Milk quality prediction using Machine Learning integrated with Arduino

A staple of human nourishment, milk can be tainted, compromising consumer health and the dairy industry's reputation. Chemicals, particularly water, are often added to milk to adulterate it.Traditional methods for assessing milk quality typically involve sensory evaluation, chemical analysis, and microbiological examination may not be effective especially in low concentrations. Milk has a complex chemical composition. Detecting adulterants within this complex matrix requires advanced analytical techniques. This paper explores an innovative approach to tackle the problem of milk adulteration by integrating machine learning with sensor technology. The system is designed for monitoring and predicting milk quality using a combination of data collection through sensors, machine learning techniques, and web deployment. The system integrates hardware components such as Arduino Uno and various sensors (pH, turbidity, temperature) to gather data on milk quality factors. Data was collected after testing eight different milk samples which includes skimmed milk, homogenized milk, standardized milk with varying fat concentrations. The data is processed and analyzed using machine learning algorithms including Support Vector Machine, Adaboost classifier, and Random Forest to predict milk quality.