Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services

Design of agricultural product traceability system based on blockchain and RFID

Ensuring the traceability of agricultural products is essential for quality control and food safety. Recent technological advances have provided new ways to enhance traceability systems. This study aims to use blockchain technology, centralized database and RFID tags to develop a secure agricultural product traceability system, retain the detailed information of agricultural products traceability, ensure that the summary information of agricultural products on the chain cannot be modified, and optimize the SM3 algorithm to effectively summarize the traceability data and improve the efficiency of the system. The aggregated data is time-stamped, recorded on the blockchain, and written into an RFID tag. The optimization of the SM3 algorithm improved the efficiency by 30% and reduced the execution time of 192-byte messages to 210µs. The system ensures accurate linking of traceability data through secure data retention and unalterable summaries on the blockchain. The integrated use of blockchain, centralized database and RFID technology, as well as the enhanced SM3 algorithm, allows the system to meet the standards for data accuracy and performance requirements in agricultural traceability applications.

Analysis of Internet of things implementation barriers in the cold supply chain: An integrated ISM-MICMAC and DEMATEL approach

Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimize operating procedures, and increase productivity. The integration of the IoT in this complicated setting is hindered by specific barriers that require thorough examination. Prominent barriers to IoT implementation in a cold supply chain, which is the main objective, are identified using a two-stage model. After reviewing the available literature on IoT implementation, 13 barriers were identified. The survey data were cross-validated for quality, and Cronbach's alpha test was employed to ensure validity. This study applies the interpretative structural modeling technique in the first phase to identify the main barriers. Among these barriers, "regulatory compliance" and "cold chain networks" are the key drivers of IoT adoption strategies. MICMAC's driving and dependence power element categorization helps evaluate barrier interactions. In the second phase of this study, a decision-making trial and evaluation laboratory methodology was employed to identify causal relationships between barriers and evaluate them according to their relative importance. Each cause is a potential drive, and if its efficiency can be enhanced, the system benefits as a whole. The findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.

Virtualization and digital twins of the food supply chain for enhanced food safety

Meeting food safety requirements without jeopardizing quality attributes or sustainability involves adopting a holistic perspective of food products, their manufacturing processes and their storage and distribution practices. The virtualization of the food supply chain offers opportunities to evaluate, simulate, and predict challenges and mishaps potentially contributing to present and future food safety risks. Food systems virtualization poses several requirements: (1) a comprehensive framework composed of instrumental, digital, and computational methods to evaluate internal and external factors that impact food safety; (2) nondestructive and real-time sensing methods, such as spectroscopic-based techniques, to facilitate mapping and tracking food safety and quality indicators; (3) a dynamic platform supported by the Internet of Things (IoT) interconnectivity to integrate information, perform online data analysis and exchange information on product history, outbreaks, exposure to risky situations, etc.; and (4) comprehensive and complementary mathematical modeling techniques (including but not limited to chemical reactions and microbial inactivation and growth kinetics) based on extensive data sets to make realistic simulations and predictions possible. Despite current limitations in data integration and technical skills for virtualization to reach its full potential, its increasing adoption as an interactive and dynamic tool for food systems evaluation can improve resource utilization and rational design of products, processes and logistics for enhanced food safety. Virtualization offers affordable and reliable options to assist stakeholders in decision-making and personnel training. This chapter focuses on definitions and requirements for developing and applying virtual food systems, including digital twins, and their role and future trends in enhancing food safety.

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

The quality of fresh foods tends to deteriorate rapidly during harvesting, storage, and transportation. Intelligent detection equipment is designed to monitor and ensure product quality in the supply chain, measure appropriate food quality parameters in real time, and thus minimize quality degradation and potential financial losses. Through various available tracking devices, consumers can obtain actionable information about fresh food products. This paper reviews the recent progress in intelligent detection equipment for sensing the quality deterioration of fresh foods, including computer vision equipment, electronic nose, smart colorimetric films, hyperspectral imaging (HSI), near-infrared spectroscopy (NIR), nuclear magnetic resonance (NMR), ultrasonic non-destructive testing, and intelligent tracing equipment. These devices offer the advantages of high speed, non-destructive operation, precision, and high sensitivity.

Pricing decision and channel selection of fresh agricultural products dual-channel supply chain based on blockchain

The application of blockchain can effectively improve the efficiency of fresh agricultural product circulation and consumer trust, but it can also increase investment costs. In this context, this paper introduces parameters such as blockchain unit variable cost, the level of blockchain technology investment, and consumer channel preference in two dual-channel supply chain systems dominated by fresh agricultural product manufacturers: online direct sales and distribution. It compares and analyzes pricing and channel selection strategies in both cases of not using and using blockchain. The research shows that when blockchain is used, manufacturer profits are higher in the direct sales model than in the distribution model. Traditional retailers' profits are lower in the direct sales model than in the distribution model. Total supply chain profits are higher in the direct sales model than in the distribution model, and they exhibit an inverted "U" shape as the level of blockchain investment increases. In the online direct sales model, if the blockchain technology unit variable cost is within a certain threshold range, manufacturer profits, traditional retailer profits, and total supply chain profits are all higher than when blockchain technology is not used. In the online distribution model, when the blockchain variable cost and blockchain usage level meet certain conditions, manufacturers, traditional retailers, and online distributors all have higher profits when using blockchain technology than when not using it. This study provides theoretical guidance for the practical application of blockchain technology in dual-channel fresh agricultural product supply chains.

