Ruchire Eranga Wijesinghe

The release of microplastics (MPs) from nylon tea bags poses a critical concern for human exposure; however, their detection and quantification remain challenging especially in beverage matrices, and hence, this study pioneers the use of high-resolution optical coherence tomography (OCT) integrated with an image processing algorithm to rapidly detect and quantify the size and count of the MPs directly in the water extractions simulating tea brewing. The water extractions prepared by simulating tea brewing conditions, hot (100 °C, 1–5 min), cold (2 °C, 1 h), and ambient (30 °C, 1 h), were observed employing OCT imaging and validated through Nile Red (NR) staining and digital microscopy. The nylon tea bags steeped in hot water for 5 minutes released 16 000 to 24 000 LMPs (>30 µm) and SMPs (12–30 µm) per millilitre. The estimated daily intake (EDI) of MPs indicates a higher exposure for children (ranging from 0.201 to 0.349 mm³ kg⁻¹ day⁻¹) compared to adults (0.046 to 0.080 mm³ kg⁻¹ day⁻¹). In contrast, cold brewing for 1 hour released fewer LMPs but an equal quantity of small MPs (SMPs) compared to hot brewing. This OCT-based approach offers a rapid, versatile platform for the detection and quantification of MPs from diverse packaging materials and provides a powerful tool for comprehensive risk assessment when combined with chemical and toxicological analyses.

Non‐contact, high‐resolution imaging is critical in modern industrial environments, where complex and multilayered structures demand precise inspection without disrupting production. Optical coherence tomography (OCT), a non‐destructive, depth‐resolved imaging modality, has emerged as a transformative tool for real‐time visualization of internal features in advanced manufacturing processes. This review begins with the principles of OCT and provides a comprehensive analysis of its applications across key manufacturing sectors, including display inspection, thin‐film monitoring, microelectronics, printed circuit board (PCB) analysis, laser‐based manufacturing, and coating evaluation. For each domain, representative methods are examined for their effectiveness in detecting subsurface defects, measuring internal geometries, and supporting quality control. The role of OCT in industry is highlighted through comparisons with conventional inspection techniques. In addition, the review discusses current challenges across resolution, optical penetration and contrast, signal quality, system integration, hardware cost, throughput, and artificial intelligence (AI) and data infrastructure. It also highlights emerging advances in adaptive optics, multimodal and AI‐enhanced imaging, compact and portable OCT platforms, and next‐generation computational and data‐processing architectures for real‐time industrial deployment. By combining technical evaluation, comparative analysis, and future‐oriented perspectives, this review positions OCT as a core technology and foundational tool for next‐generation quality assurance in smart manufacturing and Industry 4.0 environments.

Plant disease has a tremendous impact on global food security, and bitter rot caused by Colletotrichum spp. is a greater challenge to post-harvest quality. Conventional diagnosis is precise but invasive and therefore inappropriate for real-time purposes. This study investigates optical coherence tomography (OCT) as a high-resolution, non-invasive imaging method to detect internal structural changes from disease progression. The developed OCT-based image analysis framework stages diseases by assessing morphological degradation. The discovery of unique oval-shaped internal features, invisible to other non-invasive methods, demonstrates OCT’s potential for early detection, accurate monitoring, and real-time application in precision agriculture.

This study presents a real-time system for detecting sheath damage in multicore wire production, addressing the limitations of conventional high-voltage testing. After evaluating multiple sensing techniques, laser-diffused reflection emerged as the most reliable, non-intrusive, and effective for continuous monitoring. A calibrated system combining a laser source and photodetector was implemented and tested on a live production line, achieving 83.75% detection accuracy. The system maintained consistent performance across wire colors and insulation conditions. This advancement offers a safer, more efficient alternative for in-process quality control. Future work aims to enhance robustness and incorporate intelligent algorithms to further optimize detection accuracy.

