Thermal Image Data Research Articles

Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

A Novel Real-Time Thermal Analysis and Layer Time Control Framework for Large-Scale Additive Manufacturing

Abstract Production efficiency and product quality need to be addressed simultaneously to ensure the reliability of large-scale additive manufacturing. Specifically, print surface temperature plays a critical role in determining the quality characteristics of the product. Moreover, heat transfer via conduction as a result of spatial correlation between locations on the surface of large and complex geometries necessitates the employment of more robust methodologies to extract and monitor the data. In this article, we propose a framework for real-time data extraction from thermal images and a novel method for controlling layer time during the printing process. A FLIR™ thermal camera captures and stores the stream of images from the print surface temperature, while the Thermwood Large Scale Additive Manufacturing (LSAM™) machine is printing components. A set of digital image processing tasks were performed to extract the thermal data. Separate regression models based on real-time thermal imaging data are built on each location on the surface to predict the associated temperatures. Subsequently, a control method is proposed to find the best time for printing the next layer given the predictions. Finally, several scenarios based on the cooling dynamics of surface structure were defined and analyzed, and the results were compared to the current fixed layer time policy. It was concluded that the proposed method can significantly increase the efficiency by reducing the overall printing time while preserving the quality.

Measuring and modelling of apple flower stigma temperature as a step towards improved fire blight prediction

In many areas, fire blight (Erwinia amylovora) is a sporadic but potentially devastating disease of apples. Infections occur primarily during bloom when warm weather conducive to bacteria multiplication on the stigma of contaminated flowers is followed by a wetting event, facilitating plant entry. Fire blight prediction models which rely on air temperature for disease forecast can help, but currently produce many false positive and some false negative prognoses. The differences between air and apple flower stigma temperature can explain some of the issues. The present study undertakes an introductory step in resolving this matter by being the first of its kind to document apple stigma temperatures and its departure from air temperature. Thermocouples continuously monitored flower temperature for the blooming seasons of 2018 and 2019 in the orchard of Saint-Bruno-de-Montarville, Québec, Canada, while a thermal imager measured the temperature of randomly selected flowers in 2019. Flower stigma temperature measured with thermocouples followed the diurnal pattern of air temperature, but stigma temperature was higher/lower than air with maxima/minima at peak hours of the day/night. Temperatures measured with the thermal imager revealed a mean positive difference with the air temperature during the day (1.6 ± 1.3 °C). Stigma to air differences for both instruments had a strong positive relation with solar radiation during daytime. Under high humidity, this difference was significantly reduced. From these findings, regression models for estimating stigma temperature were developed for fire blight forecasting. When validating with thermal imaging data, the best model utilizes air temperature, radiation and relative humidity to estimate stigma temperature with better results (RMSE = 1.04 °C) than air temperature alone (RMSE = 2.05 °C). Although the application of these findings for fire blight prediction models was not tested, there is evidence that models that solely rely on air temperature are at risk of errors.

How Does Augmented Observation Facilitate Multimodal Representational Thinking? Applying Deep Learning to Decode Complex Student Construct

In this paper, we demonstrate how machine learning could be used to quickly assess a student’s multimodal representational thinking. Multimodal representational thinking is the complex construct that encodes how students form conceptual, perceptual, graphical, or mathematical symbols in their mind. The augmented reality (AR) technology is adopted to diversify student’s representations. The AR technology utilized a low-cost, high-resolution thermal camera attached to a smartphone which allows students to explore the unseen world of thermodynamics. Ninth-grade students (N = 314) engaged in a prediction–observation–explanation (POE) inquiry cycle scaffolded to leverage the augmented observation provided by the aforementioned device. The objective is to investigate how machine learning could expedite the automated assessment of multimodal representational thinking of heat energy. Two automated text classification methods were adopted to decode different mental representations students used to explain their haptic perception, thermal imaging, and graph data collected in the lab. Since current automated assessment in science education rarely considers multilabel classification, we resorted to the help of the state-of-the-art deep learning technique—bidirectional encoder representations from transformers (BERT). The BERT model classified open-ended responses into appropriate categories with higher precision than the traditional machine learning method. The satisfactory accuracy of deep learning in assigning multiple labels is revolutionary in processing qualitative data. The complex student construct, such as multimodal representational thinking, is rarely mutually exclusive. The study avails a convenient technique to analyze qualitative data that does not satisfy the mutual-exclusiveness assumption. Implications and future studies are discussed.

