Search Results (214)

The unique molecular fingerprint spectral characteristics in the terahertz (THz) band provide distinct advantages for non-destructive and rapid biomolecular detection. However, conventional THz metasurface biosensors still face significant challenges in achieving highly sensitive and precise detection. This study proposes a sensing platform based on quasi-bound states in the continuum (Quasi-BIC), which enhances molecular fingerprint recognition through resonance amplification. We designed a symmetric graphene double-split square ring metasurface structure. By modulating the Fermi level of graphene, this system generated continuously tunable Quasi-BIC resonance peaks across a broad THz spectral range, achieving precise spectral overlap with the characteristic absorption lines of lactose (1.19 THz and 1.37 THz) and tyrosine (0.958 THz). The results demonstrated a remarkable 763-fold enhancement in absorption peak intensity through envelope analysis for analytes with 0.1 μm thickness, compared to conventional bare substrate detection. This terahertz BIC metasurface sensor demonstrates high detection sensitivity, holding significant application value in fields such as biomedical diagnosis, food safety, and pharmaceutical testing. Full article

Pomegranate blackheart disease, as an internal disease affecting the global pomegranate industry, is difficult to identify externally and urgently requires non-destructive detection methods for rapid diagnosis. This study established discriminative models for blackheart disease severity in pomegranates by using near-infrared (NIR) spectroscopy and soft X-ray imaging techniques. The results showed that the optimal NIR-based discriminative model, constructed with a Random Forest (RF) algorithm based on spectra preprocessed by the second-derivative (D2) denoising and a Competitive Adaptive Reweighted Sampling (CARS) algorithm, achieved a prediction set accuracy of 86.00%; the optimal soft X-ray imaging-based discriminative model, built with an RF algorithm using textural features extracted from images preprocessed by median filtering and a Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm combined with gray-level co-occurrence matrix (GLCM) and gray-gradient co-occurrence matrix (GGCM) algorithms, reached a prediction set accuracy of 93.10%. In terms of model performance, the model based on soft X-ray imaging exhibited superior performance. Both techniques possess distinct advantages and limitations yet enable non-destructive detection of pomegranate blackheart disease. Further technical optimizations in the future could provide enhanced support for the healthy development of the pomegranate industry. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

Efficient nitrogen management is key to maximizing production and minimizing the environmental impact of horticultural crops. This study analyses the effect of different doses of nitrogen on the development of broccoli (Brassica oleracea var. italica) (cultivar Parthenon), processing tomato (Solanum lycopersicum) (cultivar H1015), and processing pepper (Capsicum annuum) (cultivar Ramonete Lamuyo) in open fields in Extremadura and evaluates rapid and efficient methods for diagnosing their nutritional status. Trials were carried out at the La Orden Experimental Farm (CICYTEX) with different nitrogen fertilization rates. The N doses were 0–60–120–180 kg N/ha for peppers in 2020 and 2021 and 0–200–300 kg N/ha for 2022. For broccoli, the N doses were 0–100–200–300 kg N/ha in 2020 and 0–200–300 kg N/ha for 2022. For tomatoes, the N doses were 0–100–200–300 kg N/ha in 2021 and 0–200–350 kg N/ha for 2022. The following three indicators were compared: chlorophyll content measured with optical sensors, petiole sap nitrate concentration, and the nitrogen nutrition index (NNI). The results indicate that chlorophyll measurement is not suitable for broccoli due to the characteristics of its leaves, but is useful for tomatoes and peppers, providing a quick and non-destructive diagnosis. Nitrate concentration in sap, although more laborious and destructive, was found to be effective in discriminating nutritional status in the three species. However, the NNI did not prove to be a good reference method in open field conditions. These results highlight the importance of adapting nutrient monitoring strategies to the crop and management conditions, contributing to a more efficient use of nitrogen and a reduction in the environmental impact of nitrate leaching. Full article

