Impact of nutrients in food quality and safety by machine learning classifier using internet of things

The traditional Food Supply Chain (FSC) is challenged by a number of issues, including uncertainty, security, expense, complexity, and quality worries. A precise supply chain is required to address these problems. Managing the supply chain to distribute high-quality goods is a difficult task in the modern food sector. The efficient Tree Augmented Naive Bayes linked with FSC utilizing Internet of Things (IoT) is proposed in this paper to enable tracking, tracing, and managing the full food supply chain process, including supplier, exporter, and consumers. This paper's goals are to evaluate the food safety and create the best possible chronological data for the analysis of potential future assumptions. It also intends to connect the producer and the buyer by delivering high-quality food goods while using IoT technology to transport food from the producers to the customers. IoT-based food supply chain traceability is used to successfully transact data with ambiguous, unreliable, and inadequate data. In order to provide secure backdrop and food safety for FSC process utilizing IoT technology, the suggested effective Tree Augmented Naive Bayes algorithm would be able to overcome the challenges of conventional supply chain.