Thermal Modeling in Metal Additive Manufacturing Using Graph Theory: Experimental Validation With Laser Powder Bed Fusion Using In Situ Infrared Thermography Data

AbstractThe objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history of additively manufactured parts. The graph theory approach for thermal modeling in additive manufacturing (AM) was recently published in these transactions. In the present paper, the graph theory approach is validated with in situ infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize the foregoing objective through the following four tasks. First, two kinds of test shapes, namely, a cylinder and cone, are made in two separate builds on a production-type LPBF machine (Renishaw AM250); the material used for these tests is stainless steel (SAE 316L). The intent of both builds is to influence the thermal history of the part by controlling the cooling time between the melting of successive layers, called the interlayer cooling time (ILCT). Second, layer-wise thermal images of the top surface of the part are acquired using an in situ a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds is used to validate the graph theory-predicted surface temperature trends. Fourth, the surface temperature trends predicted using graph theory are compared with results from finite element (FE) analysis. The results substantiate the computational advantages of the graph theory approach over finite element analysis. As an example, for the cylinder-shaped test part, the graph theory approach predicts the surface temperature trends to within 10% mean absolute percentage error (MAPE) and approximately 16 K root mean squared error (RMSE) relative to the surface temperature trends measured by the thermal camera. Furthermore, the graph theory-based temperature predictions are made in less than 65 min, which is substantially faster than the actual build time of 171 min. In comparison, for an identical level of resolution and prediction error, the finite element approach requires 175 min.

Activity analysis of thermal imaging videos using a difference imaging approach

Infrared thermal imaging is a passive imaging technique that captures the emitted radiation from an object to estimate surface temperature, often for inference of heat transfer. Infrared thermal imaging offers the potential to detect movement without the challenges of glare, shadows, or changes in lighting associated with visual digital imaging or active infrared imaging. In this paper, we employ a frame subtraction algorithm for extracting the pixel-by-pixel relative change in signal from a fixed focus video file, tailored for use with thermal imaging videos. By summing the absolute differences across an entire video, we are able to assign quantitative activity assessments to thermal imaging data for comparison with simultaneous recordings of metabolic rates. We tested the accuracy and limits of this approach by analyzing movement of a metronome and provide an example application of the approach to a study of Darwin's finches. In principle, this "Difference Imaging Thermography" (DIT) would allow for activity data to be standardized to energetic measurements and could be applied to any radiometric imaging system.

Comparative study of Al2O3-YAG:Ce composite ceramic and single crystal YAG:Ce phosphors for high-power laser lighting

Recent studies revealed that both the Al2O3–Y3Al5O12:Ce (YAG:Ce) composite ceramic (YAG-CC) and single crystalline YAG:Ce (YAG-SC) possess very high luminescence-saturation thresholds (LSTs), which makes them promising for laser lighting applications. The present study is aimed to conduct a comparative investigation between YAG-CC and YAG-SC on aspects of the laser-pumped luminescence and the associated thermal behavior. The colorimetric and photometric properties (e.g. luminous flux/efficacy, correlated color temperature, and color rendering index) were investigated systematically, by conducting the measurements using a custom-built sphere-spectroradiometer system in the transmission mode. It was found that YAG-SC has a superior LST and it could maintain a high luminous efficacy (106 lm/W) under the irradiation of a 15.33 W blue laser, while the YAG-CC saturated at around 7.76 W. Moreover, by combining the thermal imaging data with the spectral variation of the YAG-CC, novel experimental insights into the origin of luminescence saturation were revealed and proposed as the “thermally-induced absorption saturation”.