Real-time monitoring of cotton moisture status and determination of appropriate irrigation thresholds are essential for achieving precision irrigation. Currently employed diagnostic methods based on physiological indicators, remote sensing, or soil moisture measurements typically present limitations including cumbersome procedures, high labor intensity, requirements for specialized technical expertise, and delayed results. To address these challenges, this study investigated the relationship between plant water content and leaf RGB color values (red, green, and blue color values measured using LScolor technology) during the bud, flowering, and boll development stages, with the objective of establishing a predictive model for rapid, real-time moisture status monitoring. Given that leaf position and color values (R, G, and B) of different functional leaves may influence the relationship between leaf color and plant water content, and this relationship varies across different temporal periods, a two-year experiment was conducted. In 2023, leaf color data from the top five functional leaves were measured at five time points daily throughout the irrigation cycle. In 2024, the following four irrigation treatments were established: one conventional irrigation control treatment (CK) and three irrigation treatments at 72% (T1), 70% (T2), and 68% (T3) plant water content thresholds. Results demonstrated that the following: (1) plant water content initially declined during the day and subsequently showed slight recovery, indicating cotton’s particular susceptibility to water stress between 2:30 p.m. and 7:00 p.m.; (2) plant water content continuously decreased across five measurement periods following irrigation during the bud, flowering, and boll development stages, with R and G color values of the five functional leaves showing declining trends between 2:30 p.m. and 7:00 p.m., while B color values exhibited no consistent pattern; (3) correlation analysis revealed significant positive correlations between plant water content and R and G color values of the five functional leaves during the 2:30 p.m. to 5:00 p.m. period, with highly significant correlations observed for the third and fourth leaves from the apex; (4) univariate and bivariate linear regression models were successfully established between cotton water content and R and G color values of the third and fourth leaves from the top; and (5) under 72% plant water content conditions, cotton achieved the highest yield and Irrigation Water Use Efficiency, indicating that 72% represents the optimal irrigation threshold. In conclusion, integrating leaf color–plant water content relationships with the 72% irrigation threshold enables rapid, non-destructive, large-scale diagnosis of cotton moisture status, providing a robust foundation for implementing effective precision irrigation strategies. Full article

Adzuki bean rust disease is an important factor restricting the yield of the adzuki bean. Late prevention and control at the early stage of the disease will lead to crop failure. Traditional diagnosis methods of adzuki bean rust disease mainly rely on field observations and laboratory tests, which are inefficient, time-consuming, highly dependent on professional knowledge, and cannot meet the requirements of modern agriculture for rapid and accurate diagnosis. To address this issue, a diagnosis method of adzuki bean rust disease was proposed using spectroscopy and deep learning methods. First, visible/near-infrared (UV/VNIR) spectroscopy was used to extract the spectral information of leaves, and discrete wavelet transform (DWT) was applied to preprocess and smooth the original canopy spectral data to effectively reduce the impact of noise interference. Second, the competitive adaptive reweighted sampling (CARS) algorithm was implemented in the range of 425–825 nm to determine the optimal characteristic wavenumbers, thereby reducing data redundancy. Finally, 51 characteristic wavenumbers were selected and imported into the LeNet-5 deep learning model for simulation and evaluation. The results showed that the accuracy, precision, recall, and F1 score on the test set were 99.65%, 98.04%, 99.01%, and 98.52%, respectively. The proposed DWT-CARS-LeNet-5 model can diagnose adzuki bean rust quickly, accurately, and non-destructively. This method can provide a cutting-edge solution for improving the accuracy of prevention and control of adzuki bean rust disease in agricultural practice. Full article

Cracks in concrete structures are key indicators for structural health diagnosis, and the demand for automated detection is gradually increasing. Although various non-destructive testing (NDT) methods and concrete defect detection software have been widely applied, their detection performance varies significantly when dealing with cracks of different shapes and scales. In particular, under complex environmental conditions, detecting fine, irregular, or occluded cracks remains a major challenge. Traditional image-processing-based methods face clear limitations in feature extraction and detection efficiency in practical applications. To address these issues, we propose the CGSW-YOLOv5 algorithm, which enhances detection performance through the following innovations: First, a Concrete Crack Feature Enhancement Block (CNeB) is introduced to improve fine-detail capture. Second, an Adaptive Multi-Scale Feature Aggregation attention mechanism (AMFA) is designed to optimize convolutional kernel selection. Third, the Dynamic Gradient Focusing Weighted IoU loss (DGFW-IoU) is adopted to improve localization accuracy for small targets. Finally, a Lightweight Dual-Stream Convolutional Feature Enhancement module (LDSConv) is constructed to achieve efficient feature utilization. Experimental results show that the CGSW-YOLOv5 algorithm achieves detection accuracies of 71.74% and 72.85% on a self-built dataset and a concrete crack dataset under various environmental conditions (windy, rainy, and foggy), respectively. These results represent improvements of 4.49% and 4.6% over the baseline algorithm, demonstrating superior detection performance and strong environmental adaptability. The proposed method provides an effective solution for intelligent crack detection in concrete structures. Full article

