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1
de Souza, Romina, Claudia Buchhart, Kurt Heil, Jürgen Plass, Francisco M. Padilla, and Urs Schmidhalter. "Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV." Remote Sensing 13, no. 9 (2021): 1691. http://dx.doi.org/10.3390/rs13091691.
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Optical sensors have been widely reported to be useful tools to assess biomass, nutrition, and water status in several crops. However, the use of these sensors could be affected by the time of day and sky conditions. This study aimed to evaluate the effect of time of day and sky conditions (sunny versus overcast) on several vegetation indices (VI) calculated from two active sensors (the Crop Circle ACS-470 and Greenseeker RT100), two passive sensors (the hyperspectral bidirectional passive spectrometer and HandySpec Field sensor), and images taken from an unmanned aerial vehicle (UAV). The experimental work was conducted in a wheat crop in south-west Germany, with eight nitrogen (N) application treatments. Optical sensor measurements were made throughout the vegetative growth period on different dates in 2019 at 9:00, 14:00, and 16:00 solar time to evaluate the effect of time of day, and on a sunny and overcast day only at 9:00 h to evaluate the influence of sky conditions on different vegetation indices. For most vegetation indices evaluated, there were significant differences between paired time measurements, regardless of the sensor and day of measurement. The smallest differences between measurement times were found between measurements at 14:00 and 16:00 h, and they were observed for the vehicle-carried and the handheld hyperspectral passive sensor being lower than 2% and 4%, respectively, for the indices NIR/Red edge ratio, Red edge inflection point (REIP), and the water index. Differences were lower than 5% for the vehicle-carried active sensors Crop Circle ACS-470 (indices NIR/Red edge and NIR/Red ratios, and NDVI) and Greenseeker RT100 (index NDVI). The most stable indices over measurement times were the NIR/Red edge ratio, water index, and REIP index, regardless of the sensor used. The most considerable differences between measurement times were found for the simple ratios NIR/Red and NIR/Green. For measurements made on a sunny and overcast day, the most stable were the indices NIR/Red edge ratio, water index, and REIP. In practical terms, these results confirm that passive and active sensors could be used to measure on-farm at any time of day from 9:00 to 16:00 h by choosing optimized indices.
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Ou, Fang, Anne van Klinken, Petar Ševo, et al. "Handheld NIR Spectral Sensor Module Based on a Fully-Integrated Detector Array." Sensors 22, no. 18 (2022): 7027. http://dx.doi.org/10.3390/s22187027.
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For decades, near-infrared (NIR) spectroscopy has been a valuable tool for material analysis in a variety of applications, ranging from industrial process monitoring to quality assessment. Traditional spectrometers are typically bulky, fragile and expensive, which makes them unsuitable for portable and in-field use. Thus, there is a growing interest for miniaturized, robust and low-cost NIR sensors. In this study, we demonstrate a handheld NIR spectral sensor module, based on a fully-integrated multipixel detector array, sensitive in the 850–1700 nm wavelength range. Differently from a spectrometer, the spectral sensor measures a limited number of NIR spectral bands. The capabilities of the spectral sensor module were evaluated alongside a commercially available portable spectrometer for two application cases: to quantify the moisture content in rice grains and to classify plastic types. Both devices achieved the two sensing tasks with comparable performance. Moisture quantification was achieved with a root mean square error (RMSE) prediction of 1.4% and 1.1% by the spectral sensor and spectrometer, respectively. Classification of the plastic type was achieved with a prediction accuracy on unknown samples of 100% and 96.4% by the spectral sensor and spectrometer, respectively. The results from this study are promising and demonstrate the potential for the compact NIR modules to be used in a variety of NIR sensing applications.
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Milella, A., M. Nielsen, and G. Reina. "Sensing in the visible spectrum and beyond for terrain estimation in precision agriculture." Advances in Animal Biosciences 8, no. 2 (2017): 423–29. http://dx.doi.org/10.1017/s2040470017000152.
