Abstract

Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern for many people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile applications. They generate a large amount of data that can be optimized and used efficiently by advanced deep learning (ADL) techniques. The importance of such innovations from the viewpoint of supply chain management is significant in different processes such as for broadened visibility, provenance, digitalization, disintermediation, and smart contracts. This article takes the secure IoT–blockchain data of Industry 4.0 in the food sector as a research object. Using ADL techniques, we propose a hybrid model based on recurrent neural networks (RNN). Therefore, we used long short-term memory (LSTM) and gated recurrent units (GRU) as a prediction model and genetic algorithm (GA) optimization jointly to optimize the parameters of the hybrid model. We select the optimal training parameters by GA and finally cascade LSTM with GRU. We evaluated the performance of the proposed system for a different number of users. This paper aims to help supply chain practitioners to take advantage of the state-of-the-art technologies; it will also help the industry to make policies according to the predictions of ADL.

Highlights

  • Industry 4.0 incorporates wireless technology, sensors, and smart machines to create a wholly connected enterprise

  • Food safety is a significant concern in the modern world; governments need to quickly formulate policies and take various measures to strengthen the management of the safe production of agricultural products, including identification and tracking

  • We propose a hybrid model based on recurrent neural networks (RNN)

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Summary

Introduction

Industry 4.0 incorporates wireless technology, sensors, and smart machines to create a wholly connected enterprise. The Internet of Things (IoT) is helping to connect devices and sensors, and the combination of IoT and blockchain is proving to be a time and money-saving approach—one which generates a large amount of data Analyzing these data using advanced deep learning techniques (ADL). AI provides critical alerts and recommendations for when things go wrong; it helps us to monitor suppliers in other regions of the world to prevent the problem proactively It simplifies supply chain relationship management and helps us to optimize our supply chain processes to increase customer satisfaction and generate more profit for the business. This technology can control and monitor risk mitigation efforts and strengthen the supply chain, and it could prove useful in preventing security breaches Dairy products such as meat, milk, yogurt, butter, and cheese play a vital role in meeting the food requirements of humans.

IoT and Blockchain in Food Industry
Objective
Provenance
Payments
Management
System Model
Proposed Solution
Private Blockchain Platform
Endpoint Security
Integration of IoT with Blockchain
Advanced Deep Learning
Genetic Algorithm
LSTM and GRU
GA-Based Hybrid Deep Learning Model
Performance Evaluation
Implications
Findings
Conclusions
Full Text
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