Search Results (25)

This comprehensive review synthesizes current advances and persistent challenges in integrating worker safety and food safety through human-robot collaboration (HRC) in poultry processing. Rapid industry expansion and rising consumer demand for ready-to-eat poultry products have heightened occupational risks and foodborne contamination concerns, necessitating holistic safety strategies. The review examines ergonomic, microbiological, and regulatory risks specific to poultry lines, and maps how state-of-the-art collaborative robots (“cobots”)—including power and force-limiting arms, adaptive soft grippers, machine vision, and biosensor integration—can support safer, more hygienic, and more productive operations. The authors analyze technical scientific literature (2018–2025) and real-world case studies, highlighting how automation (e.g., vision-guided deboning and intelligent sanitation) can reduce repetitive strain injuries, lower contamination rates, and improve production consistency. The review also addresses the psychological and sociocultural dimensions that affect workforce acceptance, as well as economic and regulatory barriers to adoption, particularly in small- and mid-sized plants. Key research gaps include gripper adaptability, validation of food safety outcomes in mixed human-cobot workflows, and the need for deeper workforce retraining and feedback mechanisms. The authors propose a multidisciplinary roadmap: harmonizing ergonomic, safety, and hygiene standards; developing adaptive food-grade robotic end-effectors; fostering explainable AI for process transparency; and advancing workforce education programs. Ultimately, successful HRC deployment in poultry processing will depend on continuous collaboration among industry, researchers, and regulatory authorities to ensure both safety and competitiveness in a rapidly evolving global food system. Full article

Background: In the era of rapid digital transformation, efficient food logistics (FL) is critical for sustainability and competitiveness. Maintaining food quality, minimizing waste, and optimizing costs are complex challenges that advanced digital technologies aim to address, particularly amid growing e-commerce and last-mile delivery demands. This underscores the need for a structured, quantitative evaluation of technological solutions to ensure operational reliability, efficiency, and sustainability. Methods: This study employs a Multi-Criteria Decision Making (MCDM) model combining Criterion Impact LOSs (CILOS) and Multi-Objective Optimization on the basis of Simple Ratio Analysis (MOOSRA) to evaluate key FL technologies: IoT, blockchain, Big Data analytics, automation and robotics, and cloud/edge computing. Nine evaluation criteria relevant to logistics providers were used, covering operational efficiency, flexibility, sustainability, food safety, data reliability, KPI support, scalability, costs, and implementation speed. CILOS determined criteria weights by considering interdependencies, and MOOSRA ranked technologies by benefits-to-costs ratios. Sensitivity analysis validated result robustness. Results: Automation and robotics ranked highest for enhancing efficiency, reducing errors, and improving handling and safety. Blockchain was second, supporting traceability and data security. Big Data analytics was third, enabling demand prediction and inventory optimization. IoT ranked fourth, providing real-time monitoring, while cloud/edge computing ranked fifth due to indirect operational impact. Conclusions: The CILOS–MOOSRA model enables transparent, structured evaluation, integrating quantitative metrics with logistics providers’ priorities. Results highlight technologies that enhance efficiency, reliability, and sustainability while revealing integration challenges, providing a strategic foundation for digital transformation in FL. Full article

Shellfish aquaculture is considered a major pillar of the seafood industry for its high market value, which increases the value for global food security and sustainability, often constrained in terms of conventional, labor-intensive practices. This review outlines the importance of automation and its advances in the shellfish value chain, starting from the hatchery operations to harvesting, processing, traceability, and logistics. Emerging technologies such as imaging, computer vision, artificial intelligence, robotics, IoT, blockchain, and RFID provide a major impact in transforming the shellfish sector by improving the efficiency, reducing the labor costs and environmental impacts, enhancing the food safety, and providing transparency throughout the supply chain. The studies involving the bivalves and crustaceans on their automated feeding, harvesting, grading, depuration, non-destructive quality assessments, and smart monitoring in transportation are highlighted in this review to address concerns involved with conventional practices. The review puts forth the need for integrating automated technologies into farm management and post-harvest operations to scale shellfish aquaculture sustainably, meeting the rising global demand while aligning with the Sustainability Development Goals (SDGs). Full article

