20505 research outputs found
Sort by
Active chlorophyll fluorescence technologies in precision weed management: overview and perspectives
Get PDFWeeds are among the primary factors that adversely affect crop yields. Chlorophyll fluorescence, as a sensitive indicator of photosynthetic activity in green plants, provides direct insight into photosynthetic efficiency and the functional status of the photosynthetic apparatus. This makes it a valuable tool for assessing plant health and stress responses. Active chlorophyll fluorescence technology uses an external light source to excite plant leaves, enabling the rapid acquisition of fluorescence signals for real-time monitoring of vegetation in the field. This technology shows great potential for weed detection, as it allows for accurate discrimination between crops and weeds. Furthermore, since weed-induced stress affects the photosynthetic process of plants, resulting in changes in fluorescence characteristics, chlorophyll fluorescence can also be used to detect herbicide resistance in weeds. This paper reviews the progress in using active chlorophyll fluorescence sensor technology for weed detection. It specifically outlines the principles and structure of active fluorescence sensors and their applications at different stages of field operations, including rapid classification of soil and weeds during the seedling stage, identification of in-row weeds during cultivation, and assessment of herbicide efficacy after application. By monitoring changes in fluorescence parameters, herbicide-resistant weeds can be detected early, providing a scientific basis for precision herbicide application.National Natural Science Foundation of China: 320011417, Zhenjiang Science & Technology Program: NY2024020, Modern Agricultural Machinery Equipment and Technology Promotion Project of Jiangsu Province: NJ2024-26, Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment: XTCX2010, Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture: 2025QSZD2504, China Postdoctoral Science Foundation: 2025T180690Agricultur
Competence retention analysis: a technique for predicting and managing retention within organizational training design and delivery
No full textData supporting this study cannot be made available due to commercial restrictions and the nature of the research.Those responsible in organisations for the design and delivery of training require a practical method for the analysis and prediction of skills retention. To address this, a taxonomy of nine psychological domains was developed specifically to provide a finer-grained approach to analysis of the skills required in the performance of trained tasks. An extant predictive model relevant to five of the domains was applied to produce a set of domain retention curves for physical/lower-order cognitive skills. These curves informed the development of a novel Competence Retention Analysis Technique (CRA-T) that incorporates a simple ‘traffic light’ approach indicating workforce proficiency, following a period without practice. CRA-T simplifies the process of understanding skill retention for practitioners by providing an alternative to separate empirical studies. By identifying the psychological domains involved in task performance insights can be gained into the acquisition and retention of these components, allowing the determination of those most at risk of decay. CRA-T is suitable for the analysis of a range of physical/cognitive tasks across sectors, where systematic approaches to training analysis/design for skill retention optimisation are required. CRA-T considers complex cognitive skills, but as no predictive models currently exist, longitudinal research is required to define their retention levels.The research was funded by the Defence Science and Technology Laboratory (DSTL) through the Defence Human Capability Science and Technology Centre (contract number: DSTLX-1000069524) and the Human and Social Sciences Research Capability (contract number: DSTL/AGR/01035/01).Journal of Cognitive Engineering and Decision Makin
Combining fusion-based and solid-state additive manufacturing: investigation of additive DED structures with friction surfacing interlayer
No full textFusion-based additive manufacturing (AM) techniques face some challenges for aluminium due to the necessity of material melting resulting in insufficient bonding. The present work provides a novel insight into the combination of fusion-based and solid-state AM approaches to successfully generate structures from different aluminium alloys. Specifically, the friction-based solid-state AM technique of friction surfacing (FS) is used to generate an interlayer structure on AA2050 substrate material. On top of this structure, additional AA5087 is deposited via Wire and Arc Additive Manufacturing (WAAM). For the FS interlayer structure, two different alloys, AA5083 and AA7050, are explored. Additionally, the effect of inter-layer rolling is investigated for the final WAAM structure. The built structures are investigated with special focus on the interfaces, i.e., FS deposit-to-substrate and WAAM deposit-to-FS deposit interfaces. In the cross sections, no defects could be detected at the FS deposit-to-substrate interfaces and the structures did not show visible cracks at the WAAM deposit-to-FS deposit interfaces. The investigation showed that the mechanical properties of the WAAM structure improve when inter-layer rolling is applied, leading to homogeneous mechanical properties across the interfaces. The study highlights that FS as friction-based solid-state AM process is capable to build interlayer structures for material combinations, which cannot be achieved directly via a fusion-based process. The approach of combining different AM techniques can be advantageous not only to achieve a dissimilar material combinations but also to build hybrid structures with locally optimized properties.This research was partially funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723600 through the LASIMM project.Additive Manufacturing Letter
MSConv-YOLO: An improved small target detection algorithm based on YOLOv8