Small Object Bird Detection in Infrared Drone Videos Using Mask R-CNN Deep Learning

A reliable method to estimate population sizes of wild turkeys (Meleagris gallopavo) using unmanned aerial vehicles and thermal video imaging data collected at several field sites in Texas is described. Automating the data processing of airborne survey videos provides a fast and reproducible way to count wild turkeys for wildlife management and conservation. A deep learning semantic segmentation pipeline is developed to detect and count roosting Rio-Grande wild turkeys (M.g. intermedia) which appear as small faint objects in drone-based thermal IR videos. The proposed approach to detect roosting turkeys that appear as small objects, relies on Mask R-CNN, a deep architecture semantic segmentation algorithm. This is followed by a post-processing data association and filtering (DAF) process for counting the number of roosting birds. DAF was used to eliminate false positives like rocks and other small bright objects, which often have noisy detections across temporally adjacent video frames, that can be filtered using appearance association and distance-based gating across time. Transfer learning was used to train the Mask R-CNN network by initializing using ImageNet weights. Drone-based thermal IR videos are extremely challenging due to the complexity of the natural environment including weather effects, occlusion of birds, terrain, trees, complex tree shapes, rocks, water and thermal inversion. The transect videos were collected at night at several times and altitudes to optimize data collection opportunities without disturbing the roosting turkeys. Preliminary performance evaluation using 280 video frames is promising.

INFRARED THERMAL IMAGING AS AN INNOVATIVE APPROACH FOR EARLY DETECTION INFESTATION OF STORED PRODUCT INSECTS IN CERTAIN STORED GRAINS

Grains of field crops, such as wheat, maize, faba bean and white bean, are considered strategic food for humanity worldwide and Egypt. Unfortunately, percent losses of grains quantity may reach to 15-30%, as a result of stored product insect damage, and the losses increased dramatically in the last years, as an outcome of quickly productions of these pests. Experiments were conducted on infrared thermal imaging that demonstrate early detection of infestation by stored product insects in wheat, maize, broad bean, white bean and bean grains. The imaging is dependent on subtle significant differences in temperature between infested and healthy grains. Because the thermal imaging data are digital, computer programs can be used to analysis differences in temperature and mining figures explained for that. Results revealed that the use of thermal imaging offers an alternative method to detect an insect infestation. Data concluded that thermal imaging has the potential to identify whether the grains of crops that tested are infested or not, but is less effective in identifying which developmental stage is present. Moreover, it could apply this technique easily on a large scale in silos, storage, mills and granaries without negative impact on quality of stored grains.

Implementation of Wavelet Analysis on Thermal Images for Affective States Recognition of Children With Autism Spectrum Disorder

Children with Autism Spectrum Disorder are identified as a group of people who has difficulties in socio-emotional interaction. Most of them lack the proper context in producing social response through facial expression and speech. Since emotion is the key for effective social interaction, it is justifiably vital for them to comprehend the correct emotion expressions and recognitions. Emotion is a type of affective states and can be detected through physical reaction and physiological signals. In general, recognition of affective states from physical reaction such as facial expression and speech for autistic children is often unpredictable. Hence, an alternative method of identifying the affective states through physiological signals is proposed. Though considered non-invasive, most of the current recognition methods require sensors to be patched on to the skin body to measure the signals. This would most likely cause discomfort to the children and mask their “true” affective states. The study proposed the use of thermal imaging modality as a passive medium to analyze the physiological signals associated with the affective states nonobtrusively. The study hypothesized that, the impact of cutaneous temperature changes due to the pulsating blood flow in the blood vessels at the frontal face area measured from the modality could have a direct impact to the different affective states of autistic children. A structured experimental setup was designed to measure thermal imaging data generated from different affective state expressions induced using different sets of audio-video stimuli. A wavelet-based technique for pattern detection in time series was deployed to spot the changes measured from the region of interest. In the study, the affective state model for typical developing children aged between 5 and 9 years old was used as the baseline to evaluate the performance of the affective state classifier for autistic children. The results from the classifier showed the efficacy of the technique and accorded good performance of classification accuracy at 88% in identifying the affective states of autistic children. The results were momentous in distinguishing basic affective states and the information could provide a more effective response towards improving social-emotion interaction amongst the autistic children.

GMM-based Automatic Defect Recognition Algorithm for Pressure Vessels Defect Detection through ECPT

In order to realize the automatic identification of pressure vessel defects, an improved adaptive defect recognition feature extraction algorithm through ECPT (Eddy current pulsed thermography) is proposed. The proposed feature extraction algorithm consists of five elements: thermal image data segmentation, variable interval search, probability density function modeling, data classification, and reconstructed image acquisition. The combination of data block selection and variable interval search can reduce the double counting. And the KG-EM (Kmeans-GMM-EM) algorithm is proposed to obtain the Gaussian mixture model corresponding to the classification, and thus the corresponding probability is obtained to classify the TTRs (Transient Thermal Response). The reconstructed thermal image is obtained by the classified TTRs. This method can extract the main information of the image accurately and efficiently. Experimental results are provided to demonstrate their effectiveness.

