In this paper, we present a method that facilitates Internet of Things (IoT) for building a product passport and data exchange enabling the next stage of the circular economy. SmartTags based on printed sensors (i.e., using functional ink) and a modified GS1 barcode standard enable unique identification of objects on a per item-level (including Fast-Moving Consumer Goods—FMCG), collecting, sensing, and reading of parameters from environment as well as tracking a products’ lifecycle. The developed ontology is the first effort to define a semantic model for dynamic sensors, including datamatrix and QR codes. The evaluation of decoding and readability of identifiers (QR codes) showed good performance for detection of sensor state printed over and outside the QR code data matrix, i.e., the recognition ability with image vision algorithm was possible. The evaluation of the decoding performance of the QR code data matrix printed with sensors was also efficient, i.e., the QR code ability to be decoded with the reader after reversible and irreversible process of ink (dis)appearing was preserved, with slight drop in performance if ink density is low.

This research describes the use of high-performance computing (HPC) and deep learning to create prediction models that could be deployed on edge AI devices equipped with camera and installed in poultry farms. The main idea is to leverage an existing IoT farming platform and use HPC offline to run deep learning to train the models for object detection and object segmentation, where the objects are chickens in images taken on farm. The models can be ported from HPC to edge AI devices to create a new type of computer vision kit to enhance the existing digital poultry farm platform. Such new sensors enable implementing functions such as counting chickens, detection of dead chickens, and even assessing their weight or detecting uneven growth. These functions combined with the monitoring of environmental parameters, could enable early disease detection and improve the decision-making process. The experiment focused on Faster R-CNN architectures and AutoML was used to identify the most suitable architecture for chicken detection and segmentation for the given dataset. For the selected architectures, further hyperparameter optimization was carried out and we achieved the accuracy of AP = 85%, AP50 = 98%, and AP75 = 96% for object detection and AP = 90%, AP50 = 98%, and AP75 = 96% for instance segmentation. These models were installed on edge AI devices and evaluated in the online mode on actual poultry farms. Initial results are promising, but further development of the dataset and improvements in prediction models is needed.

Blockchain offers decentralized, trustworthy and immutable data storage based on distributed ledger technology. Blockchain technology is recognized as an innovation enabler in many areas, with the food supply chain being one of them. This paper provides a systematic literature review of the current state of adoption of blockchain technology in the agri-food sector, specifically with a focus on the wine supply chain. Blockchain has the potential to improve the traceability and authenticity of the data provenance of wine products, increase consumer trust, and reduce fraud and errors. With these goals in mind, this study identifies the main research questions pertinent to the value proposition and competitive advantage of blockchain technology in the wine value chain and key players involved in the authentication and value chain recording. The PRISMA methodology was adopted to identify, screen and select only the relevant studies that were included in the analysis. This study also addresses the limitations and challenges for adoption, such as high implementation cost and lack of competences, and the need for standardized protocols and regulations. Finally, this systematic literature review includes an analysis of reports of blockchain applications in the wine sector and outlines the recommendations for future research to further explore the potential of blockchain-based solutions that could benefit all stakeholders across the wine value chain.

This research explores the role of synthetic data in enhancing the accuracy of deep learning models for automated poultry farm management. A hybrid dataset was created by combining real images of chickens with 400 FLUX.1 [dev] generated synthetic images, aiming to reduce reliance on extensive manual data collection. The YOLOv9 model was trained on various dataset compositions to assess the impact of synthetic data on detection performance. Additionally, automated annotation techniques utilizing Grounding DINO and SAM2 streamlined dataset labeling, significantly reducing manual effort. Experimental results demonstrate that models trained on a balanced combination of real and synthetic images performed comparably to those trained on larger, augmented datasets, confirming the effectiveness of synthetic data in improving model generalization. The best-performing model trained on 300 real and 100 synthetic images achieved mAP = 0.829, while models trained on 100 real and 300 synthetic images reached mAP = 0.820, highlighting the potential of generative AI to bridge data scarcity gaps in precision poultry farming. This study demonstrates that synthetic data can enhance AI-driven poultry monitoring and reduce the importance of collecting real data.

The chapter focuses on the importance of a strong ecosystem for successful and sustainable market presence in the Internet of Things world. It analyzes emerging value and business models together with the ecosystem structure and the roles of different stakeholders. The supply chain domain-related use cases, the ecosystem supporting them as well as ways of creating and expanding such ecosystems are given based on the experience of H2020 TagItSmart! project, which ran for over three years with more than 30 partners.

The evolution from the Edge-Cloud Continuum to the Cognitive Computing Continuum (CCC) has introduced new challenges which necessitate advanced frameworks that integrate cognitive capabilities to enhance interoperability, adaptability, and resource efficiency. Considering insights from ongoing research and initiatives on the cognitive cloud, we identify the core concepts essential for transitioning to the CCC, including cognitive orchestration, distributed AI, and sovereign data management. We then introduce ENACT, a novel framework designed to embrace these concepts aiming to provide cognitive, highly adaptive orchestration to support modern hyper-distributed and data-intensive applications. Furthermore, ENACT employs bespoke mechanisms to enable dynamic continuum modelling and visibility as well as to facilitate application-level automation and adaptability. This paper presents the motivation behind the ENACT approach, reviews the state-of-the-art across its fundamental technological concepts and highlights its key innovations. Overall, it contributes to the formalization of the CCC architectural principles and, ultimately, to the realization of CCC.