Deployment of Smart Specimen Transport System Using RFID and NB-IoT Technologies for Hospital Laboratory

In this study, we propose a specimen tube prototype and smart specimen transport box using radio frequency identification (RFID) and narrow band-Internet of Things (NB-IoT) technology to use in the Department of Laboratory Medicine, King Chulalongkorn Memorial Hospital. Our proposed method replaces the existing system, based on barcode technology, with shortage usage and low reliability. In addition, tube-tagged barcode has not eliminated the lost or incorrect delivery issues in many laboratories. In this solution, the passive RFID tag is attached to the surface of the specimen tube and stores information such as patient records, required tests, and receiver laboratory location. This information can be written and read multiple times using an RFID device. While delivering the specimen tubes via our proposed smart specimen transport box from one clinical laboratory to another, the NB-IoT attached to the box monitors the temperature and humidity values inside the box and tracks the box's GPS location to check whether the box arrives at the destination. The environmental condition inside the specimen transport box is sent to the cloud and can be monitored by doctors. The experimental results have proven the innovation of our solution and opened a new dimension for integrating RFID and IoT technologies into the specimen logistic system in the hospital.

Needs, Challenges and Countermeasures of SARS-CoV-2 Surveillance in Cold-Chain Foods and Packaging to Prevent Possible COVID-19 Resurgence: A Perspective from Advanced Detections

The pandemic caused by SARS-CoV-2 has a huge impact on the global economy. SARS-CoV-2 could possibly and potentially be transmitted to humans through cold-chain foods and packaging (namely good-to-human), although it mainly depends on a human-to-human route. It is imperative to develop countermeasures to cope with the spread of viruses and fulfil effective surveillance of cold-chain foods and packaging. This review outlined SARS-CoV-2-related cold-chain food incidents and current methods for detecting SARS-CoV-2. Then the needs, challenges and practicable countermeasures for SARS-CoV-2 detection, specifically for cold-chain foods and packaging, were underlined. In fact, currently established detection methods for SARS-CoV-2 are mostly used for humans; thus, these may not be ideally applied to cold-chain foods directly. Therefore, it creates a need to develop novel methods and low-cost, automatic, mini-sized devices specifically for cold-chain foods and packaging. The review intended to draw people's attention to the possible spread of SARS-CoV-2 with cold-chain foods and proposed perspectives for futuristic cold-chain foods monitoring during the pandemic.

Food Quality, Drug Safety, and Increasing Public Health Measures in Supply Chain Management

Over the last decade, there has been an increased interest in public health measures concerning food quality and drug safety in supply chains and logistics operations. Against this backdrop, this study systematically reviewed the extant literature to identify gaps in studying food quality and drug safety, the proposed solutions to these issues, and potential future research directions. This study utilized content analysis. The objectives of the review were to (1) identify the factors affecting food quality and possible solutions to improve results, (2) analyze the factors that affect drug safety and identify ways to mitigate them through proper management; and (3) establish integrated supply chains for food and drugs by implementing modern technologies, followed by one another to ensure a multi-layered cross-verification cascade and resource management at the different phases to ensure quality, safety, and sustainability for the benefit of public health. This review investigated and identified the most recent trends and technologies used for successfully integrated supply chains that can guarantee food quality and drug safety. Using appropriate keywords, 298 articles were identified, and 205 were shortlisted for the analysis. All analysis and conclusions are based on the available literature. The outcomes of this paper identify new research directions in public health and supply chain management.

Traceability of Sustainability and Safety in Fishery Supply Chain Management Systems Using Radio Frequency Identification Technology

At present, sustainability and emerging technology are the main issues in any supply chain management (SCM) sector. At the same time, the ongoing pandemic is increasing consumers' concerns about food safety, processing, and distribution, which should meet sustainability requirements. Thus, supervision and monitoring of product quality with symmetric information traceability are important in fresh food and fishery SCM. Food safety and traceability systems based on blockchain, Internet of Things (IoT), wireless sensor networks (WSN), and radio frequency identification (RFID) provide reliability from production to consumption. This review focuses on RFID-based traceability systems in fisheries' SCM, which have been employed globally to ensure fish quality and security, and summarizes their advantages in real-time applications. The results of this study will help future researchers to improve consumers' trust in fisheries SCM. Thus, this review aims to provide guidelines and solutions for enhancing the reliability of RFID-based traceability in food SCM systems so to ensure the integrity and transparency of product information.

Status Estimation and In-Process Connection of Kanbans Using BLE Beacons and LPWA Network to Implement Intra-Traceability for the Kanban System

IoT-based measurement systems for manufacturing have been widely implemented. As components that can be implemented at low cost, BLE beacons have been used in several systems developed in previous research. In this work, we focus on the Kanban system, which is a measure used in manufacturing strategy. The Kanban system emphasizes inventory management and is used to produce only required amounts. In the Kanban system, the Kanban cards are rotated through the factory along with the products, and when the products change to a different process route, the Kanban card is removed from the products and the products are assigned to another Kanban. For this reason, a single Kanban cannot trace products from plan to completion. In this work, we propose a system that uses a Bluetooth low energy (BLE) beacon to connect Kanbans in different routes but assigned to the same products. The proposed method estimates the beacon status of whether the Kanban is inside or outside a postbox, which can then be computed by a micro controller at low computational cost. In addition, the system connects the Kanbans using the beacons as paired connection targets. In an experiment, we confirmed that the system connected 70% of the beacons accurately. We also confirmed that the system could connect the Kanbans at a small implementation cost.

Analysis of factors affecting IoT-based smart hospital design

Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a “boat”), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used.