Search Results (71)

Rainwater harvesting (abbreviation: RWH) presents a valuable alternative water source for agriculture, particularly in regions facing water scarcity. However, contaminants leaching from roofing materials, such as asbestos cement (abbreviation: AC), may compromise water quality and affect plant physiological responses. This paper aimed to assess how simulated rainwater, reflecting the different levels of contamination (1, 2, 5, 10, and 20 mg/L), influences leaf temperature in tomato plants (Solanum lycopersicum), a known non-invasive indicator of plant water stress. The treatments were applied over a four-week period under controlled greenhouse conditions. Leaf temperature was monitored using infrared thermography. Results showed that higher treatment concentrations led to a significant increase in leaf temperature, indicating elevated water stress. These findings suggest that even low levels of contaminants originating from roofing materials can induce detectable physiological stress in plants. Monitoring leaf temperature offers a rapid and non-destructive method for assessing environmental water quality impacts on crops. The outcomes of this research have direct applicability in the safer design of RWH systems and in evaluating the suitability of collected rainwater for irrigation use. Full article

Solar photovoltaic (SPV) arrays play a pivotal role in advancing clean and sustainable energy systems, with a worldwide total installed capacity of 1.6 terawatts and annual investments reaching USD 480 billion in 2023. However, climate disaster effects, particularly extremely hot weather events, can compromise the performance and resilience of SPV panels through thermal deterioration and degradation, which may lead to lessened operational life and potential failure. These heatwave-related consequences highlight the need for timely inspection and precise anomaly diagnosis of SPV panels to ensure optimal energy production. This case study focuses on intelligent remote inspection by employing aerial radiometric infrared thermography within a predictive maintenance framework to enhance diagnostic monitoring and early scrutiny capabilities for SPV power plant sites. The proposed methodology leverages pre-trained deep learning (DL) algorithms, enabling a deep transfer learning approach, to test the effectiveness of multiclass classification (or diagnosis) of various thermal anomalies of the SPV panel. This case study adopted a highly imbalanced 6-class thermographic radiometric dataset (floating-point temperature numerical values in degrees Celsius) for training and validating the pre-trained DL predictive classification models and comparing them with a customized convolutional neural network (CNN) ensembled model. The performance metrics demonstrate that among selected pre-trained DL models, the MobileNetV2 exhibits the highest F1 score (0.998) and accuracy (0.998), followed by InceptionV3 and VGG16, which recorded an F1 score of 0.997 and an accuracy of 0.998 in performing the smart inspection of 6-class thermal anomalies, whereas the customized CNN ensembled model achieved both a perfect F1 score (1.000) and accuracy (1.000). Furthermore, to create trust in the intelligent inspection system, we investigated the pre-trained DL predictive classification models using perceptive explainability to display the most discriminative data features, and mathematical-structure-based interpretability to portray multiclass feature clustering. Full article

Current techniques for fault analysis in photovoltaic (PV) systems plants involve either electrical performance measurements or image processing, as well as line infrared thermography for visual inspection. Deep convolutional neural networks (CNNs) are machine learning algorithms that perform tasks involving images, such as image classification and object recognition. However, to train a model effectively to recognize different patterns, it is crucial to have a sufficiently balanced dataset. Unfortunately, this is not always feasible owing to the limited availability of publicly accessible datasets for PV thermographic data and the unequal distribution of different faults in real-world systems. In this study, three data augmentation techniques—geometric transformations (GTs), generative adversarial networks (GANs), and the denoising diffusion probabilistic model (DDPM)—were combined with a CNN to classify faults in PV modules through thermographic images and identify the type of fault in 11 different classes (i.e., soiling, shadowing, and diode). Through the cross-validation method, the main results found with the Wasserstein GAN (WGAN) and DDPM networks combined with the CNN for anomaly classification achieved testing accuracies of 86.98% and 89.83%, respectively. These results demonstrate the effectiveness of both networks for accurately classifying anomalies in the dataset. The results corroborate the use of the diffusion model as a PV data augmentation technique when compared with other methods such as GANs and GTs. Full article