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A multi-sensor approach for terrain estimation is proposed using a combination of complementary optical sensors that cover the visible (VIS), near infrared (NIR) and infrared (IR) spectrum. The sensor suite includes a stereovision sensor, a VIS-NIR camera and a thermal camera, and it is intended to be mounted on board an agricultural vehicle, pointing downward to scan the portion of the terrain ahead. A method to integrate the different sensor data and create a multi-modal dense 3D terrain map is presented. The stereovision input is used to generate 3D point clouds that incorporate RGB-D information, whereas the VIS-NIR camera and the thermal sensor are employed to extract respectively spectral signatures and temperature information, to characterize the nature of the observed surfaces. Experimental tests carried out by an off-road vehicle are presented, showing the feasibility of the proposed approach.
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Gianquinto, Giorgio, Francesco Orsini, Giuseppina Pennisi, and Stefano Bona. "Sources of Variation in Assessing Canopy Reflectance of Processing Tomato by Means of Multispectral Radiometry." Sensors 19, no. 21 (2019): 4730. http://dx.doi.org/10.3390/s19214730.
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Canopy reflectance sensors are a viable technology to optimize the fertilization management of crops. In this research, canopy reflectance was measured through a passive sensor to evaluate the effects of either crop features (N fertilization, soil mulching, appearance of red fruits, and cultivars) or sampling methods (sampling size, sensor position, and hour of sampling) on the reliability of vegetation indices (VIs). Sixteen VIs were derived, including seven simple wavelength reflectance ratios (NIR/R460, NIR/R510, NIR/R560, NIR/R610, NIR/R660, NIR/R710, NIR/R760), seven normalized indices (NDVI, G-NDVI, MCARISAVI, OSAVI, TSAVI, TCARI), and two combined indices (TCARI/OSAVI; MCARI/OSAVI). NIR/560 and G-NDVI (Normalized Difference Vegetation Index on Greenness) were the most reliable in discriminating among fertilization rates, with results unaffected by the appearance of maturing fruits, and the most stable in response to different cultivars. Black mulching film did not affect NIR/560 and G-NDVI behavior at the beginning of the growing season, when the crop is more responsive to N management. Due to a moderate variability of NIR/560 and G-NDVI, a small sample size (5–10 observations) is sufficient to obtain reliable measurements. Performing the measurements at 11:00 and 14:00 and maintaining a greater distance (1.8 m) between plants and instrument enhanced measurement consistency. Accordingly, NIR/560 and G-NDVI resulted in the most reliable VIs.
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Jo and Kim. "NIR Reflection Augmentation for DeepLearning-Based NIR Face Recognition." Symmetry 11, no. 10 (2019): 1234. http://dx.doi.org/10.3390/sym11101234.
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Face recognition using a near-infrared (NIR) sensor is widely applied to practical applications such as mobile unlocking or access control. However, unlike RGB sensors, few deep learning approaches have studied NIR face recognition. We conducted comparative experiments for the application of deep learning to NIR face recognition. To accomplish this, we gathered five public databases and trained two deep learning architectures. In our experiments, we found that simple architecture could have a competitive performance on the NIR face databases that are mostly composed of frontal face images. Furthermore, we propose a data augmentation method to train the architectures to improve recognition of users who wear glasses. With this augmented training set, the recognition rate for users who wear glasses increased by up to 16%. This result implies that the recognition of those who wear glasses can be overcome using this simple method without constructing an additional training set. Furthermore, the model that uses augmented data has symmetry with those trained with real glasses-wearing data regarding the recognition of people who wear glasses.
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Pereira, Luciana Escalante, Giancarlo Lastoria, Bruna Semler de Almeida, et al. "APPLICATION OF AERIAL AND ORBITAL SENSOR PHOTOGRAPHS TO IDENTIFY AND DELINEATE WATER BODIES." Boletim de Ciências Geodésicas 23, no. 4 (2017): 591–605. http://dx.doi.org/10.1590/s1982-21702017000400039.
