How Will AI Shape Our Food Systems of the Future?

Artificial intelligence (AI) has become an important part of our daily lives. Beyond chatbots and self-driving cars, AI is making significant strides in the agrifood industry. Find out how AI could reshape the entire food chain.

Artificial intelligence (AI) is a field of computer science focused on creating algorithms to perform tasks that typically need human intelligence. By handling various types of data, such as images, texts, and sounds, AI enables advanced data analysis, which can provide valuable insights to address a diverse range of challenges. Challenges like the ones our food systems face today.

From the effects of climate change to food waste and a growing food demand despite diminishing available space to produce it, there are numerous issues facing the agrifood industry. In 2020, the European Union (EU) attempted to tackle some of these issues by approving several action plans to become climate-neutral by 2050. Central to this new climate centred approach to policy is the Farm to Fork strategy that focuses on transforming our food systems to become more sustainable and adaptable.

Recently, AI has put forward a new means to solving issues throughout the entire food chain, offering a number of solutions in food production, food safety, food quality, and supply chain management. These solutions could also play a key role in shaping this new policy strategy. But while AI could offer new solutions, it also comes with ethical and practical challenges if not implemented responsibly.

Smarter farming

Conventional farming methods, like monocultures on large-scale land plots, are no longer sufficient to meet the growing food demand, which is expected to increase between 59% and 98% by 2050.¹ This increase in production will shape the agrifood industry in ways we have not seen before. To increase crop production, farmers worldwide will need to tackle challenges such as crop diseases, inadequate storage management, pesticide control, weed management, and water scarcity.

The introduction of new technologies can bring about significant advancements on all of these fronts. Even today, farming practices can be optimised by robotics, drones, and self-driving vehicles. The data gathered by these AI-powered machines could inform farmers on issues such as crop status, weather forecasting, and environmental changes that could impact their crops. Effectively, these new innovations can support farmers to work ‘smarter’ rather than harder, potentially leading to higher yields and more efficient production. Although these technologies seem costly at first glance, AI insights can also dramatically improve the efficiency of crops and increase production. One example of the benefits of AI-powered robotics can be seen in strawberry picking by a robot that can harvest the same amount of fruit as 30 human workers over the same time period. Another application is AI-powered weeding technology that allows farmers to reduce herbicide use by 90% compared to traditional weed treatments while saving up to 30% on cost.²

Learn about why we use pesticides here

AI can also be implemented on less conventional farming operations, such as regenerative farms. Recent research has claimed that regenerative agriculture should be combined with modern agricultural technology and data science, such as AI, to maximise its effectiveness. Their interplay might help to gain more insight into the soil status as well as optimise land productivity.³

An AI tea picking robot at a tea plantation in Hangzhou, China, 2023. It is understood that the AI tea picking robot can accurately identify tea buds through image recognition. (Photo by Costfoto/NurPhoto via Getty Images)

AI in food processing

Food processing typically involves a lot of operational practices. In the EU, more than 4 million people work in food manufacturing.⁴ These workers often perform repetitive production steps, where mistakes may occur. In addition, the food sector often faces challenges such as staff shortages and adverse working conditions, including slippery ground, noise, or non-ergonomic positions. Autonomous food manufacturing may be the key to addressing the issues of rising food demand, staff shortages, and inefficient production rates. Furthermore, autonomous robots might offer more effective management of sanitary procedures than human workers.⁵

Robot installations, which perform automatic and repetitive food process steps, increase worldwide almost 20% year-over-year.⁵ Despite this high number, food and beverage automation is still quite some way behind other industries, such as automotive manufacturing. There are also still a few food-specific hurdles to overcome. Natural products exhibit a range of irregular shapes and sizes that make it challenging to program robots to interact with them. To effectively address the biological variability, robots in food production can be AI-controlled, as AI is especially able to handle this variability. Not only may the AI robots automate process steps, but they will also reduce the likelihood of human error and increase safety standards. This is in contrast to human workers who might introduce pathogens into the food if they are sick or haven’t thoroughly washed their hands.

