WasteWise Food Loss and Waste Predictor wins Rep. Thomas P. Tiffany’s 2024 Congressional App Challenge in Wisconsin’s Seventh District

When asked what inspired the creation of WasteWise Food Loss and Waste Predictor, the student said, “I’ve always wanted to leave the Earth better than how I found it. Unfortunately, due to poor business practices and waste management (amongst other things), climate change is accelerating our world towards the worst. If we don’t do anything about it, the Earth we know and love today might be magnitudes less beautiful and enjoyable decades from now. One large contributor to climate change that I think we should focus on is the issue of food loss and waste. Globally, food loss and waste accounts for a third of all food produced, and is one of the largest contributors to carbon emissions. I thought that if we could reduce even a small portion of this FLW, it could have massive benefits for reducing the carbon emissions that contribute to climate change. Furthermore, reducing the food lost during the supply chain and in the food wasted by consumers could have the impact of reducing landfills and pollution. If we can produce and distribute food more efficiently, perhaps even world hunger rates can be reduced. 

Once I learned about the far reaching effects of food loss and waste, I decided to leverage AI to aid in the predictions. I hope that by developing this app, non profits, businesses concerned with certain stages of the food supply chain, and potentially even policy makers can target high FLW commodities, countries, or supply chain stages with specialized interventions in order to reduce it. In addition to reform in that sphere, this app also serves as a proof of concept for the use of decision trees to predict food loss and waste in large datasets. Perhaps other researchers can expand upon my work to create more accurate, more comprehensive, localized models that can be used in an industrial setting.”