Journal of Industrial Information Integration最新文献

{"title":"A review on machine learning and deep learning techniques for plant leaf disease detection and classification with IoT in agriculture industry","authors":"Priyadharshini Arputharaj,&nbsp;Kalaivanan Karunanithy","doi":"10.1016/j.jii.2026.101078","DOIUrl":"10.1016/j.jii.2026.101078","url":null,"abstract":"<div><div>Agriculture serves as a major source of food and plays a key function as the backbone of most countries’ economies. However, farmers are encountering many challenges in this sector, such as drought, flooding, diseases, nutrient deficiency, and so on. The technological advancements in the field of agriculture, also called smart agriculture, are necessary to address the requirements of the expanding population and manage the associated challenges. Among those, plant leaf diseases are the primary concern that severely impacts crop yield and economic stability. This technical review examines various Machine Learning (ML) and Deep Learning (DL) approaches used to identify and classify different plant leaf diseases. This review gives an overview of the current state-of-the-art ML, DL, and IoT-enabled disease prediction systems and their recent advances in developing an intelligent system in smart agriculture. It provides insights into the various technological developments and discusses the benefits and opportunities of AI-based models in plant disease management.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101078"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-layered data service model for Cyber-Physical Production Networks","authors":"Ada Bagozi,&nbsp;Devis Bianchini,&nbsp;Massimiliano Garda,&nbsp;Michele Melchiori,&nbsp;Anisa Rula","doi":"10.1016/j.jii.2025.101043","DOIUrl":"10.1016/j.jii.2025.101043","url":null,"abstract":"<div><div>In modern smart factories, supply chains are no longer isolated; instead, they are evolving into interconnected and dynamic networks, where intertwined supply chains enable real-time collaboration and data sharing for adaptive decision-making across multiple stakeholders. By harnessing data from sensors and connected devices, data-driven decisions can be made to optimize the entire supply chain, and to provide novel and customer-friendly products and services. Cyber-Physical Systems form the foundation of Cyber-Physical Production Systems (CPPS) by enabling real-time data exchange and intelligent automation at the factory level, while horizontal integration connects CPPS across different production facilities to enhance supply chain coordination, thus forming the so-called Cyber-Physical Production Networks (CPPN). In CPPN, the Internet of Services (IoS) paradigm, in combination with the Internet of Things (IoT), plays a crucial role in facilitating horizontal integration and seamless collaboration between intertwined supply chains. Since the IoS paradigm has to enable data sharing and processing within individual smart factories and across factory borders, there is a need to design service-oriented architectures specifically tailored to data governance in both CPPS and CPPN. However, existing service-oriented approaches for CPPS primarily focus on deployment layers (e.g., fog/edge computing or IT/production levels) while neglecting data-oriented aspects, limiting modularity and effective data service design across CPPS and CPPN levels. To bridge this gap, in this paper, we propose a multi-layered service-oriented model for CPPN focused on data services, which includes atomic services for data collection and processing, and composite services for governing the data flow within smart factories and throughout the supply chains they participate in. One of the significant advantages of the multi-layered approach is a clear separation of concerns in service design, with the ability to address issues of modularity, scalability, data sovereignty and data access, by distinguishing between CPPS and CPPN levels. In the paper, we critically evaluate different strategies for the management of a service ecosystem that is compliant with the proposed model.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101043"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-source domain adaptation via evidence-based target pseudo-labels for human–computer collaboration fault diagnosis","authors":"Jilun Tian ,&nbsp;Hao Luo ,&nbsp;Pengfei Yan ,&nbsp;Xinyu Qiao ,&nbsp;Shimeng Wu ,&nbsp;Jiusi Zhang","doi":"10.1016/j.jii.2026.101077","DOIUrl":"10.1016/j.jii.2026.101077","url":null,"abstract":"<div><div>Existing data-driven fault diagnosis methods imply the decision-making automatically, but lack adaptation and trustworthiness for varying working conditions. Unsupervised domain adaptation (UDA) relies on the cross-domain distribution disparity to achieve high-performance diagnostics. However, it struggles in complex multi-domain and diverse-source scenarios, which