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

Every problem has a solution, and the bulk of those answers are made possible by advances in technology. The new ideas and their implementation have dramatically changed the human world in the last twenty years. Everything has been automated, from routine domestic tasks to industrial manufacturing, making daily living considerably simpler. Yet the secret to getting the desired results is deploying the appropriate technology in the correct way. One such technology is machine learning, which uses algorithms to make the machine understand and act more precisely and accurately like a human. A maj or worry in the dairy business is the quality of the milk, which is predicted by a machine learning model in “MilkSafe: A Hardware-Enabled Milk Quality Prediction using Machine Learning.” Sensors were used to gather the milk characteristics, including pH, temperature, turbidity, and colour, which were then entered into the model for analysis and condition prediction. Based on various milk characteristics, the pH, turbidity, colour, and temperature outputs will display a range of values. The milk is rated as low, medium, or high based on these criteria. The sensors will gather this information from the milk with the aid of the microcontroller, and the microcontroller being used in this application is the Arduino UNO. The serial monitor of the Arduino IDE will show the output. The gathered data will be used to train the model, and this model will provide us with the findings of our analysis on milk quality. The algorithms utilised in this study include Naive Bayes, Random Forest, KNN, Logistic Regression, and Random Forest with being the most accurate. Using four input features (colour, turbidity, temperature, and pH), the suggested model produces 98.27% accuracy, enabling a fully automated, dependable, and effectively used convenient gadget.