scholarly journals Floral infrared emissivity estimates using simple tools

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Michael J. M. Harrap ◽  
Sean A. Rands
Keyword(s):  
Thermal Imaging ◽  
Plant Biology ◽  
Water Bath ◽  
Infrared Emissivity ◽  
Thermal Camera ◽  
Vegetative Tissue ◽  
Floral Tissue ◽  
A Value ◽  
Accurate Temperature ◽  
Thermal Infrared Radiation

Abstract Background Floral temperature has important consequences for plant biology, and accurate temperature measurements are therefore important to plant research. Thermography, also referred to as thermal imaging, is beginning to be used more frequently to measure and visualize floral temperature. Accurate thermographic measurements require information about the object’s emissivity (its capacity to emit thermal radiation with temperature), to obtain accurate temperature readings. However, there are currently no published estimates of floral emissivity available. This is most likely to be due to flowers being unsuitable for the most common protocols for emissivity estimation. Instead, researchers have used emissivity estimates collected on vegetative plant tissue when conducting floral thermography, assuming these tissues to have the same emissivity. As floral tissue differs from vegetative tissue, it is unclear how appropriate and accurate these vegetative tissue emissivity estimates are when they are applied to floral tissue. Results We collect floral emissivity estimates using two protocols, using a thermocouple and a water bath, providing a guide for making estimates of floral emissivity that can be carried out without needing specialist equipment (apart from the thermal camera). Both protocols involve measuring the thermal infrared radiation from flowers of a known temperature, providing the required information for emissivity estimation. Floral temperature is known within these protocols using either a thermocouple, or by heating the flowers within a water bath. Emissivity estimates indicate floral emissivity is high, near 1, at least across petals. While the two protocols generally indicated the same trends, the water bath protocol gave more realistic and less variable estimates. While some variation with flower species and location on the flower is observed in emissivity estimates, these are generally small or can be explained as resulting from artefacts of these protocols, relating to thermocouple or water surface contact quality. Conclusions Floral emissivity appears to be high, and seems quite consistent across most flowers and between species, at least across petals. A value near 1, for example 0.98, is recommended for accurate thermographic measurements of floral temperature. This suggests that the similarly high values based on vegetation emissivity estimates used by previous researchers were appropriate.

scholarly journals Assessing the Performance of a Low-Cost Thermal Camera in Proximal and Aerial Conditions

2020 ◽  
Vol 12 (21) ◽  
pp. 3591
Author(s):  
Matheus Gabriel Acorsi ◽  
Leandro Maria Gimenez ◽  
Maurício Martello
Keyword(s):  
Thermal Imaging ◽  
Low Cost ◽  
Temperature Measurements ◽  
Aerial Images ◽  
Thermal Camera ◽  
Calibration Models ◽  
Accurate Temperature ◽  
Thermal Cameras ◽  
Accuracy Indices ◽  
Better Than

The development of low-cost miniaturized thermal cameras has expanded the use of remotely sensed surface temperature and promoted advances in applications involving proximal and aerial data acquisition. However, deriving accurate temperature readings from these cameras is often challenging due to the sensitivity of the sensor, which changes according to the internal temperature. Moreover, the photogrammetry processing required to produce orthomosaics from aerial images can also be problematic and introduce errors to the temperature readings. In this study, we assessed the performance of the FLIR Lepton 3.5 camera in both proximal and aerial conditions based on precision and accuracy indices derived from reference temperature measurements. The aerial analysis was conducted using three flight altitudes replicated along the day, exploring the effect of the distance between the camera and the target, and the blending mode configuration used to create orthomosaics. During the tests, the camera was able to deliver results within the accuracy reported by the manufacturer when using factory calibration, with a root mean square error (RMSE) of 1.08 °C for proximal condition and ≤3.18 °C during aerial missions. Results among different flight altitudes revealed that the overall precision remained stable (R² = 0.94–0.96), contrasting with the accuracy results, decreasing towards higher flight altitudes due to atmospheric attenuation, which is not accounted by factory calibration (RMSE = 2.63–3.18 °C). The blending modes tested also influenced the final accuracy, with the best results obtained with the average (RMSE = 3.14 °C) and disabled mode (RMSE = 3.08 °C). Furthermore, empirical line calibration models using ground reference targets were tested, reducing the errors on temperature measurements by up to 1.83 °C, with a final accuracy better than 2 °C. Other important results include a simplified co-registering method developed to overcome alignment issues encountered during orthomosaic creation using non-geotagged thermal images, and a set of insights and recommendations to reduce errors when deriving temperature readings from aerial thermal imaging.

