AI-powered food tracking could help households reduce waste and save resources

Reducing food waste has become a critical priority for sustainability policies worldwide, but a large share of discarded food originates inside households rather than along supply chains. Limited awareness of stored ingredients, inconsistent meal planning, and busy daily routines often lead to avoidable waste that accumulates across millions of homes.

The study, titled “Generative AI for Sustainable Food Consumption: A Pilot Study on Reducing Household Waste,” published in the journal Sustainability, investigates how AI-powered digital tools can assist households in monitoring food inventories, managing expiration dates, and encouraging more sustainable consumption behavior. The research evaluates a generative AI–based mobile application designed to track household food supplies and provide automated reminders and recommendations aimed at preventing avoidable food disposal.

Generative AI enters the fight against household food waste

According to international estimates, a significant share of food waste occurs at the consumer level, particularly in urban households where busy lifestyles, inconsistent meal planning, and limited awareness of stored ingredients often lead to forgotten or expired food.

The researchers focus on the domestic phase of the food consumption cycle, often referred to as the pre-consumption stage. This stage occurs after food has been purchased and stored but before it is prepared and consumed. It is during this period that households frequently lose track of ingredients, overlook expiration dates, or purchase duplicate products because they are unaware of what is already stored in refrigerators or cupboards.

Traditional approaches to reducing household food waste often rely on awareness campaigns or manual food tracking applications. While these tools can encourage responsible consumption, they typically require users to manually enter data about purchased items and monitor expiration dates themselves. Over time, the effort required to maintain these records discourages consistent use, limiting their effectiveness.

To address this challenge, the study introduces an AI-driven mobile application called ZeroWasteAI, designed to automate food inventory management using generative artificial intelligence. The system incorporates Google’s Gemini 1.5 Flash model to recognize food items and manage ingredient lists while tracking their expected expiration timelines. By automating these tasks, the application aims to remove the cognitive burden associated with manual tracking and help households maintain a clearer understanding of their available food supplies.

The application performs several key functions intended to reduce waste. First, it identifies and organizes household food items into a digital inventory that reflects what users currently have in storage. Second, it monitors expiration timelines and sends reminders when ingredients should be used soon. Third, it analyzes the available ingredients and generates recommendations that encourage households to consume food before it becomes unusable.

Such automation is critical for maintaining long-term user engagement. Many previous digital food management solutions have struggled because they require continuous manual input from users. By contrast, generative AI systems can process information and generate insights with minimal user effort, making them more practical for everyday household use.

Testing AI-driven food management in real households

To evaluate the effectiveness of the ZeroWasteAI system, the researchers conducted a pilot study involving households in Lima, Peru, an urban environment where rapid population growth and changing consumption patterns have contributed to rising levels of household food waste.

The research design followed a four-week quasi-experimental approach involving 11 households. Participants used the application to manage their food inventory and received automated notifications about ingredient usage and expiration timelines. The researchers collected both quantitative and qualitative data to assess how the system influenced consumption behavior and food waste levels.

The results indicate that participants became more aware of their stored food items and were better able to prioritize the use of ingredients approaching expiration. Automated reminders encouraged households to incorporate these items into meal planning rather than allowing them to remain forgotten in storage.

The application also reduced unnecessary food purchases. When households have a clear record of available ingredients, they are less likely to buy duplicate items during grocery shopping. This improvement in inventory awareness helps prevent the accumulation of excess food that may later be discarded.

Participants also reported that the AI-generated suggestions for ingredient usage helped them discover practical ways to use items that might otherwise have gone unused. By analyzing the available ingredients in each household, the system could generate meal suggestions or preparation ideas that encouraged timely consumption.

Another significant benefit identified in the study was the reduction of cognitive load associated with food management. Busy urban lifestyles often leave little time for carefully monitoring food supplies, leading many households to rely on quick decisions or rough estimates when planning meals and shopping. Automating these processes through AI allows households to manage food resources more efficiently without requiring constant attention.

Sustainable consumption and the role of digital innovation

Food waste contributes significantly to global environmental challenges because discarded food represents not only lost nutrition but also wasted energy, water, and agricultural resources used in production and transportation.

When food decomposes in landfills, it generates methane, a greenhouse gas with a much higher warming potential than carbon dioxide. Reducing household food waste can therefore contribute directly to climate change mitigation by lowering emissions associated with waste management and unnecessary food production.

Digital technologies are increasingly viewed as tools capable of supporting sustainability transitions by influencing consumer behavior. Applications that provide real-time information and personalized recommendations can help individuals make more informed decisions about resource consumption in areas such as energy use, transportation, and food management.

Generative artificial intelligence introduces new possibilities within this space because it can analyze complex data patterns and generate tailored recommendations. In the context of household food management, AI can integrate information about available ingredients, expiration timelines, and consumption habits to create actionable guidance for users.

The study also highlights the importance of addressing behavioral barriers to sustainable consumption. Many people are aware that food waste is an environmental issue but struggle to translate this awareness into everyday practice. By automating reminders and providing convenient suggestions, AI-driven systems can bridge the gap between awareness and action.

Limitations and future research directions

While the pilot study demonstrates promising results, the researchers acknowledge several limitations that should be addressed in future research. The sample size was relatively small, involving only eleven households in a single city. Larger studies across diverse cultural and economic contexts would provide a more comprehensive understanding of how AI-driven food management systems perform in different environments.

Another limitation concerns the duration of the pilot program. A four-week testing period offers valuable initial insights but may not capture long-term behavioral changes or sustained engagement with the application. Future studies could extend the observation period to examine whether households continue using AI-based food management tools over several months or years.

The researchers also suggest that future systems could integrate with additional technologies to enhance functionality. For example, smart refrigerators, barcode scanning tools, or retail platforms could automatically update food inventories and provide even more accurate tracking of household supplies.

Integration with grocery delivery services could further expand the capabilities of AI-driven food management systems. By linking household inventory data with shopping platforms, AI could help consumers avoid purchasing items they already have while suggesting complementary ingredients needed for planned meals.