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Abstract: The application of orbital sensors to identify and delineate water bodies was evaluated in this study. Reference aerial photos were used to measure the surface area of three water bodies in São Gabriel do Oeste, MS, Brazil and assess seven sensors commonly used in environmental studies: ALOS-AVNIR, CBERS 2B-CCD, CBERS 2B-HRC, IRS P6-LISS3, LANDSAT-TM, LANDSAT-ETM+, and LANDSAT-OLI. The images were analyzed with the near infrared (NIR) band, and digital processing techniques including image fusion (spatial enhancement), false-color composition, and pre-processed radiometric correction were applied to some sensors. Image fusion and radiometric correction were applied to three sensors; only color composition was not conducted on the HRC sensor. In all water bodies analyzed, images from the CCD sensor showed the greatest values of imprecision, reaching 192% for Water Body #3 without digital processing. Considering the spectral properties of the NIR band, we expected more precise data from the analyses using this spectral range. However, color composite analyses obtained greater percent precision compared with analyses that only used the NIR band.
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Zhu, Banghe, and Henry Jonathan. "A Review of Image Sensors Used in Near-Infrared and Shortwave Infrared Fluorescence Imaging." Sensors 24, no. 11 (2024): 3539. http://dx.doi.org/10.3390/s24113539.
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To translate near-infrared (NIR) and shortwave infrared (SWIR) fluorescence imaging into the clinic, the paired imaging device needs to detect trace doses of fluorescent imaging agents. Except for the filtration scheme and excitation light source, the image sensor used will finally determine the detection limitations of NIR and SWIR fluorescence imaging systems. In this review, we investigate the current state-of-the-art image sensors used in NIR and SWIR fluorescence imaging systems and discuss the advantages and limitations of their characteristics, such as readout architecture and noise factors. Finally, the imaging performance of these image sensors is evaluated and compared.
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Sharma, Anuj K., Ankit Kumar Pandey, and Baljinder Kaur. "Fluoride Fiber-Based Plasmonic Biosensor with Two-Dimensional Material Heterostructures: Enhancement of Overall Figure-of-Merit via Optimization of Radiation Damping in Near Infrared Region." Materials 12, no. 9 (2019): 1542. http://dx.doi.org/10.3390/ma12091542.
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Two-dimensional (2D) heterostructure materials show captivating properties for application in surface plasmon resonance (SPR) sensors. A fluoride fiber-based SPR sensor is proposed and simulated with the inclusion of a 2D heterostructure as the analyte interacting layer. The monolayers of two 2D heterostructures (BlueP/MoS2 and BlueP/WS2, respectively) are considered in near infrared (NIR). In NIR, an HBL (62HfF4-33BaF2-5LaF3) fluoride glass core and NaF clad are considered. The emphasis is placed on figure of merit (FOM) enhancement via optimization of radiation damping through simultaneous tuning of Ag thickness (dm) and NIR wavelength (λ) at the Ag-2D heterostructure–analyte interfaces. Field distribution analysis is performed in order to understand the interaction of NIR signal with analyte at optimum radiation damping (ORD) condition. While the ORD leads to significantly larger FOM for both, the BlueP/MoS2 (FOM = 19179.69 RIU−1 (RIU: refractive index unit) at dm = 38.2 nm and λ = 813.4 nm)-based sensor shows massively larger FOM compared with the BlueP/WS2 (FOM = 7371.30 RIU−1 at dm = 38.2 nm and λ = 811.2 nm)-based sensor. The overall sensing performance was more methodically evaluated in terms of the low degree of photodamage of the analyte, low signal scattering, high power loss, and large field variation. The BlueP/MoS2-based fiber SPR sensor under ORD conditions opens up new paths for biosensing with highly enhanced overall performance.
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Ren, Guangxin, Xusheng Zhang, Rui Wu, Lingling Yin, Wenyan Hu, and Zhengzhu Zhang. "Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors." Biosensors 13, no. 1 (2023): 92. http://dx.doi.org/10.3390/bios13010092.
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The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO−SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.
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Cho, Yongjin, Kenneth A. Sudduth, and Scott T. Drummond. "Profile Soil Property Estimation Using a VIS-NIR-EC-Force Probe." Transactions of the ASABE 60, no. 3 (2017): 683–92. http://dx.doi.org/10.13031/trans.12049.