scholarly journals Thermal Imaging Metrology Using High Dynamic Range Near-Infrared Photovoltaic-Mode Camera

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6151
Author(s):  
Thomas B. O. Rockett ◽  
Nicholas A. Boone ◽  
Robert D. Richards ◽  
Jon R. Willmott
Keyword(s):  
Measurement Uncertainty ◽  
Wide Temperature Range ◽  
Thermal Imaging ◽  
Near Infrared ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Thermal Camera ◽  
Temperature Range ◽  
Photovoltaic Mode ◽  
High Dynamic

The measurement of a wide temperature range in a scene requires hardware capable of high dynamic range imaging. We describe a novel near-infrared thermal imaging system operating at a wavelength of 940 nm based on a commercial photovoltaic mode high dynamic range camera and analyse its measurement uncertainty. The system is capable of measuring over an unprecedently wide temperature range; however, this comes at the cost of a reduced temperature resolution and increased uncertainty compared to a conventional CMOS camera operating in photodetective mode. Despite this, the photovoltaic mode thermal camera has an acceptable level of uncertainty for most thermal imaging applications with an NETD of 4–12 °C and a combined measurement uncertainty of approximately 1% K if a low pixel clock is used. We discuss the various sources of uncertainty and how they might be minimised to further improve the performance of the thermal camera. The thermal camera is a good choice for imaging low frame rate applications that have a wide inter-scene temperature range.

scholarly journals An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

2020 ◽  
Vol 3 (1) ◽  
pp. 13 ◽  
Author(s):  
Tareq Khan
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Thermal Imaging ◽  
Closed Loop ◽  
Thermal Image ◽  
Microwave Oven ◽  
Closed Loop Control ◽  
Target Temperature ◽  
Thermal Camera ◽  
Loop Control

Whenever food in a microwave oven is heated, the user estimates the time to heat. This estimation can be incorrect, leading the food to be too hot or still cold. In this research, an intelligent microwave oven is designed. After the food is put into the microwave oven and the door is closed, it captures the image of the food, classifies the image and then suggests the food’s target temperature by learning from previous experiences, so the user does not have to recall the target food temperature each time the same food is warmed. The temperature of the food is measured using a thermal camera. The proposed microwave incorporates a display to show a real-time colored thermal image of the food. The microwave automatically stops the heating when the temperature of the food hits the target temperature using closed-loop control. The deep learning-based image classifier gradually learns the type of foods that are consumed in that household and becomes smarter in temperature recommendation. The system can classify and recommend target temperature with 93% accuracy. A prototype is developed using a microcontroller-based system and successfully tested.

Innovative Air Leakage Test Method for B-Cars

2017 ◽  
Author(s):  
Erdem Altug ◽  
Ayşe Ece Yildizcelik ◽  
Burak Birinkulu
Keyword(s):  
Thermal Imaging ◽  
Test Method ◽  
Imaging Method ◽  
Air Leakage ◽  
Thermal Camera ◽  
Air Leak ◽  
Test Device ◽  
Wind Noise ◽  
Leakage Test ◽  
Air Leak Test

Ford Otosan’s Yeniköy plant, which assembles the Transit Courier and Tourneo Courier, has developed a innovative air leakage test method. Air leakage is not a directly measurable metric by customer, but it can be observed in some different ways such as wind noise, road noise, water ingress, dust intrusion, door closing efforts, and better cooling/heating performance. In order to detect leak point by conventional method is dramatically time consuming, needed to remove trim parts and issues under 11/s could not be detectable. Innovative air leakage test method has been designed by using air heater unit, air leak test device and thermal camera. By the assistance of thermal imaging method air leak rate has been improved.

scholarly journals Application of G100/120 thermal imaging camera in energy efficiency measuring in building construction