Get PDFThis article belongs to the Section Computer Vision and Pattern RecognitionSmall object detection in UAV aerial imagery presents significant challenges due to scale variations, sparse feature representation, and complex backgrounds. To address these issues, this paper focuses on practical engineering improvements to the existing YOLOv8s framework, rather than proposing a fundamentally new algorithm. We introduce MultiScaleConv-YOLO (MSConv-YOLO), an enhanced model that integrates well-established techniques to improve detection performance for small targets. Specifically, the proposed approach introduces three key improvements: (1) a MultiScaleConv (MSConv) module that combines depthwise separable and dilated convolutions with varying dilation rates, enhancing multi-scale feature extraction while maintaining efficiency; (2) the replacement of CIoU with WIoU v3 as the bounding box regression loss, which incorporates a dynamic non-monotonic focusing mechanism to improve localization for small targets; and (3) the addition of a high-resolution detection head in the neck–head structure, leveraging FPN and PAN to preserve fine-grained features and ensure full-scale coverage. Experimental results on the VisDrone2019 dataset show that MSConv-YOLO outperforms the baseline YOLOv8s by achieving a 6.9% improvement in [email protected] and a 6.3% gain in recall. Ablation studies further validate the complementary impact of each enhancement. This paper presents practical and effective engineering enhancements to small object detection in UAV scenarios, offering an improved solution without introducing entirely new theoretical constructs. Future work will focus on lightweight deployment and adaptation to more complex environments.Journal of Imagin
Machine learning in thermography non-destructive testing: a systematic review
Get PDFThis paper reviews recent advances in machine learning (ML) algorithms to improve the postprocessing and interpretation of thermographic data in non-destructive testing (NDT). While traditional NDT methods (e.g., visual inspection, ultrasonic testing) each have their own advantages and limitations, thermographic techniques (e.g., pulsed thermography, laser thermography) have become valuable complementary tools, particularly in inspecting advanced materials such as carbon fiber-reinforced polymers (CFRPs) and superalloys. These techniques generate large volumes of thermal data, which can be challenging to analyze efficiently and accurately. This review focuses on how ML can accelerate defect detection and automated classification in thermographic NDT. We summarize currently popular algorithms and analyze the limitations of existing workflows. Furthermore, this structured analysis provides an in-depth understanding of how artificial intelligence can assist in processing NDT data, with the potential to enable more accurate defect detection and characterization in industrial applications.This work was funded by the EPSRC platform grant (grant number EP/P027121/1).Applied Science
Zero-shot 3D reconstruction of industrial assets: a completion-to-reconstruction framework trained on synthetic data
Get PDFThis article belongs to the Special Issue Advances of Artificial Intelligence and Vision Applications, 2nd EditionCreating high-fidelity digital twins (DTs) for Industry 4.0 applications, it is fundamentally reliant on the accurate 3D modeling of physical assets, a task complicated by the inherent imperfections of real-world point cloud data. This paper addresses the challenge of reconstructing accurate, watertight, and topologically sound 3D meshes from sparse, noisy, and incomplete point clouds acquired in complex industrial environments. We introduce a robust two-stage completion-to-reconstruction framework, C2R3D-Net, that systematically tackles this problem. The methodology first employs a pretrained, self-supervised point cloud completion network to infer a dense and structurally coherent geometric representation from degraded inputs. Subsequently, a novel adaptive surface reconstruction network generates the final high-fidelity mesh. This network features a hybrid encoder (FKAConv-LSA-DC), which integrates fixed-kernel and deformable convolutions with local self-attention to robustly capture both coarse geometry and fine details, and a boundary-aware multi-head interpolation decoder, which explicitly models sharp edges and thin structures to preserve geometric fidelity. Comprehensive experiments on the large-scale synthetic ShapeNet benchmark demonstrate state-of-the-art performance across all standard metrics. Crucially, we validate the framework’s strong zero-shot generalization capability by deploying the model—trained exclusively on synthetic data—to reconstruct complex assets from a custom-collected industrial dataset without any additional fine-tuning. The results confirm the method’s suitability as a robust and scalable approach for 3D asset modeling, a critical enabling step for creating high-fidelity DTs in demanding, unseen industrial settings.Electronic
Gecko-Inspired robots for underground cable inspection: improved YOLOv8 for automated defect detection
Get PDFThis article belongs to the Special Issue Robotics: From Technologies to ApplicationsTo enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments.Electronic
Perennial flower strips can be a cost‐effective tool for pest suppression in orchards
Get PDFSpecial Focus: The Role of Agricultural Trade in Countering the Effects of Extreme WeatherFlower strips can provide many economic benefits in commercial orchards, including reducing crop damage by a problematic pest, rosy apple aphid (Dysaphis plantaginea [Passerini]). To explore the financial costs and benefits of this effect, we developed a bio‐economic model to compare the establishment and opportunity costs of perennial wildflower strips with benefits derived from increased yields due to reduced D. plantaginea fruit damage under high and low pest pressure. This was calculated across three scenarios: (1) a flower strip on land that would otherwise be an extension of the standard grass headland, (2) a flower strip on land that could otherwise be used to produce apples and (3) a flower strip in the centre of an orchard. Through reduction of D. plantaginea fruit damage alone, our study shows that flower strips on the headland can be a positive financial investment. If non‐crop land was not available, establishment of a flower strip in the centre of an orchard, instead of the edge, could recoup opportunity costs by providing benefits to crops on both sides of the flower strip. Our study can help guide the optimal placement of flower strips and inform subsidy value for these schemes.Biotechnology and Biological Sciences Research Council (BBSRC)This work was financially supported by Syngenta and the Biotechnology and Biological Sciences Research Council (BBSRC), part of UK Research and Innovation (UKRI) with the FoodBioSystems Doctoral Training Partnership (FBSDTP) (BB/T008776/1Journal of Agricultural Economic
Flight crew’s cognitive states detection using psychophysiological measurements and machine learning techniques
Get PDFIn the ever-evolving landscape of aviation safety, the accurate assessment of
pilots' mental states is of paramount significance. This thesis elucidates the
critical role of Electroencephalogram (EEG) data in comprehending pilots'
cognitive conditions. The dataset, sourced from attention-related human
performance limiting states, was publicly available on the NASA open portal
website and encompasses EEG, electrocardiogram, galvanic skin response, and
respiration data.
