Fruit Verity-Detecting Adulteration in fruits Using Machine Learning.

Abstract

Abstract: Detecting fruit adulteration is a critical challenge with traditional methods often falling short due to their timeconsuming nature and high costs. Chemical tests, while common, have limitations that hinder their efficiency. Fortunately, a beacon of hope emerges in the realm of machine learning, offering a promising alternative for identifying adulteration in fruits. By leveraging the power of data, machine learning algorithms can be finely tuned to recognize intricate patterns that act as red flags for adulteration. These data sources span from detailed laboratory tests to the nuances revealed through sensory analysis and even extend to the analysis of fruit images. Once a machine learning model undergoes meticulous training on this diverse array of datasets, it transforms into a potent tool for swift and precise adulteration detection. This breakthrough not only strengthens food safety measures but also accelerates the identification of compromised products, ultimately safeguarding public health. Consumers are empowered with a robust defense against the persistent problem of adulteration. The revolutionary nature of this approach brings about a paradigm shift, offering a proactive solution that outpaces traditional methods