Some common and emerging applications of AI-controlled robotics in food production include picking, placing, cutting, and slicing food products like fruit, vegetables, fish, and meat. However, automating these tasks is especially challenging for cooked and prepared products, as they might be non-rigid, fragile, and easily damaged. Moreover, they could be randomly distributed or stored within containers instead of aligning on a conveyor belt. This unpredictable positioning presents challenges for automated processes, such as gripping a single item from a full box or precisely cutting the food. Another barrier is the upfront implementation cost, with specialised robots often carrying a price tag of up to €150,000 - potentially limiting smaller businesses to benefit from this technology until pricing becomes more competitive.⁶

Icelandic company Marel presents a robot for meat processing at a meat industry trade fair in Frankfurt, 2022. (Photo by Arne Dedert/picture alliance via Getty Images)

Better food safety & higher-quality products

Recently, AI has been increasingly used to enhance food safety measures. Food safety involves ensuring that food is handled, prepared, and stored in a manner that prevents contamination and reduces the risk of foodborne illnesses. This includes implementing practices and regulations that protect against harmful substances, such as bacteria, viruses, or foreign objects.

By predicting the risk of bacterial infections, AI can help prevent outbreaks and improve the overall safety of food products. For example, AI can accurately forecast the shelf life of perishable goods like eggs, milk, and meat, aiding in inventory management and waste reduction. While traditional shelf life predictions often rely on kinetic models, AI can combine multiple factors, such as pH, fluctuations in storage temperature, and visual changes, to create more accurate forecasts of shelf life.⁷

AI-based systems also play a significant role in assessing and enhancing food quality - including taste, texture, appearance, nutritional content, and freshness. For instance, AI-based systems can be used in the sorting of fruits, vegetables, cereals, and nuts, using image analysis to categorise and ensure consistent quality. In the brewing industry, AI technology could even help you to produce recipes at the same level as a master brewer.⁸ There are also several rising technologies, such as the e-nose and e-tongue that, combined with AI analytics, can "smell" and “taste” coffee and tea to estimate their quality based on specific flavour profiles.⁹

Smoother supply chain management

Transportation of perishable food across the supply chain faces numerous challenges around storage costs, inventory management, route optimisation, and traceability. These challenges affect both global and small local food providers, who want to find the most efficient ways to do business for the sake of profitability. By implementing AI-driven solutions, these businesses can benefit from improved sales forecasts, order and stock control, and production monitoring.

One practical example of data-driven technology in the food supply chain is the management of freshness in perishable food. By monitoring the condition of the product via sensors, AI analytics can accurately predict its freshness and align it with the expected time of arrival at its destination. This intelligent routing ultimately leads to improved inventory management and reduced food waste by 50% or more.¹⁰ Additionally, AI facilitates the analysis of historical and real-time data to identify consumer patterns and forecast resource shortages. Proactive transportation planning will thus reduce waste and shipping costs, enabling businesses to provide a more consistent service for their customers.

AI can also help us optimise our efforts to reduce food waste at home. Statistics across 54 countries demonstrate that households discard around 11% of all food.¹¹ An AI-powered app, such as Kitche, can suggest recipes based on the ingredients available in the user’s fridge to help minimise this food waste.¹² Other food apps also often include diet planners, grocery lists, and calorie and nutrition monitoring, promoting healthier and more sustainable food choices.

Ethics and challenges with AI

AI is a term that is frequently used, but often with some confusion about what it means and what its impact is on daily life. While popular culture often portrays AI as chatbots or self-driving cars, it can more realistically mean automatic food processing or more advanced shelf life monitoring to reduce food loss. Advanced AI applications offer numerous benefits, but they also raise unique concerns and issues.¹³

One of the most relevant benefits of using AI systems in the agrifood industry is sustainability. However, one counterargument is that replacing humans with robots may lead to the use of more dangerous pesticides or soil compaction due to the heavy machinery.¹³ Companies therefore currently focus on manufacturing ultra-light farming machines to account for this change.¹⁴ AI may also take jobs away from people on the farm level but will create new opportunities for high-tech developers. At the same time, robots can undertake tasks for which finding available workers is often challenging. Another important concern is building trust in agricultural AI, which can be challenging when autonomous machines collect private and sensitive data during farming operations, such as assessing the status of crops or livestock. Data ownership and privacy is a general concern when using AI in businesses. In addition, it is crucial to ensure transparency and accountability when AI-driven machines make decisions instead of human workers, as the ultimate responsibility still lies with the food provider.

From new and faster AI-driven technologies that enhance productivity to accurate forecasting that can help both farmers and policymakers better understand the impacts of changing climates on our growing systems, these new innovations will contribute to reshaping the entire agrifood industry. In the EU, they will also play a role in shaping the Farm to Fork Strategy to make food systems more fair, healthy, and environmentally friendly.¹⁵ Yet, it remains crucial that the innovations align with ethical and social considerations to ensure they are responsibly implemented. If we can do this, AI can create a more sustainable agrifood industry that works more harmoniously with our planet to ensure it continues to nourish our future generations.