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Abstract. Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa, and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R2 = 0.32, 0.67, and 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in WC estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.
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Lv, Chaogeng, Yali He, Chuanzhi Kang, et al. "Tracing the Geographical Origins of Dendrobe (Dendrobium spp.) by Near-Infrared Spectroscopy Sensor Combined with Porphyrin and Chemometrics." Journal of Analytical Methods in Chemistry 2020 (September 12, 2020): 1–8. http://dx.doi.org/10.1155/2020/8879957.
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Dendrobe (Dendrobium spp.) is a traditional medicinal and edible food, which is rich in nutrients and contains biologically active metabolites. The quality and price of dendrobe are related to its geographical origins, and high quality dendrobe is often imitated by low quality dendrobe in the market. In this work, near-infrared (NIR) spectroscopy sensor combined with porphyrin and chemometrics was used to distinguish 360 dendrobe samples from twelve different geographical origins. Partial least squares discriminant analysis (PLSDA) was used to study the sensing performance of traditional NIR and tera-(4-methoxyphenyl)-porphyrin (TMPP)-NIR on the identification of dendrobe origin. In the PLSDA model, the recognition rate of the training and prediction set of the TMPP-NIR could reach 100%, which was higher than the 91.85% and 91.34% of traditional NIR. And the accuracy, sensitivity, and specificity of the TMPP-NIR sensor are all 1.00. The mechanism of TMPP improving the specificity of NIR spectroscopy should be related to the π-π conjugated system and the methoxy groups of TMPP interact with the chemical components of dendrobe. This study reflected that NIR spectrum with TMPP sensor was an effective approach for identifying the geographic origin of dendrobe.
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Xie, Xinhui. "Designing Photoelectric Sensor Module in Infrared Communication and Applying Wireless Sensor Network in Leakage Cable Detection." Journal of Nanoelectronics and Optoelectronics 18, no. 2 (2023): 193–201. http://dx.doi.org/10.1166/jno.2023.3377.
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With technological advancement, Near-infrared (NIR) spectroscopy instruments have gradually entered people’s daily life and production with their unique performance. Therefore, it has a positive significance in researching NIR spectroscopy analysis technology. This work designs a refrigerating Lead Sulfide (PbS) NIR photoelectric sensor P2664-05 and its photoelectric signal conversion circuit. The converted voltage signal is used for subsequent circuit processing through the amplification and frequency selection circuit. The aim is to ensure the sensor’s corresponding detectable spectral wavelength, sensitivity, and signal-to-noise ratio are improved under low temperatures. To this end, the refrigeration current generation circuit is designed. The refrigerating temperature is set below −15 °C, and the refrigerating current is 500 mA. At the same time, it is necessary to monitor whether the sensor’s temperature state meets the system stability requirements during the refrigeration process. The designed sensor module is connected to the instrument system in the test, and the whiteboard without spectral absorption is scanned. The results show that the designed NIR sensor’s shortcomings in noise processing performance have been improved using a System on a Programmable Chip (SoPC) as a processor. An infrared communication node is designed using its built-in digital resources and TFDU infrared communication chip. The designed NIR sensor and communication node are added to the 485 bus to build a communication network. In leakage cable detection, the communication network has higher security, lower error rate, and lower power consumption.
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Wang, Di, Lin Xie, Simon Yang, and Fengchun Tian. "Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors." Sensors 18, no. 10 (2018): 3222. http://dx.doi.org/10.3390/s18103222.
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Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.
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Eberl, R., B. Parthey, and J. Wilke. "A near Infrared Spectroscopic Sensor for the Monitoring of Brewing Processes." Journal of Near Infrared Spectroscopy 6, A (1998): A133—A140. http://dx.doi.org/10.1255/jnirs.181.