currently lack in-depth analysis. The proposed approach implements a novel multi-source domain adversarial network (MSDA) architecture via evidence-based target pseudo-label learning (ETPL) with dynamic multi-loss weightings. Specifically, MSDA constrains the disparity of diverse source–target pairs to obtain generalized domain-invariant features via an adversarial mechanism, and ETPL performs target pseudo-label learning while applying Dempster–Shafer (DS) evidence theory to assign sample-wise weights through MSDA and an unsupervised algorithm. Meanwhile, this study provides a theoretical analysis including a detailed generalization error bound for multi-source scenarios and target pseudo-labels, illustrating its dependence on distribution discrepancy and pseudo-label quality metrics. Human–computer collaboration approach is adopted to strengthen both advantages from human and machines by sample-wise analysis. Sufficient experimental results on two real-world case studies validate the effectiveness, successfully accomplishing complex cross-domain fault diagnosis and illustrating its potential applications in industrial settings.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101077"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A knowledge-driven decision support architecture for sustainable supplier analysis in an infrastructure project","authors":"Song-Shun Lin ,&nbsp;Xin-Jiang Zheng ,&nbsp;Zhao-Yao Bao","doi":"10.1016/j.jii.2025.100994","DOIUrl":"10.1016/j.jii.2025.100994","url":null,"abstract":"<div><div>As supplier analysis becomes increasingly complex, there is a growing need for structured methods that support multi-dimensional evaluation under uncertainty. A knowledge-driven decision support approach (KDDSA) is proposed, leveraging entropy-based interval-valued spherical fuzzy sets to assign criteria weights. Additionally, a weighted coefficient of variation is introduced to measure consensus and account for variability in judgments, strengthening decision-making reliability. The proposed approach addresses the practical challenges of integrating multiple, often conflicting criteria in supplier analysis by incorporating economic, environmental, social, and supplier-specific dimensions, structured across sixteen indicators. To assess its practical applicability, KDDSA is applied to an infrastructure project, where uncertain assessments are integrated and processed through a multi-layered decision structure. The results highlight the critical importance of consensus building and uncertainty management for achieving reliable outcomes. By integrating heterogeneous information with advanced fuzzy modeling, the proposed approach enhances industrial information integration in complex decision-making contexts. The findings reinforce the potential of structured and information-integrated evaluation methods in enhancing supplier management within infrastructure supply chains.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 100994"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble-based ship weather multi-objective route optimization","authors":"Kumars Mahmoodi ,&nbsp;Jari Böling ,&nbsp;Roberto Vettor","doi":"10.1016/j.jii.2026.101075","DOIUrl":"10.1016/j.jii.2026.101075","url":null,"abstract":"<div><div>Many traditional and state-of-the-art ship routing methods rely on single-objective formulations, deterministic weather inputs, or fixed operational assumptions, which may lead to suboptimal or impractical routing decisions under realistic and uncertain marine environments. This study presents an ensemble-based multi-objective optimization framework for ship route planning under uncertain weather conditions. The framework integrates a neural network model, trained on real onboard ship performance data and tuned using Bayesian hyperparameter optimization, to predict fuel consumption based on ship speed and marine weather parameters. An ensemble of weather forecasts is assigned to route waypoints using a bootstrapping method, enabling the evaluation of multiple cost functions reflecting trade-offs between voyage time, fuel consumption, and safety. Four optimization objective strategies — ensemble mean, worst-case, risk-aware, and Conditional Value-at-Risk (CVaR) — are implemented within a Grey Wolf Optimizer (GWO) to derive optimal routes across various voyages. The results demonstrate notable variations in route performance based on the selected strategy. For example, the CVaR approach achieves a balance between robustness and efficiency, with voyage fuel consumption for the longest journey (Voyage 3) reaching 490,475 kg, while the worst-case strategy prioritizes risk-averse paths, resulting in the highest fuel usage at 505,308 kg. Conversely, the ensemble mean strategy offers the lowest average fuel consumption (474,078 kg) but may expose the voyage to higher uncertainty. Furthermore, the proposed GWO demonstrates high precision in schedule adherence, maintaining arrival time deviations within a 30-minute margin across all optimized voyages, thereby justifying its effectiveness in handling complex multi-objective constraints. The developed framework is applicable to real-time voyage optimization and can support ship operators in achieving fuel efficiency and safety under varying ocean conditions.