In this article, we present a unique system for identifying edible oils through the analysis of their thermophysical properties. The method is based on the use of active infrared thermography. The heating of the oils results from the optical absorption of laser radiation at a specified wavelength. This approach enables greater selectivity in differentiating between various types of edible oils, as the results depend not only on the thermal properties of the specific oils but also on their optical properties, which are uniquely characteristic of each oil. Additionally, the developed system provides a detailed visualization of spatial temperature gradients within the sample’s volume, as well as their changes over time. It overcomes the limitations of other methods that determine only the thermal conductivity coefficients of oils through resistive heating of the sample. In this article, four types of vegetable oils (extra virgin olive oil, sesame oil, sunflower oil, and rapeseed oil) have been studied. Fatty acid analysis, differential scanning calorimetry, and UV-VIS spectroscopy have been used to determine the authenticity, moisture content, and optical properties of the studied samples. The developed system allows for the visualization and determination of the emerging temperature gradients in the sample volume. Full article

This study presents a novel approach for monitoring waste substrate digestion under high-density polyethylene (HDPE) geomembranes in sewage treatment plants. The method integrates infrared thermal imaging with a clustering algorithm to predict the distribution of various substrates beneath Traditional outdoor large-scale opaque geomembranes, using solar radiation as an excitation source. The technique leverages ambient weather conditions to assess the thermal responses of HDPE covers. Cooling constants are used to reconstruct thermal images, and clustering algorithms are explored to segment and identify different material states beneath the covers. Laboratory experiments have validated the algorithm’s effectiveness in accurately classifying varied regions by analyzing transient temperature variations caused by natural excitations. This method provides critical insights into scum characteristics and biogas collection processes, thereby enhancing decision-making in sewage treatment management. The methodology under development is anticipated to undergo rigorous evaluation across various floating covers at a large-scale sewage treatment facility in Melbourne. Subsequent to field validation, the implementation of an on-site, continuous thermography monitoring system is envisioned to be further advanced. Full article

Irregularly shaped wounds cause severe chronic infections, which have attracted worldwide attention due to their high prevalence and poor treatment outcomes. In this study, we designed a new composite functional dressing consisting of traditional Chinese herb carbonized plant powder (CPP) and a polyacrylic acid (PAA)/polyethylenimine (PEI) gel. The rapid gelation of the dressing within 6–8 s allowed the gel to be firmly attached to an irregularly shaped wound surface and avoided powder detachment. In addition, through an infrared thermography analysis, a coagulation assay, and a morphological examination of regenerative tissue in animal wound models, it was found that the dressing substrates had synergistic effects on photothermal sterilization, rapid hemostasis, and anti-inflammatory activity, thereby achieving an 88% wound closure rate on the 9th day after the formation of the wound. This multifunctional hemostatic material is expected to be adaptable to irregular wounds and promote rapid wound healing. Full article

Urban green infrastructure (UGI) plays a vital role in mitigating climate change risks, including urban development-induced warming. The effective maintenance and monitoring of UGI are essential for detecting early signs of water stress and preventing potential fire hazards. Recent research shows that plants close their stomata under limited soil moisture availability, leading to an increase in leaf temperature. Multi-spectral cameras can detect thermal differentiation during periods of water stress and well-watered conditions. This paper examines the thermography of five characteristic green wall and green roof plant types (Pachysandra terminalis, Lonicera nit. Hohenheimer, Rubus tricolor, Liriope muscari Big Blue, and Hedera algeriensis Bellecour) under different levels of water stress compared to a well-watered reference group measured by thermal cameras. The experiment consists of a (1) pre-test experiment identifying the suitable number of days to create three different levels of water stress, and (2) the main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages. The thermal images were captured analyzed from three different types of green infrastructure. The method was suitable to detect temperature differences between plant types, between levels of water stress, and between GI types. The results show that leaf temperatures were approximately 1–3 °C warmer for water-stressed plants on the green walls, and around 3–6 °C warmer on the green roof compared to reference plants with differences among plant types. These insights are particularly relevant for UGI maintenance strategies and regulations, offering valuable information for sustainable urban planning. Full article

Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots. Full article

Water stress on crops can severely disrupt crop growth and reduce yields, requiring the accurate and prompt diagnosis of crop water stress, especially in semiarid regions. Infrared thermal imaging cameras are effective tools to monitor the spatial distribution of canopy temperature (Tc), which is the basis of the daily water stress index (DWSI) calculation. This research aimed to evaluate the variability of plant water stress under different soil cover conditions through geostatistical techniques, using detailed thermographic images of Neem canopies in the Brazilian northeastern semiarid region. Two experimental plots were established with Neem cropped under mulch and bare soil conditions. Thermal images of the leaves were taken with a portable thermographic camera and processed using Python language and the OpenCV database. The application of the geostatistical technique enabled stress indicator mapping at the leaf scale, with the spherical and exponential models providing the best fit for both soil cover conditions. The results showed that the highest levels of water stress were observed during the months with the highest air temperatures and no rainfall, especially at the apex of the leaf and close to the central veins, due to a negative water balance. Even under extreme drought conditions, mulching reduced Neem physiological water stress, leading to lower plant water stress, associated with a higher soil moisture content and a negative skewness of temperature distribution. Regarding the mapping of the stress index, the sequential Gaussian simulation method reduced the temperature uncertainty and the variation on the leaf surface. Our findings highlight that mapping the Water Stress Index offers a robust framework to precisely detect stress for agricultural management, as well as soil cover management in semiarid regions. These findings underscore the impact of meteorological and planting conditions on leaf temperature and baseline water stress, which can be valuable for regional water resource managers in diagnosing crop water status more accurately. Full article

Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study focuses on an intelligent fault detection and diagnosis (IFDD) system for the analysis of radiometric infrared thermography (IRT) of SPV arrays in a predictive maintenance setting, enabling remote inspection and diagnostic monitoring of the SPV power plant sites. The proposed IFDD system employs a custom-developed deep learning approach which relies on convolutional neural networks for effective multiclass classification of defect types. The diagnosis of SPV panels is a challenging task for issues such as IRT data scarcity, defect-patterns’ complexity, and low thermal image acquisition quality due to noise and calibration issues. Hence, this research carefully prepares a customized high-quality but severely imbalanced six-class thermographic radiometric dataset of SPV panels. With respect to previous approaches, numerical temperature values in floating-point are used to train and validate the predictive models. The trained models display high accuracy for efficient thermal anomaly diagnosis. Finally, to create a trust in the IFDD system, the process underlying the classification model is investigated with perceptive explainability, for portraying the most discriminant image features, and mathematical-structure-based interpretability, to achieve multiclass feature clustering. Full article

This study presents a process for the quantitative investigation of thinning defects occurring in the containment liner plate (CLP) of a nuclear power plant according to various depths with a combined thermal wave signal and image processing in a lock-in thermography (LIT) technique. For that, a plate sample with a size of 300 × 300 mm was produced considering the 6 mm thickness applied to an actual CLP. The sample was designed with nine thinning defects on the back side with defect sizes of 40 × 40 mm and varying thinning rates from 10% to 90%. LIT experiments were conducted under various modulation frequency conditions, and phase angle data was calculated and evaluated through four-point method processing. The calculated phase angle was correlated with the defect depth. Then, the phase image was binarized by the Otsu algorithm to evaluate defect detection ability and shape. Furthermore, the accuracy of defect depth assessment was evaluated through third-order polynomial curve fitting. The detectability was analyzed by comparing the number of pixels of the thinning defect in the binarized image and the theoretical calculation. Finally, it was concluded that LIT can be applied for fast thinning defect detection and accurate thinning depth evaluation. Full article

Botrytis cinerea (B. cinerea) causes gray mold disease (GMD), which results in physiological disorders in plants that decrease the longevity and economic value of horticultural crops. To prevent the spread of GMD during distribution, a rapid, early detection technique is necessary. Thermal imaging has been used for GMD detection in various plants, including potted roses; however, its application to cut roses, which have a high global demand, has not been established. In this study, we investigated the utility of thermal imaging for the early detection of B. cinerea infection in cut roses by monitoring changes in petal temperature after fungal inoculation. We examined the effects of GMD on the postharvest quality and petal temperature of cut roses treated with different concentrations of fungal conidial suspensions and chemicals. B. cinerea infection decreased the flower opening, disrupted the water balance, and decreased the vase life of cut roses. Additionally, the average temperature of rose petals was higher for infected flowers than for non-inoculated flowers. One day before the appearance of necrotic symptoms (day 1 of the vase period), the petal temperature in infected flowers was significantly higher, by 1.1 °C, than that of non-inoculated flowers. The GMD-induced increase in petal temperature was associated with the mRNA levels of genes related to ethylene, reactive oxygen species, and water transport. Furthermore, the increase in temperature caused by GMD was strongly correlated with symptom severity and fungal biomass. A multiple regression analysis revealed that the disease incidence in the petals was positively related to the petal temperature one day before the appearance of necrotic symptoms. These results show that thermography is an effective technique for evaluating changes in petal temperature and a possible method for early GMD detection in the cut flower industry. Full article