Ensuring food safety in fresh-cut vegetables is essential due to the frequent presence of foreign material (FM) that threatens consumer health and product quality. This study presents a real-time FM detection system developed using the YOLO object detection framework to accurately identify diverse FM types in cabbage and green onions. A custom dataset of 14 FM categories—covering various shapes, sizes, and colors—was used to train six YOLO variants. Among them, YOLOv7x demonstrated the highest overall accuracy, effectively detecting challenging objects such as transparent plastic, small stones, and insects. The system, integrated with a conveyor-based inspection setup and a Python graphical interface, maintained stable and high detection accuracy confirming its robustness for real-time inspection. These results validate the developed system as an alternative intelligent quality-control layer for continuous, automated inspection in fresh-cut vegetable processing lines, and establish a solid foundation for future robotic-based removal systems aimed at fully automated food safety assurance. Full article

Artificial Intelligence (AI) is a key technological enabler for the transition of agricultural production and management from experience-driven to data-driven, continuously advancing modern agriculture toward smart agriculture. This evolution ultimately aims to achieve a precise agricultural production model characterized by low resource consumption, high safety, high quality, high yield, and stable, sustainable development. Although machine learning, deep learning, computer vision, Internet of Things, and other AI technologies have made significant progress in numerous agricultural production applications, most studies focus on singular agricultural scenarios or specific AI algorithm research, such as object detection, navigation, agricultural machinery maintenance, and food safety, resulting in relatively limited coverage. To comprehensively elucidate the applications of AI in agriculture and provide a valuable reference for practitioners and policymakers, this paper reviews relevant research by investigating the entire agricultural production process—including planting, management, and harvesting—covering application scenarios such as seed selection during the cultivation phase, pest and disease identification and intelligent management during the growth phase, and agricultural product grading during the harvest phase, as well as agricultural machinery and devices like fault diagnosis and predictive maintenance of agricultural equipment, agricultural robots, and the agricultural Internet of Things. It first analyzes the fundamental principles and potential advantages of typical AI technologies, followed by a systematic and in-depth review of the latest progress in applying these core technologies to smart agriculture. The challenges faced by existing technologies are also explored, such as the inherent limitations of AI models—including poor generalization capability, low interpretability, and insufficient real-time performance—as well as the complex agricultural operating environments that result in multi-source, heterogeneous, and low-quality, unevenly annotated data. Furthermore, future research directions are discussed, such as lightweight network models, transfer learning, embodied intelligent agricultural robots, multimodal perception technologies, and large language models for agriculture. The aim is to provide meaningful insights for both theoretical research and practical applications of AI technologies in agriculture. Full article

Smart devices and emerging technologies are highly popular devices and technologies that considerably improve our daily living by reducing or replacing human workforces, treating disease, monitoring healthcare, enhancing service performance, improving quality, and protecting the natural environment, and promoting non-gas emissions, sustainable working, green technologies, and renewable energy. Triboelectric nanogenerators (TENGs) have recently emerged as a type of advanced energy harvesting technology that is simple, green, renewable, flexible, and endurable as an energy resource. High-performance TENGs, denoted as advanced TENGs, have potential for use in many practical applications such as in self-powered sensors and sources, portable electric devices, power grid penetration, monitoring manufacturing processes for quality control, and in medical and healthcare applications that meet the criteria for smart devices and emerging technologies. Advanced TENGs are used as highly efficient energy harvesters that can convert many types of wasted mechanical energy into the electric energy used in a range of practical applications in our daily lives. This article reviews recently advanced TENGs and their potential for use with smart devices and emerging technology applications. The work encourages and strengthens motivation to develop new smart devices and emerging technologies to serve us in many fields of our daily living. When TENGs are introduced into smart devices and emerging technologies, they can be applied in a variety of practical applications such as the food processing industry, information and communication technology, agriculture, construction, transportation, marine technology, the energy sector, mechanical processing, manufacturing, self-powered sensors, Industry 4.0, drug safety, and robotics due to their sustainable and renewable energy, light weight, cost effectiveness, flexibility, and self-powered portable energy sources. Their advantages, disadvantages, and solutions are also discussed for further research. Full article