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101075"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information scene-augmented mapping for smart bearing whole life cycle digital twin","authors":"Zixian Li ,&nbsp;Hebin Zheng ,&nbsp;Shenlan Liu ,&nbsp;Wenbin Huang ,&nbsp;Xiaoxi Ding ,&nbsp;Xiaohui Chen","doi":"10.1016/j.jii.2025.101021","DOIUrl":"10.1016/j.jii.2025.101021","url":null,"abstract":"<div><div>Benefiting from the digitization of mechanical equipment, the digital twin of smart bearing can better realize the whole life cycle intelligent operation and maintenance of mechanical equipment, where the twin data are normally utilized to realize the state mapping with the identification or prediction model. Whereas, this process is mostly single interaction, and the dynamic update of the twin model and mapping results is not considered, and this makes its real application difficult. Focusing on this issue, an information scene-augmented mapping method (ISAM) is proposed for the smart bearing whole life cycle digital twin, so as to realize the accurate dynamic interaction of virtual-real scene in the twinning process. Different from the conventional digital twin models, ISAM creates an state mapping method that can dynamically update real state and simulation parameters, and it simultaneously enhances the scenario self-consistency ability based on information scene augment. First, a physical information and prior-knowledge driven feature parameter matching network (PK-FPMN) was constructed, and the actual fault size can be dynamically matched by the measured data and the dynamic model. This will realize the virtual-real scene interaction of the digital twin. Considering the difference between the twin data and the actual data, progressive style cyclic enhancement network (PSCEN) model is then introduced in the parameter matching process. By transferring the style information of the measured information scene to the twin data, the self-consistency ability of the method in different application scenarios is improved. Finally, ISAM combines the physical entity and dynamic model to form a whole life cycle digital twin of smart bearing. And the mapped degradation state and twin data can be operated for state identification and degradation prediction. Experimental results demonstrate that the ISAM can accurately map the actual degradation state and improve the quality of twin data based on the real information scene. With virtual scene and real scene interacted, the degradation state and twin data can be used for accurately state identification and degradation prediction. It can be foreseen that the proposed ISAM for smart bearing has the potential to realize the intelligent operation and maintenance of mechanical equipment in actual industrial digitization scenarios.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101021"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145650849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Modeling and Analysis of Power Grid Cyber-Physical Systems: Application and Validation of HM-GCPS","authors":"Yang-Rong Chen ,&nbsp;Jun-E Li ,&nbsp;Jie Zhang ,&nbsp;Yu-Fei Wang ,&nbsp;Ting Zhao","doi":"10.1016/j.jii.2025.101042","DOIUrl":"10.1016/j.jii.2025.101042","url":null,"abstract":"<div><div>This paper is the second part of a two-part series. In Part Ⅰ, the hierarchical model (i.e., HM-GCPS) of power grid cyber-physical system (GCPS) is established. Part II presents the application method of HM-GCPS in Part I used to solve a specific problem, and verifies the effectiveness of HM-GCPS by an example. First, a modeling and analysis architecture, describing the problems that can be analyzed by HM-GCPS, is proposed. To verify the feasibility and effectiveness of HM-GCPS, the risk propagation analysis process of GCPS cyber space is presented based on HM-GCPS. Then, taking a provincial power dispatch data network (PDDN) as an example, the failure event chains under five cyber-attack scenarios are analyzed and deduced. These five scenarios target all layers of the GCPS cyber space. Meanwhile, the impacts of the five cyber-attack scenarios on GCPS communication services are simulated. The results show that HM-GCPS is feasible and effective when it is used for analyzing the risk propagation of GCPS cyber space.