Об устойчивом приближенном решении одной некорректно поставленной краевой задачи для метагармонического уравнения

In this paper, we consider a mixed problem for a metaharmonic equation in aregion in a rectangular cylinder. On the side faces cylinder region is set to homogeneousconditions of the first kind. The cylindrical area is bounded on one side by an arbitrarysurface on which the Cauchy conditions are set, i. e. the function and its normal derivativeare set. The other boundary of the cylindrical region, which is flat, is free. This problem is illposed,and to construct its approximate solution in the case of Cauchy data known with someerror, it is necessary to use regularizing algorithms. In this paper, the problem is reducedto the Fredholm integral equation of the first kind. Based on the solution of the integralequation, an explicit representation of the exact solution of the problem is obtained. A stablesolution of the integral equation is obtained by the method of Tikhonov regularization. Theextremal of the Tikhonov functional is considered as an approximate solution. Based on thissolution, an approximate solution of the problem as a whole is constructed. The convergencetheorem of the approximate solution of the problem to the exact one is given when the errorin the Cauchy data tends to zero and when the regularization parameter is agreed with theerror in the data. The results can be used for mathematical processing of thermal imagingdata in medical diagnostics.

Evaluation of Beef Cattle Temperament Attributes Using Infrared Thermography Technology

ObjectivesThe objective of this study was to evaluate beef cattle temperament scores using infrared thermography technology.Materials and MethodsAngus and Angus × Hereford calves (total n = 650) were brought through a handling chute system over two weaning sessions (October 2016 and 2017). Beef cattle temperament was subjectively quantified by (1) temperament score (TS), the disposition of the animal observed by an individual evaluator on a scale of 1 (calm) to 5 (excitable); (2) docility score (DS), the level of observed calmness of the animal displayed (1 = calm to 6 = excitable); and (3) qualitative behavior assessment (QBA), scored on twelve different attributes, (i.e., active, relaxed, etc.). There were two traits measured on a four-platform standing scale: (1) the standard deviation of total weight over time (SSD); and (2) the SSD’s coefficient of variation (CVSSD). Thermal images of the animal’s head were acquired by industrial fixed focus infrared camera (TiS40, Fluke Corporation, Everett, WA). The maximum, minimum, average, and standard deviation of temperature of the eye region were extracted from the thermal images. Stepwise and linear regression analyses to estimate subjective temperament traits from thermal imaging data and scale data were conducted using the reg procedure in SAS (v. 9.4, SAS Institute, Inc., Cary, NC). Correlations were estimated using the corr procedure in SAS.ResultsThe results showed low correlations between thermal imaging and subjective temperament traits. The correlations that were significant were around an absolute value of 0.1. However, all four thermal imaging traits were significantly correlated when the animal exhibited distress observed by QBA. When fitting only thermal imaging data into the regression analyses, R2 values were all under 0.03. When including SSD and CVSSD, there were a few traits with an R2 > 0.1 and none having an R2 > 0.15. The QBA traits that had an R2 between 0.1 and 0.15 were active, fearful, calm, apathetic, happy, and distressed for both linear and stepwise regressions.ConclusionAdditional validation research on this thermal imaging technology needs to be conducted with temperamental cattle as this current data was collected using observed calmer cattle, to give a more realistic application to beef cattle production. Overall this result shows potential to achieve beef cattle temperament evaluation with thermal imaging.Figure 1.

