Approach to minimize waste and maximize profit in grocery retail stores
Recently, I read that 1.3 billion tons of fruit is wasted yearly. In order to address this massive issue, I created a machine learning-based system that processes sales data and ripeness sensor data to suggest effective selling prices in order to minimize fruit wastage. The system I designed and built contains three main parts: a light sensor, a Price Calculator, and a web app. I built a circuit that works by sending out light by the LED to the fruit. A photoresistor detects the light that bounces back to the photoresistor whose resistance changes in response. Hence, this circuit allows the characterization of the color of the fruit which can be used to describe how ripe it is. Information from the sensor/circuit is taken to the server. The price calculator uses data (past sales, prices, deliveries) to calculate the most effective price at which the appropriate fruit should be sold at. I also built a web app, which is intended to be the primary interface between the price calculator and the storekeeper. The storekeeper uses the website to submits to the database using the website. The web app also presents the most up-to-date pricing suggestion, as calculated by the server, as well as the most recent ripeness data, which the Grocery Store reviews. In summary, I built an algorithm that takes past data and optimizes the best way to increase money earned.