Project Abstract

Faced with the critical issue of approximately one-third of all food produced globally being wasted, with significant economic, environmental, and social costs, there is a pressing need to address food waste now more than ever. This project aims to tackle this issue by using advanced imaging technology to better predict when food will go bad, which could greatly reduce the amount of food thrown away.This research introduces a new system that uses hyperspectral imaging to assess the quality and safety of perishable items like fruits, vegetables, and meats. Its uniqueness lies in using this technology to collect detailed information from a wide range of light wavelengths and applying modern analysis techniques to accurately predict food spoilage.In terms of methods, the study involves setting up an imaging system that captures detailed color spectrum data from food at different stages of going bad. This data is then cleaned up and simplified using a technique called Principal Component Analysis (PCA), which helps in efficiently using machine learning models to predict when food will expire.While the actual results are yet to be seen, the project aims to identify clear patterns that link the captured spectral data to signs of food spoilage, potentially leading to a powerful tool for predicting food quality and spoilage. Such a tool could dramatically cut down on food wastage.The ultimate goal of this project is to revolutionize the way food expiry is predicted, greatly reducing food waste and its negative impacts. If this technology can be integrated successfully into everyday food industry practices, it could offer a scalable, effective solution that makes food systems more sustainable and reduces global food waste.

Keywords: Machine Learning, Hyperspectral Imaging Technology, Food Expiry Prediction

 Conference Details

Session: Presentation Stream 2 at Presentation Slot 6

Location: GH043 at Tuesday 7th 13:30 – 17:00

Markers: Mukesh Tiwary, Muneeb Ahmad

Course: MSc Data Science, Masters PG

Future Plans: I have a job lined-up