Milk chain with ML & Blockchain: Revolutionizing Transparency for Ensuring Milk Purity in Dairy Industry

The global issue of milk adulteration, particularly prevalent in densely populated regions like India, poses significant health risks. Recent incidents, such as toxic milk in school meals resulting in deaths and hospitalizations in Mathura district's Agra division, emphasize the urgency for such measures. The proposed Blockchain-enabled decentralized supply chain platform "Milk Chain" address the alarming issues of malnutrition arising from the consumption of adulterated dairy products. Recognizing the transformative potential of blockchain technology, the proposed platform aims to establish a transparent, tamper-proof, and sustainable supply chain for the dairy industry. Beyond conventional traceability, the focus extends to preserving nutritional values, identifying adulteration and contamination, promoting economic viability for dairy farmers, combating counterfeit products, and enhancing overall company revenue. In this study, we investigated the key factors influencing milk quality, examining eight variables, including pH, temperature, taste, odor, fat content, turbidity, color, and grade. Employing an advanced machine learning algorithm like categorical boosting (CatBoost), we classified milk samples into low, medium, and high grades. The findings shed light on the critical parameters affecting milk quality and pave the way for the development of a robust and reliable system to ensure the integrity of dairy products throughout the supply chain. This research contributes to the urgent call for an upgraded supply chain model to safeguard public health and nutrition in regions heavily dependent on dairy consumption.