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Machine Learning-Based Adulteration Detection in Fruits

Khallikkunaisa ., Shivani S, Spandana V, Sudhiksha S, Vidhya K C

Abstract


A fruit adulteration technique based on the Internet-of-things (IoT) is created to identify the concentration of the formalin using the machine learning approaches. Various methods of the machine learning were used to classify the fruits based on their extracted attributes. A Volatile compound sensor coupled to an Arduino Uno 3 is used to obtain the concentration of formalin as the function of the output voltage of any fruit. Our system uses Machine learning techniques to precisely predict the right formalin level at all temperature and can distinguish between naturally occurring and artificially added formalin. This System’s objective is to replace the manual inspection system. This system captures images from the camera that are mounted on the belt conveyors. The necessary fruit attributes such as color and size are then obtained by image processing. Based on picture pixels, contaminated fruit is identified. Sorting is done according to size and color.


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References


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