Previous Issue
Volume 3, March
 
 

NDT, Volume 3, Issue 2 (June 2025) – 5 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Select all
Export citation of selected articles as:
15 pages, 4025 KiB  
Article
Enhancing Dermatological Diagnosis Through Medical Image Analysis: How Effective Is YOLO11 Compared to Leading CNN Models?
by Rakib Ahammed Diptho and Sarnali Basak
NDT 2025, 3(2), 11; https://doi.org/10.3390/ndt3020011 - 21 May 2025
Abstract
Skin diseases represent a major worldwide health hazard affecting millions of people yearly and substantially compromising healthcare systems. Particularly in areas where dermatologists are scarce, standard diagnostic techniques, which mostly rely on visual inspection and clinical experience, are frequently subjective, time-consuming, and prone [...] Read more.
Skin diseases represent a major worldwide health hazard affecting millions of people yearly and substantially compromising healthcare systems. Particularly in areas where dermatologists are scarce, standard diagnostic techniques, which mostly rely on visual inspection and clinical experience, are frequently subjective, time-consuming, and prone to mistakes. This investigation undertakes a comparative analysis of four state-of-the-art deep learning architectures, YOLO11, YOLOv8, VGG16, and ResNet50, in the context of skin disease identification. This study evaluates the performance of these models using pivotal metrics, building upon the foundation of the YOLO paradigm, which revolutionized spatial attention and multi-scale representation. A properly selected collection of 900 high-quality dermatological images with nine disease categories was used for investigation. Robustness and generalizability were guaranteed by using data augmentation and hyperparameter adjustment. By varying benchmark models in balancing accuracy and recall while limiting false positives and false negatives, YOLO11 obtained a test accuracy of 80.72%, precision of 88.7%, recall of 86.7%, and an F1 score of 87.0%. The expedition performance of YOLO11 signifies a promising trajectory in the development of highly accurate skin disease detection models. Our analysis not only highlights the strengths and weaknesses of the model but also underscores the rapid development of deep learning techniques in medical imaging. Full article
Show Figures

Figure 1

40 pages, 3280 KiB  
Review
Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling
by Shanmugam Vijayakumar, Palanisamy Shanmugapriya, Pasoubady Saravanane, Thanakkan Ramesh, Varunseelan Murugaiyan and Selvaraj Ilakkiya
NDT 2025, 3(2), 10; https://doi.org/10.3390/ndt3020010 - 16 May 2025
Viewed by 110
Abstract
Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned [...] Read more.
Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned aerial vehicles (UAVs), have emerged as innovative solutions. These tools offer farmers high precision (±1 cm spatial accuracy), enabling efficient and sustainable weed management. Herbicide spraying robots, mechanical weeding robots, and laser-based weeders are deployed on large-scale farms in developed countries. Similarly, UAVs are gaining popularity in many countries, particularly in Asia, for weed monitoring and herbicide application. Despite advancements in robotic and UAV weed control, their large-scale adoption remains limited. The reasons for this slow uptake and the barriers to widespread implementation are not fully understood. To address this knowledge gap, our review analyzes 155 articles and provides a comprehensive understanding of PWC challenges and needed interventions for scaling. This review revealed that AI-driven weed mapping in robots and UAVs struggles with data (quality, diversity, bias) and technical (computation, deployment, cost) barriers. Improved data (collection, processing, synthesis, bias mitigation) and efficient, affordable technology (edge/hybrid computing, lightweight algorithms, centralized computing resources, energy-efficient hardware) are required to improve AI-driven weed mapping adoption. Specifically, robotic weed control adoption is hindered by challenges in weed recognition, navigation complexity, limited battery life, data management (connectivity), fragmented farms, high costs, and limited digital literacy. Scaling requires advancements in weed detection and energy efficiency, development of affordable robots with shared service models, enhanced farmer training, improved rural connectivity, and precise engineering solutions. Similarly, UAV adoption in agriculture faces hurdles such as regulations (permits), limited payload and battery life, weather dependency, spray drift, sensor accuracy, lack of skilled operators, high initial and operational costs, and absence of standardized protocol. Scaling requires financing (subsidies, loans), favorable regulations (streamlined permits, online training), infrastructure development (service providers, hiring centers), technological innovation (interchangeable sensors, multipurpose UAVs), and capacity building (farmer training programs, awareness initiatives). Full article
Show Figures