Non-destructive evaluation (NDE) is highly relevant to assessing micro- and macrostructural changes in ferritic and ferritic/martensitic steels subjected to high temperature loading. These materials are widely used in energy generation, where they undergo extreme thermal and mechanical loads. This study examines the feasibility of micromagnetic NDE techniques, i.e., micromagnetic measurements, supported by machine learning methods, to identify and characterize the micro- and macrostructural changes caused by the mechanical loading at high temperatures of power plant steels, i.e., ferritic/martensitic P91 and the high chromium ferritic steel HiperFer-17Cr2. While the P91 did not show any systematic changes in micromagnetic measurements, which generally correlate with the evolution of the microstructure and the mechanical properties, for the HiperFer-17Cr2, pronounced changes in the micromagnetic properties were observed. In correlation with the evolution of the hardness and cyclic deformation behavior, which are both mainly attributed to Laves phase precipitation, the micromagnetic measurements significantly changed depending on the temperature, number of load cycles and load amplitude applied. Thus, these NDE methods can be used for early diagnosis and preventive maintenance strategies for HiperFer-17Cr2, potentially extending the lifespan of the components and mitigating safety risks. Full article

X-ray imaging, as a technique of non-destructive testing, has demonstrated considerable promise in COVID-19 diagnosis, particularly if supplemented with artificial intelligence (AI). Both radiologic technologists and AI researchers have raised the alarm about having to use increased doses of radiation in order to get more refined images and, hence, enhance diagnostic precision. In this research, we assess whether the disparity in exposure to the radiation dose considerably influences the credibility of AI-based diagnostic systems for COVID-19. A heterogeneous dataset of chest X-rays acquired at varying degrees of radiation exposure was run through four convolutional neural networks: VGG16, VGG19, ResNet50, and ResNet50V2. Results indicated above 91% accuracies, demonstrating that greater radiation exposure does not appreciably enhance diagnostic accuracy. Low radiation exposure sufficient to be utilized by human radiologists is therefore adequate for AI-based diagnosis. These findings are useful to the medical community, emphasizing that maximum diagnostic accuracy using AI does not need increased doses of radiation, thus further guaranteeing the safe application of X-ray imaging in COVID-19 diagnosis and possibly other medical and veterinary applications. Full article

Nitrogen is a crucial environmental factor influencing lettuce growth, development, and quality formation. This study aimed to determine the relationship between plant growth, nutritional quality formation, and different nitrogen levels of lettuce. A machine learning approach was also applied to data collected from RGB and hyperspectral imaging systems. Traditional methods for nitrogen diagnosis in lettuce, such as laboratory-based analysis of plant samples, are labor-intensive, time-consuming, and lack real-time monitoring capabilities. In contrast, the deep learning models used in this research can make full use of the original data from imaging systems. Nondestructive techniques have the ability to handle complex relationships in the data, enabling more accurate and efficient nitrogen diagnosis. Collected spectral features were combined with chemometrics, and a lettuce nitrogen regression diagnostic model was trained. Furthermore, lettuce crop growth was assessed using a model development of environmental and plant physiological parameters. Additionally, nitrogen fertilization was precisely assessed using developed models. Lettuce cultivation experiments under different nitrogen levels showed the best physiological and biochemical indicators performance when the nitrogen concentration reached 18.75 mmol·L⁻¹. Using machine learning with hyperspectral reflectance in nitrogen diagnostics, random forest showed excellent performance with the highest R², MSE, and MAE of 0.7012, 8.940, and 2.1859, respectively. ShuffleNet-v2-1.0 obtained a high R² of 0.9592, MSE of 132.9974, and MAE of 8.1430 regarding transfer learning and hyperspectral images. Applying the transfer learning technique in RGB images exhibited EfficientNet-v2-s, the best model for precise determination of nitrogen diagnostics, with R² of 0.9859, MSE of 24.0755, and MAE of 2.3433. Current research comprehensively provides both a theoretical basis and practical solutions for precision nitrogen fertilization in lettuce cultivation. Its implications hold significance for the intelligent management of horticultural crop production. Full article

Accurate diagnosis of crop nutritional status is critical for optimizing yield and quality in modern agriculture. This study enhances the accuracy of Raman spectroscopy-based nutrient diagnosis, improving its application in precision agriculture. We propose a method to identify optimal diagnostic positions on cucumber leaves for early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, thereby providing a robust scientific basis for high-throughput phenotyping using Raman spectroscopy (RS). Using a dot-matrix approach, we collected RS data across different leaf positions and explored the selection of diagnostic positions through spectral cosine similarity analysis. These results provide critical insights for developing rapid, non-destructive methods for nutrient stress monitoring in crops. Results show that spectral similarity across positions exhibits higher instability during the early developmental stages of leaves or under short-term (24 h) nutrient stress, with significant differences in the stability of spectral data among treatment groups. However, visual analysis of the spatial distribution of positions with lower similarity values reveals consistent spectral similarity distribution patterns across different treatment groups, with the lower similarity values predominantly observed at the leaf margins, near the main veins, and at the leaf base. Excluding low-similarity data significantly improved model performance for early (24 h) nutrient deficiency diagnosis, resulting in higher precision, recall, and F1 scores. Based on these results, the efficacy of the proposed method for selecting diagnostic positions has been validated. It is recommended to avoid collecting RS data from areas near the leaf margins, main veins, and the leaf base when diagnosing early nutrient deficiencies in plants to enhance diagnostic accuracy. Full article

