Editorial
Milk Quality Prediction Using Machine Learning
@ARTICLE{10.4108/eetiot.4501, author={Drashti Bhavsar and Yash Jobanputra and Nirmal Keshari Swain and Debabrata Swain}, title={Milk Quality Prediction Using Machine Learning}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2023}, month={11}, keywords={Machine Learning, Milk Quality Prediction, Random Forest, Support Vector Machine, Label Encoding}, doi={10.4108/eetiot.4501} }- Drashti Bhavsar
Yash Jobanputra
Nirmal Keshari Swain
Debabrata Swain
Year: 2023
Milk Quality Prediction Using Machine Learning
IOT
EAI
DOI: 10.4108/eetiot.4501
Abstract
Milk is the main dietary supply for every individual. High-quality milk shouldn't contain any adulterants. Dairy products are sold everywhere in society. Yet, the local milk vendors use a wide range of adulterants in their products, permanently altering the evaporated. Using milk that has gone bad can have serious health consequences. On October 18 of this year, the Food Safety and Standards Authority of India (FSSAI), the nation's top food safety authority, released the final result of the National Milk Safety and Quality Survey (NMSQS) and declared the milk readily available in India to be "mostly safe." According to an FSSAI survey, 68.4% of the milk in India is tainted. The quality of milk cannot be checked by any equipment or special system. Milk that has not been pasteurized has not been treated to get rid of harmful bacteria. Infected raw milk may contain Salmonella, Campylobacter, Cryptosporidium, E. coli, Listeria, Brucella, and other dangerous pathogens. These microorganisms pose a major risk to your family's health. Manually analyzing the various milk constituents can be very challenging when determining the quality of the milk. Analyzing and discovering with the aid of machine learning can help with this endeavor. Here a machine learning-based milk quality prediction system is developed. The proposed technology has shown 99.99% classification accuracy.
Copyright © 2023 D. Bhavsar et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