Effective environmental monitoring is critical for preventing microbial and allergenic cross-contamination. However, manual swabbing methods, commonly used to verify hygienic conditions, are prone to inconsistent results because of variability in pressure, coverage, and techniques. Two novel solutions will be explored to address these challenges: a robotic swabbing system with tactile sensing control, and a fluorescence/absorbance spectrometer for non-contact, protein-based residue detection. The robotic system was evaluated against trained and untrained humans, measuring water pickup, surface coverage, and pressure consistency. Concurrently, the fluorescence system analyzed model poultry protein soil to correlate spectral patterns with contamination levels. The robotic system demonstrated statistically superior performance, achieving consistent force application and near-complete surface coverage, overcoming key limitations of manual sampling. The fluorescence system distinguished contamination with high sensitivity, validating its use as a rapid, non-contact assessment tool. Together, the robotic sample acquisition and the spectrometer’s sensitive analysis provide a dual-modality framework for enhancing hygiene monitoring in manufacturing facilities. Full article

In recent years, there have been significant advances in the application of technology in professional kitchens. This evolution of “smart kitchens” has transformed the food processing sector, ensuring higher standards of food safety through continual microbial monitoring, quality control, and hygiene improvements. This review critically discusses the recent developments in technology in commercial kitchens, focusing on their impact on microbial safety, operational efficiency, and sustainability. The literature was sourced from peer-reviewed journals, industry publications, and regulatory documents published between 2000 and 2025, selected for their relevance to the assurance of food safety using emerging technologies especially for use in commercial kitchens. Some of the most significant of these technologies currently being employed in smart kitchens include the following: smart sensors and IoT devices, artificial intelligence and machine learning systems, blockchain-based traceability technology, robotics and automation, and wearable monitoring devices. The review evaluated these technologies against criteria such as adherence to existing food safety regulations, ease of integration, cost factors, staff training requirements, and consumer perception. It is shown that these innovations will significantly enhance hygiene control, reduce the levels of waste, and increase business revenue. However, they are constrained by high installation costs, integration complexity, lack of standardized assessment measures, and the need for harmonizing automation with human oversight. Thus, for the widespread and effective uptake of these technologies, there is a need for better collaboration between policymakers, food experts, and technology innovators in creating scalable, affordable, and regulation-compliant solutions. Overall, this review provides a consolidated evidence base and practical insights for stakeholders seeking to implement advanced microbial safety technologies in professional kitchens, highlighting both current capabilities and future research opportunities. Full article

The article examines the application of collaborative robots (cobots) as a modern engineering tool for the transformation of the food industry following the principles of Industry 5.0. A conceptual engineering model has been developed that integrates collaborative robots with IoT systems, digital twins, and predictive analytics to increase the flexibility, safety, and sustainability of production processes. The proposed model is validated through a practical case study focused on a yogurt packaging line in the dairy sector, where cobot systems demonstrate a significant improvement in operational efficiency and process safety. A step-by-step strategic roadmap is presented to guide industrial enterprises through the various stages of implementation, from the initial assessment to the full-scale integration of solutions. Additionally, a comparative analysis has been performed between traditional automated systems and the integrated approach with collaborative robots, which highlights the technological, economic, and human-oriented advantages of the latter. The results of the study confirm that collaborative robotics offers an effective and applicable path for transforming the food and beverage industry towards a sustainable, adaptive, and human-centered manufacturing ecosystem characteristic of Industry 5.0. Full article

Collaborative robotics and computer vision systems are increasingly important in automating complex industrial tasks with greater safety and productivity. This work presents an integrated vision system powered by a trained neural network and coupled with a collaborative robot for real-time sorting and quality inspection in a food product conveyor process. Multiple object detection models were trained on custom datasets using advanced augmentation techniques to optimize performance. The proposed system achieved a detection and classification accuracy of 98%, successfully processing more than 600 items with high efficiency and low computational cost. Unlike conventional solutions that rely on ROS (Robot Operating System), this implementation used a Windows-based Python framework for greater accessibility and industrial compatibility. The results demonstrated the reliability and industrial applicability of the solution, offering a scalable and accurate methodology that can be adapted to various industrial applications. Full article

