Affordable IoT-Based Milk Quality Assessment for Small-Scale Dairy Farmers Using Machine Learning

Maintaining milk quality is essential not only to avoid losses for small-scale dairy farmers and milk vendors but also builds a basis on which standards of health can be proven and consumer confidence built-mostly for the sustainability of a dairy market. Traditional methods of establishing milk quality assessments are usually expensive, prone to human mistakes, and limited to larger dealers, thus challenging competition by the smaller vendors. To overcome these barriers we designed an IoT-based milk grading system to particularly suit the needs of small-scale dairy operators that would be developed as an affordable and reliable quality control solution. Low-cost IoT sensors monitor a set of key indicators that are used to measure the important aspects of milk quality: temperature, pH, and turbidity, sending real-time data to the cloud-based platform. Utilizing the Decision Tree algorithm, the system classifies milk quality into low, medium, and high grades with an accuracy of 92.86%. This now makes real-time quality insights available to end-users through a user-friendly web interface without the need for expensive equipment or highly skilled labor. This solution also ensures better milk quality while enhancing consumer safety by reducing dependence on subjective evaluations. In this way, this small vendor continues to remain competitive in the market.