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The use of near infrared (NIR) analysis in the on-line process control of beer production (brewing) is exemplary reviewed. The real-time monitoring of both the mashing and fermentation processes appears to be an interesting but still unsolved problem, mainly due to the high and varying content of solid particles in the respective fluids. Intrinsic sensors, based on the effect of Attenuated Total Reflection, offer a possibility for even in-line measurements in such cases. U-shaped intrinsic sensors were fabricated of fused silica rods with a diameter of 3 mm and coupled to a NIR spectrometer via two fibre bundles. The sensor spectra turned out to be similar to transmission spectra of an optical pathlength of about twenty micrometers, confirming theoretical values obtained by an extended raytracing calculation. Spectra of brewing fluids were taken during the mashing and fermentation process, respectively, and evaluated by PLS. The sensor performance allows to monitor the progress of both processes in terms of the sugar and the ethanol production, respectively. However, further work is necessary to realise a proper industrial sensor.
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Pathiranage, Dinithi Siriwardana, Larry Leigh, and Cibele Teixeira Pinto. "Evaluation of Low-Cost Radiometer for Surface Reflectance Retrieval and Orbital Sensor’s Validation." Remote Sensing 15, no. 9 (2023): 2444. http://dx.doi.org/10.3390/rs15092444.
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This paper evaluates the Arable Mark 2 sensor, an automated and low-cost radiometer, for its potential to retrieve surface reflectance data and validate orbital sensors such as the Landsat-8 (L8) Operational Land Imager (OLI) Level 2 product. While orbital sensors are widely used for monitoring solar radiation changes, managing natural resources, and understanding climatic trends, atmospheric effects can make it challenging to obtain accurate measurements. Equipped with multiple sensors, including long-wave and short-wave radiometers, the Arable Mark 2 sensor can measure upwelling and downwelling irradiance to calculate surface reflectance. To assess the accuracy and consistency of the Arable Mark 2 sensor, the study performed a cross-calibration using a ground truth measurement collected with the Analytical Spectral Device (ASD) as the reference point. Additionally, a spectral band adjustment factor (SBAF) was applied across the calibrated Arable surface reflectance to compare it against the orbital sensor. An automated library aided in calculating SBAF for the days with unavailable hyperspectral data. The study found that the Arable Mark 2 sensor can provide accurate surface reflectance data that can be used for orbital sensor validation. The Arable sensor was successfully calibrated against the ASD FieldSpec with an average difference of less than 1/10 reflectance unit (reflectance unit = 0.01) for the blue, green, yellow, and red bands. The red-edge and NIR-1 bands showed an average difference of less than 1/2 reflectance units, while the NIR-2 band had an average difference of less than 1/10 reflectance unit of calibration accuracy. The calibrated Arable surface reflectance data was then compared against orbital sensor surface reflectance data, and the results showed good agreement between the two datasets. The study concludes that the low-cost and automated nature of the Arable Mark 2 sensor makes it a promising tool for surface reflectance retrieval and orbital sensor validation.
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Althobaiti, Murad. "In Silico Investigation of SNR and Dermis Sensitivity for Optimum Dual-Channel Near-Infrared Glucose Sensor Designs for Different Skin Colors." Biosensors 12, no. 10 (2022): 805. http://dx.doi.org/10.3390/bios12100805.
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Diabetes is a serious health condition that requires patients to regularly monitor their blood glucose level, making the development of practical, compact, and non-invasive techniques essential. Optical glucose sensors—and, specifically, NIR sensors—have the advantages of being non-invasive, compact, inexpensive, and user-friendly devices. However, these sensors have low accuracy and are yet to be adopted by healthcare providers. In our previous work, we introduced a non-invasive dual-channel technique for NIR sensors, in which a long channel is utilized to measure the glucose level in the inner skin (dermis) layer, while a short channel is used to measure the noise signal of the superficial skin (epidermis) layer. In this work, we investigated the use of dual-NIR channels for patients with different skin colors (i.e., having different melanin concentrations). We also adopted a Monte Carlo simulation model that takes into consideration the differences between different skin layers, in terms of blood content, water content, melanin concentration in the epidermis layer, and skin optical proprieties. On the basis of the signal-to-noise ratio, as well as the sensitivities of both the epidermis and dermis layers, we suggest the selection of wavelengths and source-to-detector separation for optimal NIR channels under different skin melanin concentrations. This work facilitates the improved design of a compact and non-invasive NIR glucose sensor that can be utilized by patients with different skin colors.
