Hybrid Deep Learning Approach for Milk Quality Prediction

Abstract

Milk quality prediction is considered a vital research area due to increase the need for obtain sustainable development goals. This study aims to predict milk quality by integrate gated recurrent units (GRUs) and residual network (ResNet). Our model was evaluated on milk quality prediction dataset with seven unique feature such as pH, temperature, taste, odor, fat, turbidity, and color. The prediction output is classified with high (Goog), Low (Bad), and Medium (Moderate) classes. Our model shows superior results with comparison with multi-layer perceptron (MLP), random forest (RF) and support vector machine (SVM). In terms of accuracy, precision, recall, and F1-score, 0.996, 0.992, 0.992, 0.992.