Predicting Early Stages of Beef Respiratory Disease Using Thermal Imaging Technology

ObjectivesThe objective of this research was to utilize thermal imaging technology to estimate body temperature, so that an early stage of BRD can be detected.Materials and MethodsNinety-two steers were restrained in a squeeze chute that is housed in an indoor handling facility on 6 separate occasions. At least one image was taken of each side of the head using a thermal infrared camera (FLIR E8 WiFi, FLIR, Wilsonville, OR). The distance at which the images were taken was approximately 1 m from the steer. The rectal temperature was used as the control method to compare thermal imaging data. After thermal image acquisition, images were analyzed using the FLIR ResearchIR Max software (v. 4.40.8.28, FLIR, Wilsonville, OR), with the regions of interest being the eye and nasal cavity.ResultsThe analysis focused on minimum (MIN), maximum (MAX), mean, standard deviation (SD), and range of temperatures in the regions of interest. The REG procedure in SAS (v. 9.4, SAS Institute, Inc., Cary, NC) was used to perform stepwise regression to predict rectal temperature from the outdoor temperature (OTEMP) and all imaging features. When OTEMP was greater than –17.8°C, the regression model contained OTEMP, left nasal MAX, left nasal SD, and left eye MAX temperature and right eye temperature range, with an R2 of 0.24. When OTEMP was above freezing (0°C), the regression model contained left nasal temperature range, right eye temperature range, and average nasal mean temperature, with an R2 increase to 0.50. When using all data, the regression model fit left nasal MAX, right nasal MIN, average nasal mean, and left eye MAX temperatures and right eye temperature range, with an R2 of 0.08. These results show that thermal imaging technology has higher prediction accuracy in warmer temperature ranges than extreme cold conditions.ConclusionMore validation research on this thermal imaging technology needs to be conducted at warmer temperatures since all the current data was collected on cold winter days and a large portion of U.S. cattle are reared in more temperate and warmer areas than North Dakota such as Nebraska, Kansas, Texas, Oklahoma, and Florida. Overall, these results show promise for using thermal imaging technology to help detect BRD in an earlier stage by detecting fever before other clinical signs of BRD are present.

Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures

Rebar corrosion monitoring techniques have been used in studies involving embedded sensors that can detect changes in the corrosion currents and the polarization resistance of rebars in large structures. Defect detection methods that employ infrared cameras are useful non-destructive testing methods to detect defects in concrete structures. However, the measurement results from these methods would vary depending upon several factors that affect thermography. Because of these reasons, they have not been able to provide sufficient reliability. The goal of this study is to develop a technique that uses infrared cameras to quantitatively measure rebar corrosion rates. To examine the impact of the cover thickness, the experiment variables were set at cover thicknesses of 10, 20, and 30 mm and rebar corrosion ratios of 0%, 1%, 3%, 5%, 7%, and 10%. Each variable was tested and a total of 60 specimens were created (i.e., 54 specimens and 6 preliminary specimens). In this study, corrosion was applied using an electrochemical corrosion method that employs Faraday’s law, i.e., the law of conservation of electric charge. The test results of height, width, and area of temperature distribution curve were analyzed, the height of temperature distribution curve was increased as the heating time was grown. In addition, the area of temperature distribution was varied dependent upon the corrosion rate and cover thickness.

Thermal imaging complex with tracking function for joint research of microheterogeneous processes and macrokinetics of SHS phenomenon

The paper presents the features of the method of chrono-topographical analysis of high-resolution thermal imaging data. Robotic foundations of the organization of a thermal imaging complex with a tracking function and the formation of a single chronographic map of the SHS phenomenon based on the results of temperature observations in several areas are described. It is shown that with the same measurement error of the propagation velocity of a SHS wave, a complex with a tracking function has a greater information power than a thermal imaging system with a single observation area. The approbation of the measuring complex during the investigation of SHS in the model Ni-Al system was performed. A joint analysis of heterogeneous processes revealed a similarity in the character of the propagation of combustion centers along the SHS wave front and the motion of the front itself. Based on the chronographic maps of the SHS process, it was found that there were two types of foci in the reaction wave, which differed in the dynamics of the boundary propagation along the front.