The dairy supply chain encompasses all stages involved in the production, processing, distribution, and delivery of dairy products from farms to end consumers. Artificial intelligence (AI) refers to the use of advanced technologies to optimize processes and make informed decisions. Using the PRISMA methodology, this research analyzes AI technologies applied in the dairy supply chain, their impact on process optimization, the factors facilitating or hindering their adoption, and their potential to enhance sustainability and operational efficiency. The findings show that artificial intelligence (AI) is transforming dairy supply chain management through technologies such as artificial neural networks, deep learning, IoT sensors, and blockchain. These tools enable real-time planning and decision-making optimization, improve product quality and safety, and ensure traceability. The use of machine learning algorithms, such as Tabu Search, ACO, and SARIMA, is highlighted for predicting production, managing inventories, and optimizing logistics. Additionally, AI fosters sustainability by reducing environmental impact through more responsible farming practices and process automation, such as robotic milking. However, its adoption faces barriers such as high costs, lack of infrastructure, and technical training, particularly in small businesses. Despite these challenges, AI drives operational efficiency, strengthens food safety, and supports the transition toward a more sustainable and resilient supply chain. It is important to note that the study has limitations in analyzing long-term impacts, stakeholder resistance, and the lack of comparative studies on the effectiveness of different AI approaches. Full article

This study investigated the application of robotic automation in food manufacturing, focusing on enhancing tray transporting operations through a simulation-based approach. The findings primarily focused on bakery production but also demonstrate broader applicability to other sectors that involve repetitive and labor-intensive tasks. The researchers analyzed worker fatigue and limited productivity associated with manual tray handling. To evaluate these issues, simulations were conducted for two scenarios (Case A and Case B), applying robotic automation systems at different stages of production. Key performance indicators (throughput and utilization rates) were analyzed to assess improvements in process efficiency and reductions in worker strain. The results showed that robotic automation significantly increased throughput by 83.7% in simpler processes and by 27.1% in more complex ones, highlighting the impact of task complexity on automation effectiveness. Workforce demands decreased and demonstrated the potential of automation to alleviate physical strain in repetitive tasks. Simulations provided insights into workflow optimization, confirming their value as reliable tools for planning and refining automation strategies. The proposed framework offers a flexible and scalable solution for enhancing efficiency and consistency in manufacturing. Future research should apply similar approaches to other industries and explore the integration of human and robotic labor to further optimize safety, productivity, and cost effectiveness. Full article

Neuromorphic computing has received more and more attention recently since it can process information and interact with the world like the human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, and consumption. Smart devices in association with artificial intelligence (AI) robots and Internet of Things (IoT) systems have been used and also need to be improved to accommodate the growth of computing. Neuromorphic computing has a great potential to promote the development of smart agriculture. The aim of this paper is to describe the current principles and development of the neuromorphic computing technology, explore the potential examples of neuromorphic computing applications in smart agriculture, and consider the future development route of the neuromorphic computing in smart agriculture. Neuromorphic computing includes artificial synapses, artificial neurons, and artificial neural networks (ANNs). A neuromorphic computing system is expected to improve the agricultural production efficiency and ensure the food quality and safety for human nutrition and health in smart agriculture in the future. Full article

This paper delineates the design and realization of a Wheelchair-Mounted Robotic Arm (WMRA), envisioned as an autonomous assistance apparatus for individuals encountering motor difficulties and/or upper limb paralysis. The proposed design solution is based on employing a 3D printing process coupled with optimization design techniques to achieve a cost-oriented and user-friendly solution. The proposed design is based on utilizing commercial Arduino control hardware. The proposed device has been named Pick&Eat. The proposed device embodies reliability, functionality, and cost-effectiveness, and features a modular structure housing a 4-degrees-of-freedom robotic arm with a fixing frame that can be attached to commercial wheelchairs. The arm is integrated with an interchangeable end-effector facilitating the use of various tools such as spoons or forks tailored to different food types. Electrical and sensor components were meticulously designed, incorporating sensors to ensure user safety throughout operations. Smooth and secure operations are achieved through a sequential procedure that is depicted in a specific flowchart. Experimental tests have been carried out to demonstrate the engineering feasibility and effectiveness of the proposed design solution as an innovative assistive solution for individuals grappling with upper limb impairment. Its capacity to aid patients during the eating process holds promise for enhancing their quality of life, particularly among the elderly and those with disabilities. Full article

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. Full article