MilkSafe: A Hardware-Enabled Milk Quality Prediction using Machine Learning

This project presents a enhance milk quality prediction through the integration of hardware components, including Arduino Uno, temperature sensor, colour sensor, turbidity sensor, and pH sensor, coupled with advanced machine learning techniques. Ensuring the quality of milk is crucial for both consumers and producers, as it directly impacts health and economic aspects. This study aims to develop an efficient and cost-effective solution to predict milk quality attributes based on real-time sensor data. The Arduino Uno microcontroller acts as the central hub for collecting and processing data from the multiple sensors. The temperature sensor monitors the milk's temperature, the colour sensor assesses visual properties, the turbidity sensor gauges milk clarity, and the pH sensor measures acidity levels. These sensors collectively provide a comprehensive set of data points necessary for milk quality evaluation.

Machine learning algorithms are employed to process the sensor data and make predictions regarding milk quality. The trained models then utilize sensor data to predict various milk quality parameters, including freshness, spoilage, and overall quality.

Keywords: Arduino, temperature sensor, colour sensor, turbidity sensor, pH sensor, Machine Learning.