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101042"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PV-EIR: An embodied intelligent robot for solar panel cleaning in desert regions","authors":"Xinhao Wang ,&nbsp;Lin Yang ,&nbsp;Daqiang Zhang","doi":"10.1016/j.jii.2026.101069","DOIUrl":"10.1016/j.jii.2026.101069","url":null,"abstract":"<div><div>To address the cleaning challenges faced by robots in the extreme conditions of desert photovoltaic power plants, this study proposes a photovoltaic embodied intelligence (EI) robot based on bionic brain-cerebellum coordination control. First, domain knowledge integration is used for multi-objective task planning and interpretable strategy generation, and a dedicated decision-making brain model (PV-LLM) is constructed for desert PV scenarios. Then, a PV-Cerebellum module is designed through simulation-reality dataset learning, thereby ensuring efficient mapping and robust control from abstract tasks to specific skill library invocations. Finally, the two core skills of the proposed robot, real-time obstacle avoidance navigation and panel alignment cleaning, are validated via simulation tests in the Gazebo simulation environment under desert conditions. Experimental results demonstrate that the proposed robotic system can achieve obstacle-avoidance response times as low as 0.36 s and cleaning coverage rates up to 96.67 %, validating its engineering feasibility. Overall, this work provides a new practical solution for reliable photovoltaic panel cleaning in harsh desert environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101069"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From cognitive to circular: A Digital Twin systematic review","authors":"Jairo Francisco de Souza ,&nbsp;Fabrício Martins Mendonça ,&nbsp;António José Baptista ,&nbsp;António Lucas Soares ,&nbsp;Jorão Gomes Jr.","doi":"10.1016/j.jii.2025.101051","DOIUrl":"10.1016/j.jii.2025.101051","url":null,"abstract":"<div><div>This paper aims to clarify the characteristics of Digital Twins (DTs) in their most advanced conceptual development, Cognitive Digital Twins (CDTs), and analyze their support for the implementation of the Circular Economy (CE). A systematic literature review was conducted using a specially developed five-dimensional analytical framework to characterize DT proposals and their potential for CE based on an established framework for circularity strategies. The study indicates that cognitive and hybrid DT approaches tend to cover high levels of interoperability, data flow, system levels, and cognitive processes. However, CDT use in CE demands harmonizing different strategies to cover the complete product lifecycle, which recent research on DTs has not fully addressed. This study is the first to systematically review cognitive digital twins and their relation to circularity, offering an analytical framework that can be expanded for future research in various application areas of Industry 5.0.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101051"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging model-based systems and software engineering for digital twin engineering: Methods and digital thread opportunities","authors":"Clarissa Gregory ,&nbsp;Souad Rabah ,&nbsp;Vincent Chapurlat ,&nbsp;Fedor Burčiar ,&nbsp;Monika Herchlová","doi":"10.1016/j.jii.2025.101023","DOIUrl":"10.1016/j.jii.2025.101023","url":null,"abstract":"<div><div>The Digital Twin (DT) is a reciprocally connected and synchronized representation of a physical asset so-called Physical Twin. DT is a central component of a Digital Twin System (DT System), that interconnects the DT and the Physical Twin, through different components, and executes the necessary functions such as exchanging, processing or storing data. The engineering of the DT System is a long and complex process which is poorly formalised by systematic methodological approaches despite the variety of techniques and technologies used by practitioners. Fittingly, Systems Engineering (SE) offers principles, processes, methodological tools and frameworks for the engineering and lifecycle management of complex systems. More precisely, Model-Based Systems and Software Engineering (MBSSE) places modelling activities at the centre of engineering practices to facilitate exchanges and iterations between stakeholders around the system of interest through its whole lifecycle. Stakeholders’ activities during system’s lifecycle produce digital items, perceived as Data, Information or Knowledge (DIK), which may be reused for DT System engineering. First, this paper discusses SE and MBSSE application for DT System engineering to identify the foundations of a DT System MBSSE. Second, a discussion about opportunities and research questions are presented to gather and manage items produced along lifecycle in a concept designated the Digital Thread. Indeed, Digital thread formalisation and exploitation lead various interests for DT System engineering.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"50 ","pages":"Article 101023"},"PeriodicalIF":10.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}