Reducing Food Waste Through Food Item Detection Using AI

Background

Food waste is a global issue, with one-third of all food produced being wasted, contributing to 10% of global greenhouse gas emissions. Reducing food waste is one of the most effective actions to combat climate change. By enhancing food waste management, businesses can significantly decrease their environmental impact.

Objective

The project’s goal was to develop an AI-powered computer vision system that accurately identifies food items and predicts their weight. This model aims to assist hotels and food businesses in reducing food waste by providing insights into their waste patterns, enabling them to optimize preparation, production, and purchasing practices.

Approach

A global team of 50 collaborators from Omdena worked on creating a comprehensive dataset of food items. This data was then used to train a computer vision model capable of identifying various food items and predicting their weight. The project used cutting-edge machine learning techniques and AI tools to build the algorithm, which was designed to integrate with Positive Carbon’s automated food waste monitoring system.

Results and Impact

The AI model developed has the potential to significantly reduce food waste in businesses by giving kitchens full visibility of their waste. By recognizing and predicting the weight of food items, kitchens can adjust their practices, leading to more efficient food preparation and reduced waste. The collaboration with Positive Carbon will help food businesses, particularly in the hospitality sector, reduce their environmental footprint.

Future Implications

This project has strong implications for future food waste reduction policies and technological advancements. As the model continues to evolve, it could be applied more broadly across various industries to optimize food waste management. Additionally, the insights gained from this work could inform future research into AI-driven solutions for sustainability and carbon footprint reduction.