Milk quality estimation using machine learning algorithms

This research presented a machine learning-based approach for estimating milk quality using physicochemical characteristics of milk. The proposed system utilized seven input parameters—pH, temperature, taste, odor, fat content, turbidity, and colour—to classify milk samples into High, Medium, and Low quality categories. Four supervised machine learning algorithms, namely Extra Trees Classifier, Random Forest Classifier, AdaBoost Classifier, and Artificial Neural Network (ANN), were implemented and comparatively evaluated. The experimental results demonstrated that the Extra Trees Classifier achieved the highest prediction accuracy of 95.00%, outperforming Random Forest (94.58%), AdaBoost (86.25%), and ANN (73.75%). Based on this comparative analysis, the Extra Trees model was selected as the final prediction model and successfully deployed in a Flask-based web application. The developed application provides real-time milk quality prediction through a simple graphical interface while maintaining prediction history using SQLite. The combination of machine learning and web deployment offers a practical, efficient, and cost-effective solution for preliminary milk quality assessment. Although laboratory testing remains essential for official certification, the proposed system can serve as an effective decision-support tool for dairy farmers, milk collection centers, educational institutions, and researchers.

The proposed system can be further enhanced by incorporating additional technologies and larger datasets. Future improvements include: ·                  Integration of IoT-based sensors for automatic collection of milk quality parameters. ·                  Development of Android and iOS mobile applications for portable prediction. ·                  Evaluation using larger datasets collected from different geographical regions. ·                  Deployment on cloud platforms for remote access and scalability. ·                  Integration with dairy management systems for continuous quality monitoring. ·                  Investigation of advanced deep learning techniques and explainable AI methods to improve prediction accuracy and interpretability. These enhancements would increase the applicability of the system in commercial dairy environments and support intelligent decision-making in food quality management.