2013 ◽  
Vol 10 (1) ◽  
pp. 153-164 ◽  
Author(s):  
Aleksandra Pavlovic ◽  
Zarko Barbaric
Keyword(s):  
Energy Efficiency ◽  
Construction Industry ◽  
Thermal Imaging ◽  
Thermal Camera ◽  
Infrared Thermal Imaging ◽  
Imaging Camera ◽  
Areas Of Interest ◽  
New Buildings ◽  
Design Construction

A measurement of energy efficiency in the construction industry aims to reduce permanently energy requirements in design, construction and use of new buildings, sannation and reconstruction of the existing ones. Over the long term, with the expected increasing in the price of energy and the development of awareness about energy conservation and environmental protection, thermal imaging methods will certainly find their wide application in the construction industry. Infrared thermal camera is used to estimate the temperature. However, we do not see all the details of interest on the thermal image, therefore we suggest using the fusion of visual and thermal images of the same part of the building recorded simultaneously. The analysis images of the object obtained by fusion of television and infrared thermal imaging shows the areas of interest for further processing.

scholarly journals Deep Learning Based Super Resolution and Classification Applications for Neonatal Thermal Images

2021 ◽  
Vol 38 (5) ◽  
pp. 1361-1368
Author(s):  
Fatih M. Senalp ◽  
Murat Ceylan
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Thermal Imaging ◽  
Similarity Index ◽  
Super Resolution ◽  
Ground Truth ◽  
Structural Similarity ◽  
Generative Adversarial Networks ◽  
Thermal Camera ◽  
Thermal Images

The thermal camera systems can be used in all kinds of applications that require the detection of heat change, but thermal imaging systems are highly costly systems. In recent years, developments in the field of deep learning have increased the success by obtaining quality results compared to traditional methods. In this paper, thermal images of neonates (healthy - unhealthy) obtained from a high-resolution thermal camera were used and these images were evaluated as high resolution (ground truth) images. Later, these thermal images were downscaled at 1/2, 1/4, 1/8 ratios, and three different datasets consisting of low-resolution images in different sizes were obtained. In this way, super-resolution applications have been carried out on the deep network model developed based on generative adversarial networks (GAN) by using three different datasets. The successful performance of the results was evaluated with PSNR (peak signal to noise ratio) and SSIM (structural similarity index measure). In addition, healthy - unhealthy classification application was carried out by means of a classifier network developed based on convolutional neural networks (CNN) to evaluate the super-resolution images obtained using different datasets. The obtained results show the importance of combining medical thermal imaging with super-resolution methods.

scholarly journals Thermal imaging in skin trauma evaluation: observations by CAT S60 mobile phone

2018 ◽  
Vol 10 (2-3) ◽  
Author(s):  
Jarmo Alametsä ◽  
Markku Oikarainen ◽  
Jarmo Perttunen ◽  
Jari Viik ◽  
Annikki Vaalasti
Keyword(s):  
Mobile Phone ◽  
Thermal Imaging ◽  
Smart Phone ◽  
Home Care Services ◽  
Lower Limbs ◽  
Skin Damage ◽  
Thermal Camera ◽  
Temperature Changes ◽  
Care Services ◽  
Skin Damages

The purpose of this study was to evaluate the usability of a mobile phone with inbuilt thermal camera in wound imaging for medical purposes. Thermal imaging could help in evaluating wound healing and in assisting doctors in diagnose making. By using CAT S60 smart phone with an inbuilt Flir thermal camera, thermal pictures from skin wounds and lower limbs were taken from six people in order to find out if thermal imaging could help the treatment and diagnosis of a patient. Thermal images were taken in order to find and visualize temperature changes (being normally invisible) in skin damage areas including deep skin damages especially from limbs and extremities. By using thermal imaging the beginning of treatment could be hastened and the monitoring of the state of a patient would be more efficient thus improving the prognosis of a patient. The thermal pictures taken from skin damages suggest that thermal imaging with CAT S60 smart phone can be used to improve nursing methods and may also help in diagnosis. Non-invasive thermal imaging may be a valuable asset and for its part hasten the beginning of treatment. The resolution and properties of CAT S60 smart phone was sufficient to detect skin damage temperature changes. This may suggest the usage of the CAT S60 smart in hospital, emergency ward and in home care services.

