Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

Internet of Things (IoT) devices are large in number, widely distributed, weak in protection ability, and vulnerable to various malicious attacks. Intrusion detection technology can provide good protection for network equipment. However, the normal traffic and abnormal traffic in the network are usually imbalanced. Imbalanced samples will seriously affect the performance of machine learning detection algorithm. Therefore, this paper proposes an intrusion detection method based on data augmentation, namely TMG-IDS. We name the proposed data augmentation model TMG-GAN, which is a data augmentation method based on generative adversarial networks (GAN). First, TMG-GAN has a multi-generator structure, which can be used to generate different types of attack data simultaneously. Second, we increase the classifier structure, which can optimize the generator and discriminator more efficiently based on the classification loss. Third, we calculate the cosine similarity between the generated samples and the original samples and other types of generated samples as a generator loss, which can further improve the quality of generated samples and reduce the class overlap area between the distributions of various generated samples. We conduct extensive experiments on two intrusion detection datasets, CICIDS2017 and UNSW-NB15. The experimental results show that compared with the advanced oversampling algorithm and the latest intrusion detection algorithm, the proposed TMG-IDS method has a good detection effect under the three indicators of Precision, Recall and F1-score.

The world is facing an unprecedented socio-economic crisis caused by the novel coronavirus infection (COVID-19). The virus is also spreading through the import and export food supply chains. The Chinese authorities have discovered the COVID-19 virus in various imported frozen meat packages. Traceability plays a vital role in food quality and food safety. The Internet of Things (IoT) provides solutions to overseeing environmental conditions, product quality, and product traceability. These solutions are traditionally based on a centralized architecture, which does not guarantee tamper-proof data sharing. The blockchain is an emerging technology that provides tamper-proof data sharing in real-time. This article presents a blockchain-enabled supply chain architecture to ensure the availability of a tamper-proof audit trail. This tamper-proof audit trail helps to make sure that all safety measures are undertaken to minimize the risk of COVID-19 and other bacteria, fungi, and parasites being present in the frozen meat supply chain.

Food supply chain plays a vital role in human health and food prices. Food supply chain inefficiencies in terms of unfair competition and lack of regulations directly affect the quality of human life and increase food safety risks. This work merges Hyperledger Fabric, an enterprise-ready blockchain platform with existing conventional infrastructure, to trace a food package from farm to fork using an identity unique for each food package while keeping it uncomplicated. It keeps the records of business transactions that are secured and accessible to stakeholders according to the agreed set of policies and rules without involving any centralized authority. This paper focuses on exploring and building an uncomplicated, low-cost solution to quickly link the existing food industry at different geographical locations in a chain to track and trace the food in the market.

This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model.

Blockchain-based applications are gaining traction in various application fields, including supply chain management, health care, and finance. The Internet of Things (IoT) is a critical component of these applications since it allows for data collection from the environment. In this work, we integrate the Hyperledger Fabric blockchain and IoT devices to demonstrate the access control and establish the root of trust for IoT devices. The Hyperledger Fabric is designed to be secure against unwanted access and use through encryption protocols, access restrictions, and cryptography algorithms. An attribute-based access control (ABAC) mechanism was created using Hyperledger Fabric components only to gain access to the IoT device. Single board computers based on the ARM architecture are becoming increasingly powerful and popular in automation applications. In this study, the Raspberry Pi 4 Model B based on ARM64 architecture is used as the IoT device. Because the ARM64 architecture is not supported by default, we build executable binaries and Docker images for the ARM64 architecture, using the Hyperledger Fabric source code. On an IoT device, we run the fabric node in native mode to evaluate the executable binaries generated for the ARM64 architecture. Through effective chaincode execution and testing, we successfully assess the Hyperledger fabric blockchain implementation and access control mechanism on the ARM64 architecture.