Block Chain Based Supply Chain for Grading Milk Quality Using Machine Learning Techniques

Sharmili  Nukapeyi; Rajeswari  Bommala

Abstract

Milk is a primary source of nutrition in practically every country on the globe. Milk is widely packaged in a variety of containers such as packages, cans, glass bottles, and polyethylene terephthalate (PET) bottles. Milk, on the other hand, has a limited period for the customer to use it because it has an expiration date. Despite the clearance of the expiration date, it is possible that the milk could have become spoiled due to improper storage after opening the initial container. Numerous prior research studies have been conducted to explore issues related to milk spoilage and staleness. To assess the purity percentage in raw milk, the fraud detection algorithm is employed. This article systematically examines machine learning approaches for detecting milk spoilage and staleness. It delves into various aspects of the detection system, sensor technology, image processing techniques, and machine learning methods. Furthermore, the paper offers insights into the methodologies previously utilized by other researchers in their investigations. Finally, the approach presented in this paper helps prevent milk spoilage and finds the spoilage percentage in advance.

Abstract

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