Blockchain technology and traceability in the agrifood industry

Recent advances in artificial intelligence towards the sustainable future of agri-food industry

Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a significant uptick in recent years. Therefore, comprehensive understanding of these techniques is needed to broaden its application in agri-food supply chain. In this review, we explored cutting-edge artificial intelligence methodologies with a focus on machine learning, neural networks, and deep learning. The application of artificial intelligence in agri-food industry and their quality assurance throughout the production process is thoroughly discussed with an emphasis on the current scientific knowledge and future perspective. Artificial intelligence has played a significant role in transforming agri-food systems by enhancing efficiency, sustainability, and productivity. Many food industries are implementing the artificial intelligence in modelling, prediction, control tool, sensory evaluation, quality control, and tackling complicated challenges in food processing. Similarly, artificial intelligence applied in agriculture to improve the entire farming process, such as crop yield optimization, use of herbicides, weeds identification, and harvesting of fruits. In summary, the integration of artificial intelligence in agri-food systems offers the potential to address key challenges in agriculture, enhance sustainability, and contribute to global food security.

Agri-Food Value Chain Traceability Using Blockchain Technology: Portuguese Hams' Production Scenario

The globalization of food markets has led companies to buy products not only locally, but also from other corners of the world. This has introduced complexity into supply chains, as products have to move longer distances and pass through more companies before reaching the end consumer. The meat industry has been no different. Events such as animal disease outbreaks have diminished consumer confidence in the industry and the supply chain. Coupled with this, consumers started demanding "more transparent" meat products. This has led companies to think about new traceability systems, which continue to enforce food safety and health rules, but at the same time enhance and make transparent to the consumer the origin and constitution of their products. This article proposes a traceability system in the agri-food (meat industry) with a multi-chain architecture, among them, blockchain. The use of blockchain in the traceability system helped to mitigate the omission of relevant data for the traceability process, allowing us to guarantee the immutability, reliability, and transparency of the data along the value chain. At the same time, the system was able to reduce the time of the traceability process by giving the user the possibility to access the traced information via a unique product identifier.

Emerging Perspectives of Blockchains in Food Supply Chain Traceability Based on Patent Analysis

In the field of blockchain (BC) technology application in the food supply chain (FSC), a patent portfolio is collected, described, and analyzed using Latent Dirichlet Allocation (LDA) modeling, with the aim of obtaining insight into technology trends in this emerging and promising field. A patent portfolio consisting of 82 documents was extracted from patent databases using PatSnap software. The analysis of latent topics using LDA indicates that inventions related to the application of BCs in FSCs are patented in four key areas: (A) BC-supported tracing and tracking in FSCs; (B) devices and methods supporting application of BCs in FSCs; (C) combining BCs and other ICT technologies in FSC; and (D) BC-supported trading in FSCs. Patenting of BC technology applications in FSCs started during the second decade of the 21st century. Consequently, patent forward citation has been relatively low, while the family size confirms that application of BCs in FSCs is not yet widely accepted. A significant increase in the number of patent applications was registered after 2019, indicating that the number of potential users in FSCs is expected to grow over time. The largest numbers of patents originate from China, India, and the US.

Redefining food safety traceability system through blockchain: findings, challenges and open issues

In the last few decades, there has been an increase in food safety and traceability issues. To prevent accidents and misconduct, it became essential to establish Food Safety Traceability System (FSTS) to trace the food from producer to consumer. The traceability systems can help track food in supply chains from farms to retail. Numerous technologies such as Radio Frequency Identification (RFID), sensor networks, and data mining have been integrated into traditional food supply chain systems to remove unsafe food products from the chain. But, these are not adequate for the current supply chain market. The emerging technology of blockchain can overcome safety and tracking issues. This can be possible with the help of blockchain features like transparent, decentralized, distributed, and immutable. Most of the previous works missed the discussion of the systematic process and technology involved in implementing the FSTS using blockchain. In this paper, we have discussed an organized state of research of the existing FSTS using blockchain. This survey paper aims to outline a detailed analysis of blockchain technology, FSTS using blockchain, consensus algorithms, security attacks, and solutions. Several survey papers and solutions based on blockchain are included in this research paper. Also, this work discusses some of the open research issues related to FSTS.

Investment Decision of Blockchain-Based Traceability Service Input for a Competitive Agri-Food Supply Chain

Competitive agri-food supply chain (hereafter, AFSC) is an important component of AFSC. In a competitive environment, more and more AFSCs use blockchain-based traceability services (hereafter, BBTS) to improve the traceability level of agricultural products. The investment rules concerning BBTS and the coordination rules in an AFSC are vital issues for many firms who want to adopt BBTS. To explore these laws, we constructed two competitive AFSCs, each of which included a supplier and a retailer. Considering the new changes in consumers’ perception of product quality and safety after using the BBTS, the demand function was modified. Then we built the income functions of chain members under three situations of investment. The research found that: (1) the improvement of customers’ perceived quality by using the BBTS can increase their benefits; (2) when decision-makers want to invest in the BBTS, they should pay attention to consumers’ perceived quality safety factor for their competitive products; (3) when the investment cost is greater than its threshold value, two competitive AFSCs should invest in the BBTS together.

