Abstract
MILK ADULTERATION DETECTION USING IOT AND ML TECHNIQUES
Mohammed Abubakar*, Adithi Gowda, Adnan Syeed, Trupthi C. M. and Sudarshan G. K.
ABSTRACT
Ensuring the purity of milk is paramount for public health, and theincreasing instances of adulteration necessitate advanced detectionmethods. This paper presents a comprehensive approach to milkadulteration detection using a combination of Internet of Things (IoT)and Machine Learning (ML) techniques. The proposed systemintegrates pH, temperature, turbidity, TDS (Total Dissolved Solids)and conductivity sensors deployed across the milk supply chain to collect real-time data.These sensors monitor critical parameters, providing a rich dataset for analysis. The collecteddata is processed using a Support Vector Machine (SVM) ML algorithm, leveraging itscapacity for efficient classification. SVM is trained on labeled datasets, enabling it to discernbetween genuine and adulterated milk samples based on the sensor readings. This modelensures robust and accurate detection of various adulterants, including those affecting pHlevels, temperature, turbidity, and conductivity.
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