The initial analyses delved into the challenges posed by noise within EEG
recordings. After rigorous testing, it was observed that prevalent preprocessing
techniques, specifically band-pass filtering coupled with Independent Component
Analysis, were not always effective. This inefficiency underscored the need for
more advanced methodologies to optimize machine learning outcomes. In
response, subsequent research stages proposed a hybrid ensemble learning
approach. This innovative approach integrated advanced automated EEG
preprocessing with Riemannian geometry. Through rigorous experimentation and
validation, it was determined that this methodology accentuated the profound
advantages of refined preprocessing, significantly enhancing the accuracy and
reliability of EEG data interpretation.
As the inquiry advanced, a more integrative approach was adopted,
amalgamating EEG with other physiological data. A novel methodology,
synergizing one-dimensional Convolutional Neural Networks with Long Short-
Term Memory architectures, was unveiled. Additionally, the impact of employing
methods to handle data imbalance on machine learning performance was
thoroughly examined. In the concluding phases, the research placed a
heightened emphasis on model interpretability. Through the integration of
SHapley Additive exPlanations values, a bridge was constructed between
intricate model predictions and nuanced human comprehension, delineating
paramount features for distinct cognitive states.
To encapsulate, this thesis offers a meticulous dissection of EEG data
manipulation, machine learning, and deep learning constructs, positing a
blueprint for the augmentation of aviation safety through in-depth cognitive state
evaluations.PhD in Aerospac
Algae bioremediation of swine and domestic wastewater promotes a reduction of coliforms and antibiotic-resistant bacteria
Get PDFThe microbiological load that wastewater may contain is an important factor to consider in wastewater treatment to avoid water bodies contamination and has taken on great relevance due to the possible presence of antibiotic-resistant bacteria. This study investigates the feasibility of bacteria control by phycoremediation treatment using Scenedesmus sp. in two types of wastewater (domestic and swine wastewater). It was determined the cell growth of microalgae culture, and the reduction of total coliforms and enterobacteria load throughout ten days of experiment. In addition, the removal of antibiotic-resistant bacteria was performed using five different antibiotics commonly used in clinical diagnosis: Ampicillin Tetracycline, Ciprofloxacin, Sulfamethoxazole, and Ceftriaxone. The results shown a significant decrease in total coliforms and enterobacteria in the phycoremediation process, it was removed up to 98 % of total coliforms [ from (8.7 ± 2.31) × 10^4 to (1.6 ± 0.17) × 10^3 CFU mL^−1] in swine wastewater and 99 % in domestic wastewater [(3.6 ± 0.31) × 10^5 to (2 ± 0.05) × 10^3 CFU mL^−1]. Significant reduction in the case of sulfamethoxazole-resistant bacteria by microalgae in swine wastewater from [(1.47 ± 0.05) × 105 to (5.3 ± 0.57) × 10^3 ] and domestic wastewater [(4.9 ± 0.15) × 10^4 to (2.9 ± 0.36) × 10^3]. These findings demonstrate the versatility and effectiveness of the phycoremediation system since the general microbial control to most specific of antibiotic-resistant bacteria in wastewater, demonstrating its great potential to reduce the risk of public health issues in urban and rural areas.The authors would like to gratefully acknowledge Water Labs from Tecnologico de Monterrey for the laboratory facilities. Additionally, they acknowledge to Consejo Nacional de Humanidades, Ciencias y Tecnología (CONAHCYT) for the PhD scholarship to I. Lopez-Pacheco [CVU: 859227] and for the Sistema Nacional de Investigadores [SNI] program awarded to E. M. M.-M [CVU:230784], G. G-M [CVU: 490688] and R.P-S [CVU: 35753].Environmental Pollutio