Superlinear’s AI for Sustainability series

Our Machine Learning Engineer, Mattia Molon, gave a talk on reducing food waste with the help of AI. He discussed the impact of food waste on society, the economy, and the environment and presented how AI can help address this problem by reducing food waste at various stages of the food lifecycle.

Watch his presentation below, or continue reading instead ⬇️

Today’s Food Waste Reality

Approximately 1.3 billion tons of food is wasted annually, accounting for nearly 30% of all the food produced worldwide. For context, this means that one is wasted for every bagel you buy. The environmental impact is significant, with rotting food producing almost 10% of global CO2 emissions – equivalent to one of every four cars on the road. Economically, the annual cost of food waste amounts to around $1 trillion. Socially, it exacerbates food scarcity and distribution issues, as one-third of all food produced goes to waste.

The Food Lifecycle

To understand how AI can help reduce food waste, it's essential to examine the food lifecycle, which consists of five main steps: production, processing, distribution, retailing, and consumption:

Here, AI can act as a "copilot" in each stage, using data analysis and generating insights for experts in their respective fields to make informed decisions and tackle specific problems. Let’s explore this in more detail.

AI as a Copilot to reduce food waste

Food production

AI can assist farmers in monitoring soil health, managing resources, and tackling diseases and pests. Smart agriculture technologies, such as satellite data, drones, and field sensors, can provide farmers with dashboards and insights to optimize planting, harvesting, and resource allocation, as well as detect pests and diseases early. For example, AI-driven dashboards can inform farmers which corners of their fields need more water or alert them to potential crop health issues.

Food processing

AI can automatically detect defects in raw materials and sort them accordingly, ensuring high-quality products and minimizing waste. For instance, AI can help differentiate between ripe and unripe tomatoes, ensuring that only suitable ones are used for making ketchup. This technology improves the efficiency of food processing by increasing the quality of the final product and reducing the risk of using inadequate materials that could lead to spoilage or unsellable goods.

Food distribution and retailing

AI can optimize food distribution by analyzing real-time traffic data and store locations, ensuring food remains fresh during transport. Additionally, dynamic pricing models can help retailers analyze customer behavior, expiration dates, and inventory levels, enabling them to adjust prices accordingly to increase revenue and reduce waste. This approach allows stores to sell more food before it spoils and helps prevent overstocking.

Food consumption

AI can help individuals and restaurants manage food consumption by reducing overbuying and tracking inventory. Smart fridges and AI assistants can suggest recipes based on available ingredients, minimizing forgotten or wasted food. For example, ChatGPT can generate a list of recipes using ingredients in your fridge, helping reduce food waste and make the most of your purchases.

In conclusion

The food system's complexity presents numerous challenges that significantly impact our lives. AI has the potential to dramatically reduce food waste by providing solutions at each stage of the food lifecycle. By understanding the role of AI in food waste reduction, we can work together to create a better future.

Are you ready to welcome AI as your copilot in making more informed decisions that also benefit our planet? Don't hesitate to reach out to Superlinear!