Poultry body temperature is closely related to their metabolism and vital activities, which can indicate their physiological status and health. Therefore, monitoring these temperature changes by analyzing thermal images can help in the early and accurate diagnosis of their diseases using a non-destructive method. On the other hand, it is very important to state which part of the bird has the greatest effect on the diagnosis of the disease. This not only speeds up the diagnosis process but also determines an important index for animal pathologists. In this study, an intelligent algorithm was presented with the aim of early diagnosis and classification of two diseases, Avian influenza and Newcastle disease, in the early hours of disease transmission. For this purpose, three different models were developed based on thermal images, including: original images, images with background removal, and images with the head and legs of the chicken separated by the YOLO-v8 model. Then, the features extracted from the thermal images, including texture and color, were evaluated in all three models with a support vector machine (SVM) classifier. Also, the most important and effective features of thermal images for the diagnosis of two diseases, Avian influenza and Newcastle disease, were introduced to other researchers by the Relief feature selection algorithm. The classification results of the original images, images without background and images of the head and legs of chickens for Avian influenza were 75.89, 83.93, and 92.48%, respectively, and for Newcastle disease were 83.04, 91.52, and 94.20% respectively. The model developed for early diagnosis of the disease showed the ability to diagnose the two diseases at 8 h after disease infection with an accuracy of more than 90%. The results show that the contribution of texture-related features is greater than other features extracted from thermal images in the diagnosis of poultry diseases. Also, focusing on the head and feet areas by the YOLO-v8 algorithm will increase the classification accuracy, which allows for more accurate diagnosis in real time and in the early stages of the disease. Full article

This paper introduces a novel diagnostic approach called partial-discharge-activated impulse frequency response analysis (PD-IFRA), developed to overcome the limitations of conventional frequency response analysis (FRA) in detecting partial discharges (PDs) in power transformers. While traditional FRA with low-impulse-voltage excitation (LIVE) effectively identifies mechanical deformations, inter-turn shorts, and insulation faults, it fails to detect incipient PD activity since PD phenomena require excitation beyond the PD inception voltage (PDIV) to initiate. This study proposes, for the first time, the extension of IFRA to moderate impulse voltage levels—without exceeding insulation limits—enabling the early and non-destructive detection of PDs. Experimental validation on a 315 kVA, 11 kV/420 V Dyn11 transformer shows that PD-IFRA effectively identifies PD-related impedance deviations within the 10 kHz–2 MHz frequency range, especially for PD sources associated with floating metal parts. Furthermore, the comparative analysis between normal, short-circuited, and PD-induced conditions demonstrates that PD-IFRA significantly enhances the precursory diagnosis of PDs where conventional FRA fails. This contribution advances transformer condition assessment by integrating PD sensitivity into FRA-based methods without compromising equipment safety. Full article

This text provides a comprehensive overview of structural timber old buildings, from an in-depth analysis of construction processes to laboratory-based research aimed at establishing a pattern for estimating the density of wood in buildings. It is now widely recognised by society that historic buildings should be subject to conservation or rehabilitation. This article discusses the good technical knowledge that those involved in old buildings should have: the understanding of and respect for old construction techniques; rigorous inspections and diagnosis before a project; and the recognition of the properties of wooden structural elements, either visually or by means of non-destructive or semi-destructive testing methods (NDT/SDT). The final section of this article presents a laboratory study that correlates penetration resistance test results with wood density and verifies them in situ by direct analysis with wood core extraction. The aim of this study is to establish and verify a reliable pattern that allows the user to estimate the density of Scots pine in any structural member in service in an old building. The results obtained in the laboratory and of wood in service show that Equation (1) is a suitable pattern to obtain wood density through the wood penetration resistance test. Full article

The diagnosis of post-stressed anchor rods is essential for maintaining the service and ensuring the safety of Electricité de France (EDF) structures. These rods are critical for the mechanical strength of structures and electromechanical components. Currently, the standard method for estimating the effective tension of post-stressed tie rods with a free length involves measuring the residual force using a hydraulic jack. However, this method can be costly, impact the structure’s operation, and pose risks to employees. Until now, there has been no reliable on-field approach to estimating residual tension using a lightweight setup. This research introduces a nondestructive method using multimodal ultrasonic guided waves to evaluate the residual tension of anchor rods with a few centimeters free at one end. The methodology was developed through both laboratory experiments and simulations. This new method allows for the extraction of dispersion curves for the first three modes, bending, torsional, and longitudinal, using time–frequency analysis and enables the estimation of the steel bar’s properties. Future work will focus on applying this methodology in the field. Full article