Integration of Innovative Technologies in the Agri-Food Sector: The Fundamentals and Practical Case of DNA-Based Traceability of Olives from Fruit to Oil

Several socio-economic problems have been hidden by the COVID-19 pandemic crisis. Particularly, the agricultural and food industrial sectors have been harshly affected by this devastating disease. Moreover, with the worldwide population increase and the agricultural production technologies being inefficient or obsolete, there is a great need to find new and successful ways to fulfill the increasing food demand. A new era of agriculture and food industry is forthcoming, with revolutionary concepts, processes and technologies, referred to as Agri-food 4.0, which enables the next level of agri-food production and trade. In addition, consumers are becoming more and more aware about the origin, traceability, healthy and high-quality of agri-food products. The integration of new process of production and data management is a mandatory step to meet consumer and market requirements. DNA traceability may provide strong approach to certify and authenticate healthy food products, particularly for olive oil. With this approach, the origin and authenticity of products are confirmed by the means of unique nucleic acid sequences. Selected tools, methods and technologies involved in and contributing to the advance of the agri-food sector are presented and discussed in this paper. Moreover, the application of DNA traceability as an innovative approach to authenticate olive products is reported in this paper as an application and promising case of smart agriculture.

HerBChain, a blockchain-based informative platform for quality assurance and quality control of herbal products

The use of medicinal plants is popular worldwide. Correct herbal authentication is of paramount importance to the safety and best interest of consumers. On the market, there is no comprehensive blockchain-based system to track the processes from plantation to manufacturing and to the sale. With the advancement of information technology, an open and transparent blockchain-based platform, HerBChain, was created to enhance the quality control of herbal products. The implementation of blockchain technology is to minimize the manipulation of recorded information. HerBChain is an information platform for recording the six important processes of herbal product manufacturing and marketing, which include plantation base, TCM processing factory, TCM manufacturer, testing laboratory, distributor and retailer. By duly recording the parameters and data essential for product quality in manufacturing and supply chain, the traceability and reliability of the products can be ensured.

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A blockchain-based informative platform, HerBChain, was established for the herbal industry.

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Plantation base, processing factory, manufacturer, testing laboratory, distributor and retailer are recorded.

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The traceability and reliability of herbal products can be ensured.

A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare

Several precision livestock farming (PLF) technologies, conceived for optimizing farming processes, are developed to detect the physical and behavioral changes of animals continuously and in real-time. The aim of this review was to explore the capacity of existing PLF technologies to contribute to the assessment of pig welfare. In a web search for commercially available PLF for pigs, 83 technologies were identified. A literature search was conducted, following systematic review guidelines (PRISMA), to identify studies on the validation of sensor technologies for assessing animal-based welfare indicators. Two validation levels were defined: internal (evaluation during system building within the same population that were used for system building) and external (evaluation on a different population than during system building). From 2,463 articles found, 111 were selected, which validated some PLF that could be applied to the assessment of animal-based welfare indicators of pigs (7% classified as external, and 93% as internal validation). From our list of commercially available PLF technologies, only 5% had been externally validated. The more often validated technologies were vision-based solutions (n = 45), followed by load-cells (n = 28; feeders and drinkers, force plates and scales), accelerometers (n = 14) and microphones (n = 14), thermal cameras (n = 10), photoelectric sensors (n = 5), radio-frequency identification (RFID) for tracking (n = 2), infrared thermometers (n = 1), and pyrometer (n = 1). Externally validated technologies were photoelectric sensors (n = 2), thermal cameras (n = 2), microphone (n = 1), load-cells (n = 1), RFID (n = 1), and pyrometer (n = 1). Measured traits included activity and posture-related behavior, feeding and drinking, other behavior, physical condition, and health. In conclusion, existing PLF technologies are potential tools for on-farm animal welfare assessment in pig production. However, validation studies are lacking for an important percentage of market available tools, and in particular research and development need to focus on identifying the feature candidates of the measures (e.g., deviations from diurnal pattern, threshold levels) that are valid signals of either negative or positive animal welfare. An important gap identified are the lack of technologies to assess affective states (both positive and negative states).