scholarly journals A.I. Driven Thermal People Counting for Smart Window Facade Using Portable Low‐Cost Miniature Thermal Imaging Sensors

2020 ◽  
Author(s):  
Moveh Samuel ◽  
Samuel-soma M Ajibade ◽  
Fred Fudah Moveh
Keyword(s):  
Thermal Imaging ◽  
Air Conditioning ◽  
Low Cost ◽  
Thermal Camera ◽  
Smart Window ◽  
People Counting ◽  
The People ◽  
Thermal Cameras ◽  
Pass Through ◽  
Imaging Sensors

People counting applications have been used in diverse applications. The ability and accuracy of thermal imaging over conventional image cameras has led to the implementation of thermal cameras in people counting applications. This paper present a thermal people counting smart glass windows. The people counting application would be remotely monitored from a single centralized PC station as it’s connected to a multiplex of mass monitoring of 20 thermal camera, all embedded into different glass windows. The thermal cameras would then be able to detect body temperatures of all individuals who pass through any of the camera range and also count the numbers of people who passed through the camera range. The data gotten can then be further utilized in various ways, example is in the control of air conditioning and lightening.

scholarly journals Improving nursing methods by using thermal imaging: Observations by CAT S60 mobile phone

2017 ◽  
Vol 9 (2-3) ◽  
pp. 74 ◽  
Author(s):  
Jarmo Alametsä ◽  
Markku Oikarainen ◽  
Jari Viik ◽  
Jarmo Perttunen
Keyword(s):  
Thermal Imaging ◽  
Cellular Phone ◽  
Home Care Services ◽  
Whole Body ◽  
Thermal Camera ◽  
Thermal Images ◽  
Temperature Changes ◽  
Non Invasive ◽  
Care Services ◽  
Human Body Temperature

The purpose of this preliminary study was to examine, how thermal imaging could help in advancing nursing methods and offer some new usage targets of thermal imaging for the behoof of a patient. By using CAT S60 cellular phone with an inbuilt Flir thermal camera, thermal pictures were taken from voluntary subjects in order to find out if thermal imaging with CAT S60 phone could help in treatment of a patient. Thermal camera images were taken in order to find out temperature changes in whole body, limbs and extremities. By using thermal imaging in nursing the beginning of treatment could be hastened and the monitoring of the state of a patient would be more efficient thus improving the prognosis of a patient. The benefit of the method is, that it is non-invasive, cheap and easy to use (inside a cellular phone) thus being a clear advantage.  The results of different usage methods seen in thermal images suggest that thermal imaging with CAT S60 phone could be used to improve nursing methods and may also for its part to help in diagnosis. The present preliminary observations via thermal images showed, that the resolution of CAT S60 phone was sufficient to detect changes in human body temperature in home life. This may suggest the usage of the CAT S60 phone in home care services.

Thermal Imaging Technique to Minimize the Wastage of Fruits

2019 ◽  
Author(s):  
Sathish K. Gurupatham ◽  
Carson Wiles
Keyword(s):  
Environmental Conditions ◽  
Thermal Imaging ◽  
Skin Color ◽  
Imaging Technique ◽  
Specific Time ◽  
Thermal Camera ◽  
Skin Texture ◽  
Thermal Images ◽  
Non Invasive ◽  
Unripe Fruits

Abstract This study is aimed at using non-invasive thermal imaging technique to assess fruit ripeness, including fruits which maintain skin color throughout ripening. The same sized four unripe fruits of avocado, kiwi, and peach from each variety were chosen with the same skin texture, color, and firmness in a batch for the study, for a total of four batches. Thermal images of these fruits were captured using a thermal camera for three consecutive days at a specific time under the same environmental conditions. The thermal images show that the temperature of fruits increases along with their ripeness level during ripening which happens due to the respiration of the fruits. The specific heat which is a function of temperature was calculated experimentally using a method developed by Hwang and Hayakama for five unripe and ripe fruits of each variety with the same ripeness level, skin texture, color, and firmness which confirmed this increase. This work demonstrates that thermal imaging technique is preferable and non-invasive for evaluation of the ripeness of fruits, especially which do not change their skin color during ripening such as kiwi.

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