Milk Quality Prediction using Machine Learning and Deep Learning Techniques

Milk quality assessment is a critical concern in the dairy industry due to its impact on public health, food safety, and economic stability. Traditional laboratory-based methods for evaluating milk quality are time-consuming, costly, and unsuitable for real-time monitoring. With the advancement of data-driven technologies, there is a growing need for intelligent systems that can automatically predict milk quality using computational techniques. This research focuses on developing a machine learning and deep learning-based framework for accurate milk quality prediction. The study utilizes a dataset of 1,059 samples with key features such as pH, temperature, taste, odour, fat, turbidity, Grade, thick ness and colour. These attributes serve as important indicators of milk freshness and quality. Multiple machine learning models, including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbours’ (KNN), and Random Forest, are implemented and compared. In addition, deep learning models such as Artificial Neural Network (ANN) and a 1D Convolutional Neural Network (1D CNN) are used to capture complex relationships in the data. Advanced preprocessing techniques such as normalization and feature scaling are applied to improve model performance. The models are evaluated using performance metrics including accuracy, precision, recall, and F1-score. Experimental results show that Random Forest and ANN achieve the highest accuracy of 99.53%, while the CNN model achieves 99.06%, demonstrating strong predictive capability and scalability for real-time applications. Furthermore, Explainable Artificial Intelligence (XAI) is integrated using SHAP to improve model interpretability. The analysis highlights that feature such as pH, temperature, and fat content play a significant role in prediction. This enhances transparency and reliability of the system. Overall, the proposed approach provides an efficient and scalable solution for automated milk quality prediction. It has strong potential for real-world applications such as smart dairy monitoring, IoT-based systems, and early detection of milk spoilage.