Consumer Perceptions of Precision Livestock Farming-A Qualitative Study in Three European Countries

Scholars in the fields of animal science and technology have investigated how precision livestock farming (PLF) can contribute to the quality and efficiency of animal husbandry and to the health and welfare of farm animals. Although the results of such studies provide promising avenues for the development of PLF technologies and their potential for the application in animal husbandry, the perspectives of consumers with regard to PLF technologies have yet to be the subject of much investigation. To address this research gap, the current study explores consumer perceptions of PLF technologies within the pork and dairy value chains. The investigation is based on results from six focus group discussions conducted in three European countries, each reflecting a different market environment: Finland, the Netherlands and Spain. The results indicate that consumers expect the implementation of different PLF technologies to enhance the health and welfare of farm animals, while generating environmental improvements and increasing the transparency of value-chain processes. The analysis further reveals three over-arching consumer concerns: (1) the fear that the integration of PLF technologies will introduce more industrialisation into livestock farming production; (2) the concern that PLF technologies and data are vulnerable to misuse and cyber-crime; and (3) the concern that PLF information is not communicated adequately to allow informed purchase decisions. The research findings provide directions for members of the animal-based food value chain to make informed decisions to improve their sustainability, social responsibility and credibility by endorsing the acceptance of PLF (technologies) amongst European consumers.

Artificial Intelligence to Improve the Food and Agriculture Sector

The world population is expected to reach over 9 billion by 2050, which will require an increase in agricultural and food production by 70% to fit the need, a serious challenge for the agri-food industry. Such requirement, in a context of resources scarcity, climate change, COVID-19 pandemic, and very harsh socioeconomic conjecture, is difficult to fulfill without the intervention of computational tools and forecasting strategy. Hereby, we report the importance of artificial intelligence and machine learning as a predictive multidisciplinary approach integration to improve the food and agriculture sector, yet with some limitations that should be considered by stakeholders.

Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

Modern agricultural technology management is nowadays crucial in terms of the economy and the global market, while food safety, quality control, and environmentally friendly practices should not be neglected. This review aims to give perspectives on applying big data analytic and modern technologies to increase the efficacy and effectiveness of the coffee supply chain throughout the process. It was revealed that several tools such as wireless sensor networks, cloud computing, Internet of Things (IoT), image processing, convolutional neural networks (CNN), and remote sensing could be implemented in and used to improve the coffee supply chain. Those tools could help in reducing cost as well as time for entrepreneurs and create a reliable service for the customer. It can be summarized that in the long term, these modern technologies will be able to assist coffee business management and ensure the sustainable growth for the coffee industry.

Blockchain in operations for food service distribution: steps before implementation

Purpose

Considering the importance of a safe food chain for consumers and the advent of blockchain technology (BT), this research studies a food service (FS) distributor. The research aims to understand the implications related to the functional processes of distribution in FS in which it would be possible to use blockchain to achieve agility, transparency of information and improvements in food safety.

Design/methodology/approach

Firstly, theory regarding blockchain technology in the supply chain (BT-SC) and FS was analyzed to contextualize the theme conceptually. A single case study including 11 supply chain companies was applied in a BT implementation study in an FS distributor.

Findings

Investment in infrastructure is often identified as a barrier to adoption of BT-SC. This was, however, not found in this case. Furthermore, the validation of users was only necessary for those parties directly participating in the process or information input. Finally, findings differentiate between qualifying criteria and operational processes when considering BT projects in FS.

Research limitations/implications

The findings are restricted to this single case that provided an in-depth understanding of the topic. Statistical generalization is not possible at this stage of the research.

Practical implications

The study is a practical example and can provide several insights to anyone looking to implement BT in their SC.

Social implications

The social importance of the study lies in the importance of FS in the food sector, and by presenting ways that contribute to mitigating risks to consumers.

Originality/value

Real-life cases of application of BT-SC illustrate its functionalities in operational processes.

Big data for corporate social responsibility: blockchain use in Gioia del Colle DOP

The traceability of the supply chain with strict compliance with the specification to demonstrate in "transparency" the production processes in compliance with legislation and from a corporate social responsibility perspective, represents a fundamental requirement at the basis of competitive advantage in the food industries (Patelli and Mandrioli, J Food Sci 85: 3670-3678, 2020). The purpose of this work is to illustrate the innovative method for the certification and protection of the production phases of the DOP food chain and specifically the Mozzarella DOP of Gioia del Colle produced by the company Capurso Azienda Casearia Srl. This innovative approach consists of several phases that will be described in detail in the following paper. Besides, the idea of the introduction of Blockchain technology in an industry like this is an important step. This technology, associated with more accurate and intelligent management of the data acquisition process (Big data approach), optimizes the productivity of small businesses such as the dairy company. Blockchain technology guarantees security in the management of large amounts of data as never before possible, an innovative and experimental approach that makes the entire path of the production chain more controlled and optimized (Giacalone et al. International workshop on fuzzy logic and applications. Springer, Cham, pp. 218-225, 2016).

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.