High-performance flexible transparent conductive films based on copper nanowires with electroplating welded junctions

In pondering of new promising transparent conductors to replace the cost rising indium tin oxide (ITO), Cu nanowire (CuNW) films have been widely concerned, but both the adhesion and thermostability of CuNWs are very poor. In this paper, highly reliable copper nanowire transparent films on flexible polyethylene terephthalate (PET) substrates, with synchronously enhanced adhesion, improved thermostability and reduced resistivity, are prepared via electroplating welding at room temperature. The sheet resistance of the flexible welded CuNW films reduces from ~200 to ~30 Ω/sq at the transmittance of ~83%. Meanwhile, the sheet resistance does not show significant change after bending and taping test, suggesting a good adhesion of welded CuNW films to the PET substrate. Moreover, the flexible welded CuNW films show good stability to resist long-term storage and high temperature (150 °C). The thermal performance of the flexible welded CuNW films as visible transparent heaters via Joule heating is also assessed. Rapid response times (~30 s) at a low bias voltage (5.5 V) and good thermal resistance characteristics are found using measured thermal imaging data and thermochromic paint printed on top of the transparent heater. This simple, low-cost, and scalable CuNW electroplating welding technique provides an innovative approach for the preparation of next-generation flexible transparent electrodes for future optoelectronic devices.

An investigation of the Temperature Distribution of a Thin Steel Strip during the Quenching Step of a Hardening Process

The dimension quality of the strip within the hardening process is an essential parameter, which great attention needs to be paid. The flatness of the final product is influenced by the temperature distribution of the strip, specifically across the width direction. Therefore, based on physical theories, a numerical model was established. The temperature of the strip for the section before the martensitic transformation was objected in the predicted model by using a steady state approach. In addition an infrared thermal imaging camera was applied in the real process in order to validate the results and to improve the boundary conditions of the numerical model. The results revealed that the temperature of strip decreased up to 250 °C within the area between the furnace and the quenching bath. This, in turn, resulted in significant temperature difference across the width of the strip. This difference can be up to 69 °C and 41 °C according to the numerical results and thermal imaging data, respectively. Overall, this study gave a better insight into the cooling step in the hardening process. In addition, this investigation can be used to improve the hardening process as well as an input for future thermal stress investigations.

Infrared thermography applied to the prediction of structural vibration behaviour

This paper concerns the development of methodology for use of Infrared thermography (IRT) for online prediction of mechanical structural vibration behaviour; given that it has extensively been applied in non-destructive technique for evaluation of surface cracks through the observation of thermal imaging of the vibration-induced crack heat generation. To achieve this, AISI 304 steel cantilever beam coupled with a slipping friction rod was subjected to a forced excitations with an infrared camera capturing the thermal profile at the friction interface. The analysis of thermal image data recorded (radiometric) for the frictional temperature time domain waveform using a MATLAB FFT algorithm in conjunction to IR camera frequency resolution of 120Hz and the use of the heat conduction equation with the help of a finite difference approach successfully identified the structural vibration characteristics in terms of frequency and displacement, the maximum relative errors being 0.09% and 5.85% for frequencies and displacements, respectively. These findings are particularly useful in overcoming many limitations inherent in some of the current vibration measuring techniques in harsh and remote environments.

Differential gene expression and physiological changes during acute or persistent plant virus interactions may contribute to viral symptom differences.

Viruses have different strategies for infecting their hosts. Fast and acute infections result in the development of severe symptoms and may cause the death of the plant. By contrast, in a persistent interaction, the virus can survive within its host for a long time, inducing only mild symptoms. In this study, we investigated the gene expression changes induced in CymRSV-, crTMV-, and TCV-infected Nicotiana benthamiana and in PVX- and TMV-U1-infected Solanum lycopersicum plants after the systemic spread of the virus by two different high-throughput methods: microarray hybridization or RNA sequencing. Using these techniques, we were able to clearly differentiate between acute and persistent infections. We validated the gene expression changes of selected genes by Northern blot hybridization or by qRT-PCR. We show that, in contrast to persistent infections, the drastic shut-off of housekeeping genes, downregulation of photosynthesis-related transcripts and induction of stress genes are specific outcomes with acute infections. We also show that these changes are not a consequence of host necrosis or the presence of a viral silencing suppressor. Thermal imaging data and chlorophyll fluorescence measurements correlated very well with the molecular changes. We believe that the molecular and physiological changes detected during acute infections mostly contribute to virus symptom development. The observed characteristic physiological changes associated with economically more dangerous acute infections could serve as a basis for the elaboration of remote monitoring systems suitable for detecting developing virus infections in crops. Moreover, as molecular and physiological changes are characteristics of different types of virus lifestyles, this knowledge can support risk assessments of recently described novel viruses.