Intelligent Food Safety and Authentication Platform: A Blockchain-Enabled Machine Learning System for Food Verification

Authors -Dr.S.Dhanabal, Keerthana S, Sarumathy A, Abarna B, Sridhar R

Abstract- – Livestock and agriculture are essential to social and economic stability. Many individuals have serious concerns about food safety and supply chain transparency. Blockchain and the Internet of Things (IoT) are becoming more popular because of their success in a variety of applications. They provide a lot of data, which advanced deep learning (ADL) approaches can effectively use and optimize. From the perspective of supply chain management, these developments are important for a variety of activities, including increased visibility, provenance, digitization, disintermediation, and smart contracts. The safe IoT-blockchain data from Industry 4.0 in the food industry is the subject of this article’s investigation. We suggest a hybrid model based on recurrent neural networks (RNN) using ADL approaches. In order to optimize the parameters of the hybrid model, we combined genetic algorithm (GA) optimization with long short-term memory (LSTM) and gated recurrent units (GRU) as prediction models. After using GA to determine the ideal training settings, we cascade LSTM with GRU. We tested the suggested system’s performance with varying user counts. In addition to assisting supply chain practitioners in utilizing cutting-edge technologies, this article will assist the industry in formulating regulations that align with ADL’s forecasts.

DOI: /10.61463/ijset.vol.9.issue6.522