beef. Similarly, Beyond Meat, a leading plant-based food company utilizes AI to consistently enhance the flavor and texture of its meat alternatives. By analyzing sensory data, user feedback, and ingredient profiles, AI plays a key role in continually improving the overall taste and consistency of plant-based products.
Supply Chain Management
AI improves supply chain management by leveraging historical and real-time data analysis to enhance various aspects of the process including inventory levels optimization and logistics planning refining. Additionally, AI facilitates accurate forecasts for inventory management and pricing, enabling businesses to stay ahead of current trends and plan shipments proactively. This proactive approach not only reduces waste but also lowers shipping costs. Coca-Cola exemplifies this integration, employing AI and ML algorithms to streamline distribution and supply chain operations, precisely forecasting demand and optimizing inventory to mitigate stockouts and surplus.
Marketing Enhancement
In the realm of marketing strategies, AI has become an essential tool, and Coca-Cola is leading the way here. Going beyond supply chain management, Coca-Cola is pioneering the integration of AI into marketing strategies. While we’ve previously highlighted Coca-Cola’s use of AI in creative advertisements, the company has taken a step further with the introduction of Coca-Cola Y3000. This first ever AI-created flavor has taken the world by storm, amplifying the company’s marketing impact. Coca-Cola Y3000 not only showcases AI’s influence in marketing and flavor developing but also the technology’s capacity to craft packaging design, drawing inspiration from human insights on futuristic tastes.
Product Personalization
AI-driven tools help businesses understand individual preferences, dietary restrictions, and nutritional requirements. This approach allows manufacturers to craft personalized food products tailored to meet specific consumer needs. Starbucks, a pioneer in leveraging AI for personalization, has integrated this technology into its mobile app to deliver customized recommendations. By analyzing users’ past orders, preferences, and real-time location information, the app generates personalized suggestions for both food and drink options. Behind the scenes, Starbucks utilizes AI algorithms not only to enhance the menu but also to identify optimal store locations.
Personalized Nutrition
AI algorithms are at the forefront of revolutionizing personalized nutrition, tailoring nutrition regimens by delving into dietary limits, preferences, and individual health data. These programs empower users to make informed decisions about their dietary needs, taking into account factors such as age, gender, exercise levels, and specific dietary requirements.
Nutrino and Medtronic exemplify this approach by utilizing CareKit on the iPhone to provide FoodPrint reports for individuals with Type 2 diabetes. Recognizing the uniqueness of each body’s metabolism, FoodPrint delivers sophisticated scientific analysis, offering tailored advice on food choices based on individual body chemistry. Another app that is at the forefront of personalized nutrition is January AI. It combines a continuous glucose monitor, an activity tracker like a smartwatch or ring, and an AI-powered app. January AI can predict the blood glucose impact of over 32 million foods for each user, offering healthier alternatives before consumption or advising on optimal times for intermittent fasting and exercise to minimize blood glucose spikes.
In conclusion, the collaboration of non-tech brands with AI in the food industry goes beyond mere technological integration; it represents a transformative process that enhances every facet through precision and innovation. AI in the food industry has become an integral force, driving efficiency and sustainability.