Title: MilkoSense: A Rapid AI-Assisted Milk Quality Testing System for Enhanced Dairy Safety and Efficiency


Authors: Kapil Goyal, Aditi Vishwakarma Anya Jain, Nisha Rathi Abhishek Verma, Ashish Anjana


Published in: Volume 3 Issue 1 Jan June 2026, Page No. 64-71


DOI: 10.63844/IJAITR.v3.i1.2026.64-71 cite


Keywords: Milk Quality Testing, Artificial Intelligence, Machine Learning, IoT, Multi-Sensor Integration, Food Safety, Dairy Industry, Real-time Monitoring, Adulteration Detection, Smart Agriculture


Abstract: The dairy industry faces critical challenges in ensuring milk quality and safety, with traditional testing methods like the Methylene Blue Dye Reduction Test (MBRT) requiring 4-6 hours, creating significant bottlenecks in quality assurance processes. This paper presents MilkoSense, a rapid, portable, and AI-assisted milk quality monitoring system that integrates multi-sensor technologies with advanced machine learning algorithms. The system combines six key sensor types-pH, temperature, turbidity, Total Dissolved Solids (TDS), gas detection, and colorimetric analysis-with Convolutional Neural Networks (CNN) and ensemble learning methods. Integration with IoT connectivity through ESP32 platforms enables real-time data transmission and cloud-based analytics. The colorimetric detection methodology utilizes chromogenic reactions to identify multiple adulterants simultaneously, while machine learning models predict spoilage timelines and detect microbial contamination patterns. MilkoSense achieves significant reduction in testing time from 4-6 hours to under 30 minutes with enhanced accuracy exceeding 92%, improved supply chain transparency, and substantial cost savings through early intervention. This cost-effective solution represents a paradigm shift in dairy quality assurance, making advanced testing accessible across the entire value chain.


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