IoT integrated fuzzy classification analysis for detecting adulterants in cow milk

Abstract

Internet of Things (IoT) and Artificial Intelligence (AI) are two of the emerging techniques used in creating more significant opportunities in smart dairy farming (SDF). Currently, the demand for milk is continuously increasing due to the world's growing population. Thus, some suppliers are inclined towards adopting fraudulent practices such as introducing adulterants into milk to eliminate the demand and supply gap. Conventional detection techniques require specific chemicals and equipment to determine the presence of adulterants in milk. Though effective, this technique has the downsides of producing qualitative results that are laborious, time-consuming and the same milk sample cannot be further analyzed for other adulterants. Hence, this paper presents an IoT-based solution to detect adulterants in milk by measuring its pH and electrical conductivity (EC) parameters. To achieve this, a fuzzy logic system was designed in MATLAB® using the Fuzzy Logic Toolbox™ and implemented on an arduino mega microcontroller to analyze the impurities present in milk samples through hardware implemented. This research revealed that milk's pH and EC values with no adulteration range from 6.45 to 6.67 and 4.65 mS/cm to 5.26 mS/cm, respectively. Finally, the collected data is stored in the cloud using the ThingSpeak™ web platform, interconnected with an IoT (ESP8266 Wi-Fi module).

Keywords

• Milk adulterants
• Internet of Things (IoT)
• pH
• Electrical Conductivity
• Industry 4.0