Ensemble Learning and Flask Deployment for Real-Time Milk Quality Grading
Dr K Anandan1, Leo Prakash S2
1Associative professor, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.
anandmca07@gmail.com
2Student of II MCA, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.
leoroshan2002@gmail.com
ABSTRACT
Milk quality plays a vital role in safeguarding public health and sustaining the economic viability of the dairy industry. Contaminated or substandard milk can lead to serious health risks, regulatory penalties, and financial losses across the supply chain. Traditionally, milk quality assessment relies on laboratory-based testing methods that measure parameters such as pH, temperature, fat content, taste, odor, turbidity, and color. While these methods are scientifically reliable, they are often slow, expensive, and dependent on skilled technicians and specialized equipment—making them impractical for small-scale producers and decentralized collection centers.
To address these limitations, this paper presents a smart, web-based system for automated milk quality prediction using machine learning. The system is developed using Python and Flask, offering a lightweight and scalable architecture suitable for both academic and industrial deployment. It leverages three classification algorithms—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes—to analyze input parameters and classify milk samples into three quality categories: Good, Medium, or Low. Among these, the SVM model demonstrated superior performance, achieving an accuracy of 94%, thereby validating its robustness and suitability for real-world applications.
The system features an intuitive graphical user interface (GUI) that allows users to input milk parameters manually or via CSV upload. Predictions are generated in real time, and users can visualize model performance through accuracy charts and confusion matrices. All prediction results are stored in a SQLite database, ensuring traceability and enabling historical analysis. This design not only reduces manual effort and human error but also enhances consistency and accessibility—empowering dairy farmers, quality control personnel, and researchers to make informed decisions without requiring technical expertise.
By automating the milk quality assessment process, the proposed system democratizes access to reliable testing tools, supports data-driven decision-making, and contributes to improved safety and efficiency across the dairy ecosystem. Future enhancements may include integration with real-time sensors, mobile application support, and cloud-based analytics to further extend the system’s reach and impact.
Keywords: Machine Learning, Milk Quality, SVM, Flask, SQLite, Dairy Automation, Classification Models