Figure 1

21 pages, 3177 KiB  
Article
Modal Passport Concept for Enhanced Non-Destructive Monitoring and Diagnostics of Wind Turbine Blades
by Aleksey Mironov, Pavel Doronkin and Aleksejs Safonovs
NDT 2025, 3(2), 9; https://doi.org/10.3390/ndt3020009 - 30 Apr 2025
Viewed by 118
Abstract
One of the most sensitive parts of a wind turbine to environmental influences are the rotating blades. Today, there are many technologies available to assess blade condition, but they all need to be developed to become more cost-effective and more sensitive to fault [...] Read more.
One of the most sensitive parts of a wind turbine to environmental influences are the rotating blades. Today, there are many technologies available to assess blade condition, but they all need to be developed to become more cost-effective and more sensitive to fault detection. The algorithms and methods of the modal passport discussed in this paper propose a non-destructive technique already used for helicopter blade condition monitoring and diagnostics. This technique requires adaptation to wind turbine blades because they have larger dimensions, other materials and design, and operate under other conditions. To provide this adaptation, computational and experimental data on the modal properties of the blades must be obtained. The first stage of the study is planned to be performed on a scale model on stationary and rotating test rigs. At this stage of the study, algorithms and methods for the formation of a roadmap to develop a modal passport for a series of composite models of a wind turbine blade are considered. The initial stage of modal passport development included FE modeling of the blade model, calculation of modal parameters, fabricating the blades, and preparing the test equipment. Quantitative assessment of modal tests volume made it possible to plan the step-by-step execution of the roadmap for development and experimental application of the modal passport of wind turbine blade models. Full article
(This article belongs to the Topic Nondestructive Testing and Evaluation)
Show Figures

Figure 1

26 pages, 16508 KiB  
Article
Development of an Integrated Software Framework for Enhanced Hybrid Simulation in Structural Testing
by Gidewon G. Tekeste, António A. Correia and Aníbal G. Costa
NDT 2025, 3(2), 8; https://doi.org/10.3390/ndt3020008 - 15 Apr 2025
Viewed by 406
Abstract
Hybrid simulation integrates numerical and experimental techniques to analyze structural responses under static and dynamic loads. It physically tests components that are not fully characterized while modeling the rest of the structure numerically. Over the past two decades, hybrid testing platforms have become [...] Read more.
Hybrid simulation integrates numerical and experimental techniques to analyze structural responses under static and dynamic loads. It physically tests components that are not fully characterized while modeling the rest of the structure numerically. Over the past two decades, hybrid testing platforms have become increasingly modular and versatile. This paper presents the development of a robust hybrid testing software framework at the National Laboratory for Civil Engineering (LNEC), Portugal, and evaluates the efficiency of its algorithms. The framework features a LabVIEW-based control and interface application that exchanges data with OpenSees via the OpenFresco middleware using a TCP/IP protocol. Designed for slow to real-time hybrid testing, it employs a predictor–corrector algorithm for motion control, enhanced by an adaptive time series (ATS)-based error tracking and delay compensation algorithm. Its modular design facilitates the integration of new simulation tools. The framework was first assessed through simulated hybrid tests, followed by validation via a hybrid test on a two-bay, one-story steel moment-resisting frame, where one exterior column was physically tested. The results emphasized the importance of the accurate system identification of the physical substructure and the precise calibration of the actuator control and delay compensation algorithms. Full article
Show Figures

Figure 1

12 pages, 3807 KiB  
Technical Note
Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems
by Carl Lee Tolbert
NDT 2025, 3(2), 7; https://doi.org/10.3390/ndt3020007 - 24 Mar 2025
Viewed by 478
Abstract
Nondestructive testing (NDT) has a crucial role in ensuring the reliability and safety of industrial systems. However, traditional methods typically rely on external sensors, which can lead to increased costs and added complexity. The current study examined an alternative approach using variable-frequency drive [...] Read more.
Nondestructive testing (NDT) has a crucial role in ensuring the reliability and safety of industrial systems. However, traditional methods typically rely on external sensors, which can lead to increased costs and added complexity. The current study examined an alternative approach using variable-frequency drive (VFD) data for real-time fault detection and predictive maintenance. Most VFDs continuously monitor essential parameters such as motor speed, torque, efficiency, and power consumption, facilitating sensorless condition monitoring that helps detect early-stage motor and apparatus faults without additional hardware. To improve diagnostic capabilities, calculated metrics such as apparent power, efficiency, torque, and energy consumption can deliver more profound insights into system performance, assisting in identifying potential failure patterns. A Python-based data acquisition and visualization system was developed and implemented as an example of a potential solution, enabling centralized monitoring, anomaly detection, and historical data analysis. Future advancements in artificial intelligence and machine learning could further refine automated fault detection by utilizing historical VFD data to predict system failures accurately. By integrating VFD-based diagnostics into NDT, industries can develop scalable, cost-effective, intelligent testing and maintenance solutions that improve reliability and asset management in modern systems. Full article
Show Figures

Figure 1

Previous Issue
Back to TopTop