Plant physiological status is the interaction between the plant genome and the prevailing growth conditions. Accurate characterization of plant physiology is, therefore, fundamental to effective plant phenotyping studies; particularly those focused on identifying traits associated with improved yield, lower input requirements, and climate resilience. Here, we outline the approaches used to assess plant physiology and how these techniques of direct empirical observations of processes such as photosynthetic CO₂ assimilation, stomatal conductance, photosystem II electron transport, or the effectiveness of protective energy dissipation mechanisms are unsuited to high-throughput phenotyping applications. Novel optical sensors, remote/proximal sensing (multi- and hyperspectral reflectance, infrared thermography, sun-induced fluorescence), LiDAR, and automated analyses of below-ground development offer the possibility to infer plant physiological status and growth. However, there are limitations to such ‘indirect’ approaches to gauging plant physiology. These methodologies that are appropriate for the rapid high temporal screening of a number of crop varieties over a wide spatial scale do still require ‘calibration’ or ‘validation’ with direct empirical measurement of plant physiological status. The use of deep-learning and artificial intelligence approaches may enable the effective synthesis of large multivariate datasets to more accurately quantify physiological characters rapidly in high numbers of replicate plants. Advances in automated data collection and subsequent data processing represent an opportunity for plant phenotyping efforts to fully integrate fundamental physiological data into vital efforts to ensure food and agro-economic sustainability. Full article

Wood waste bottom ash (WWBA) is a waste generated in power plants during the burning of forest residues to produce energy and heat. In 2019, approximately 19,800 tons of WWBA was generated only in Lithuania. WWBA is rarely recycled or reused and is mostly landfilled, which is both costly for the industry and unsustainable. This study presents a sustainable solution to replace a part of cement with WWBA at 3%, 6%, 9%, and 12% by weight. Problems are also associated with the use of this material, as WWBA could have a relatively large surface area and a high water demand. For the evaluation of the possibilities of WWBA use for cementitious materials, the calorimetry test for the cement paste as well as X-ray diffraction (XRD), thermography (TG, DTG), and porosity (MIP) for hardened cement paste with the results of physical and mechanical properties, and the freeze–thaw resistance of the concrete was measured and compared. It was found that WWBA with a large quantity of CO₂ could be used as a microfiller with weak pozzolanic properties in the manufacture of cementitious materials. As a result, concrete containing 6% WWBA used to substitute cement has higher density, compressive strength at 28 days, and ultrasonic pulse velocity values. In terms of durability, it was verified that concrete modified with 3%, 6%, 9%, and 12% WWBA had a freeze–thaw resistance level of F150. The results show that the use of WWBA to replace cement is a valuable sustainable option for the production of conventional concrete and has a positive effect on durability. Full article

Understanding the thermal effects of different urban patterns that constitute today’s urban landscapes is critical to the development of urban resilience to climate change. This article aims to assess the efficiency of urban green spaces in thermal regulation. Through thermography, we explored the interaction between air temperature and the spatial components within these environments. Through comparative analysis involving a UAV, we studied the relationship between air temperatures at varying altitudes and the temperature within tree canopies. The results revealed significant differences in the thermal distribution between impervious urban areas with buildings and green spaces. These findings provide important information for assessing thermal comfort and the efficiency of urban green spaces in mitigating the impact of extreme heat events. During the summer months, green spaces, due to shade and the enhanced absorption of solar radiation by trees, exhibited lower temperatures compared to impervious areas. However, in winter, urban areas displayed higher temperatures, attributable to their heat retention capacity. This study contributes to the existing knowledge base by providing an in-depth examination of the thermal efficiency of urban green spaces across different layers of their lower atmosphere. Our results underscore the crucial role of tree cover in thermal comfort regulation, offering valuable information for sustainable urban planning. These insights are particularly relevant for the design of more comfortable and resilient environments in response to climatic variations and for the crafting of a tree-planting strategy in Mediterranean climate cities, an area where the impacts of climate change are becoming increasingly apparent. Full article