Strawberries, a high-value crop with growing demand, face increasing challenges from labour shortages, declining pollinator populations, and the limitations of inconsistent manual pollination. This paper presents an IoT-enabled robotic system designed to automate strawberry pollination in open-field raised-bed environments with minimal human intervention. The system consists of a mobile rover equipped with an ESP32-CAM for image capture and a robotic arm mounted on an Arduino Uno, capable of controlled X, Y, and Z positioning to perform targeted pollination. Images of strawberry beds are transmitted to a locally deployed server, which uses a lightweight detection model to identify flowers. System components communicate asynchronously via HTTP and I2C protocols, and the onboard event-driven architecture enables responsive behaviour while minimizing RAM and power usage, which is an essential requirement for low-cost, field-deployable robotics. The server also manages multi-rover scheduling through a custom priority queue designed for low-end hardware. In controlled lo0ad tests, the scheduler improved average response time by 6.9% and handled 2.4% more requests compared to the default queueing system, while maintaining stability. Preliminary field tests demonstrate successful flower identification and reliable arm positioning under real-world conditions. Although full system yield measurements are ongoing, current results validate the core design’s functional feasibility. Unlike previous systems that focus on greenhouse deployments or simpler navigation approaches, this work emphasizes modularity, affordability, and adaptability for small and medium farms, particularly in resource-constrained agricultural regions such as Sri Lanka. This study presents a promising step toward autonomous and scalable pollination systems that integrate embedded systems, robotics, and IoT for practical use in precision agriculture.

A vision-based autonomous system for emasculating okra enhances agriculture by enabling precise flower bud identification, overcoming the labor-intensive, error-prone challenges of traditional manual methods with improved accuracy and efficiency. This study presents a framework for an adaptive, automated bud identification method to assist the emasculation process, hybridized optical coherence tomography (OCT). Three YOLOv8 variants were evaluated for accuracy, detection speed, and frame rate to identify the most efficient model. To strengthen the findings, YOLO was hybridized with OCT, enabling non-invasive sub-surface verification and precise quantification of the emasculated depth of both sepal and petal layers of the flower bud. To establish a solid benchmark, gold standard color histograms and a digital imaging-based method under optimal lighting conditions with confidence scoring were also employed. The results demonstrated that the proposed method significantly outperformed these conventional frameworks, providing superior accuracy and layer differentiation during emasculation. Hence, the developed YOLOv8 hybridized OCT method for flower bud identification and emasculation offers a powerful tool to significantly improve both the precision and efficiency of crop breeding practices. This framework sets the stage for implementing scalable, artificial intelligence (AI)-driven strategies that can modernize and optimize traditional crop breeding workflows.

In human-centered robotic applications, safety, efficiency, and adaptability are critical for enabling effective interaction and performance. Incorporating electromagnetic continuously variable transmission (EM-CVT) systems into robotic designs enhances both safety and precise, adaptable motion control. The flexible power transmission offered by CVTs allows robots to operate across diverse environments, supporting various tasks, human interaction, and safe collaboration. This study presents a CVT-based mechanical subsystem developed using two cones and an intermediate belt-driven transmission mechanism, providing efficient power and motion transfer. The control subsystem consists of six strategically positioned electromagnets energized by signals from a microcontroller. This electromagnetic actuation enables rapid and precise adjustments to the transmission ratio, enhancing overall system performance. A linear relationship between slip percentage and gear ratio was observed, indicating that the control system achieves stable and efficient operation, with a measured power consumption of 2.95 W per electromagnet. Future work will focus on validating slip performance under dynamic loading conditions, integrating the system into robotic platforms, and optimizing materials and control strategies to enable broader real-world deployment.

The Printed Circuit Board (PCB) manufacturing industry is a rapidly expanding sector, fueled by advanced technologies and precision-oriented production processes. The placement of Surface-Mount Device (SMD) components in PCB assembly is efficiently automated using robots and design software-generated coordinate files; however, the PCB repair process remains significantly more complex and challenging. Repairing faulty PCBs, particularly replacing defective SMD components, requires high precision and significant manual expertise, making automated solutions both rare and difficult to implement. This study introduces a novel real-time machine vision-based coordinate estimation system designed for estimating the coordinates of SMD components during soldering or desoldering tasks. The system was specifically designed for Selective Compliance Articulated Robot Arm (SCARA) robots to overcome the challenges of repairing miniature PCB components. The proposed system integrates Image-Based Visual Servoing (IBVS) for precise X and Y coordinate estimation and a simplified laser triangulation method for Z-axis depth estimation. The system demonstrated accuracy rates of 98% for X and Y axes and 99% for the Z axis, coupled with high operational speed. The developed solution highlights the potential for automating PCB repair processes by enabling SCARA robots to execute precise picking and placement tasks. When equipped with a hot-air gun as the end-effector, the system could enable automated soldering and desoldering, effectively replacing faulty SMD components without human intervention. This advancement has the potential to bridge a critical gap in the PCB repair industry, improving efficiency and reducing dependence on manual expertise.