  In most retail stores, the number of days since initial processing is used as
a proxy for estimating the freshness of perishable foods or freshness is
assessed manually by an employee. While the former method can lead to wastage,
as some fresh foods might get disposed after a fixed number of days, the latter
can be time-consuming, expensive and impractical at scale. This project aims to
propose a Machine Learning (ML) based approach that evaluates freshness of food
based on live data. For the current scope, it only considers meat as a the
subject of analysis and attempts to classify pieces of meat as fresh,
half-fresh or spoiled. Finally the model achieved an accuracy of above 90% and
relatively high performance in terms of the cost of misclassification. It is
expected that the technology will contribute to the optimization of the
client's business operation, reducing the risk of selling defective or rotten
products that can entail serious monetary, non-monetary and health-based
consequences while also achieving higher corporate value as a sustainable
company by reducing food wastage through timely sales and disposal.