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Abdullah, N. E., M. S. Baddri, N. K. Madzhi, S. L. M. Hassan, I. S. A. Halim, and A. A. A. Rahim. "Investigation on Identification of RRIM Clone Series using Various NIR LED sensor." Journal of Physics: Conference Series 2075, no. 1 (2021): 012014. http://dx.doi.org/10.1088/1742-6596/2075/1/012014.
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Abstract The evolution of various rubber tree clone has been introduced by Malaysian Rubber Board since 1928 and it is known as Rubber Research Institute Malaysia (RRIM) clone series. To support this, it is suggested research to investigate the behavior of the clone series with the development from a sensing device with several opted NIR wavelength ranges through the latex samples. An optical sensor prototype has been built consist of four types of NIR LED at selected wavelength and the photodiode works as receptor of the light emission. As transmitter, the NIR sensors are obtain from the local distributor in Malaysia which is RC component. Arduino Uno is use as the processor for processing the data and obtain the output trough LCD. There are five selected clone use which are RRIM 3001, RRIM 2008, RRIM 2007, RRIM 2014 and RRIM 2002. The output result which represented by the voltage and then will be analysed using statistical analysis approach for data analysis. The outcome reveal that the selected rubber tree clone series is able to determine based on the NIR properties.
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Dashti, Abolfazl, Judith Müller-Maatsch, Yannick Weesepoel, et al. "The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification." Foods 11, no. 1 (2021): 71. http://dx.doi.org/10.3390/foods11010071.
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Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400–1000 nm) and a handheld NIR (900–1700 nm) spectrometer were applied to discriminate halal meat species from pork (halal certification), as well as speciation of intact and ground lamb, beef, chicken and pork (160 meat samples). Several types of class modeling multivariate approaches were applied. The presented one-class classification (OCC) approach, especially with the Vis-NIR sensor (95–100% correct classification rate), was found to be suitable for the application of halal from non-halal meat-species discrimination. In a discriminant approach, using the Vis-NIR data and support vector machine (SVM) classification, the four meat species tested could be classified with accuracies of 93.4% and 94.7% for ground and intact meat, respectively, while with partial least-squares discriminant analysis (PLS-DA), classification accuracies were 87.4% (ground) and 88.6% (intact). Using the NIR sensor, total accuracies of the SVM models were 88.2% and 81.5% for ground and intact meats, respectively, and PLS-DA classification accuracies were 88.3% (ground) and 80% (intact). We conclude that the Vis-NIR sensor was most successful in the halal certification (OCC approaches) and speciation (discriminant approaches) for both intact and ground meat using SVM.
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Eberl, Ronald, and Jürgen Wilke. "NIR spectroscopic application of a refractometric sensor." Sensors and Actuators B: Chemical 32, no. 3 (1996): 203–8. http://dx.doi.org/10.1016/s0925-4005(97)80030-5.
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Hernández-Jiménez, Miriam, Isabel Revilla, Ana M. Vivar-Quintana, Justyna Grabska, Krzysztof B. Beć, and Christian W. Huck. "Comparison of Miniaturized and Benchtop NIR Spectrophotometers for Quantifying the Fatty Acid Profile of Iberian Ham." Applied Sciences 14, no. 22 (2024): 10680. http://dx.doi.org/10.3390/app142210680.
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Iberian ham is a highly valued product, and considerable efforts have been made to characterize it quickly and accurately. In this scenario, portable NIR devices could provide an effective solution for the assessment of its attributes. However, the calibration quality of NIR equipment is directly influenced by the relevance of the used spectral region. Therefore, this study aims to evaluate the suitability of different NIR spectrometers, including four portable and one benchtop instrument, with varying spectral working ranges for quantifying the fatty acid composition of Iberian ham. Spectral measurements were carried out on both the muscle and the fat of the ham slices. The results showed that 24 equations with an RSQ > 0.5 were obtained for both the muscle and fat for the NIRFlex N-500 benchtop instrument, while 19 and 14 equations were obtained in the muscle and 16 and 10 equations in the fat for the Enterprise Sensor and MicroNIR, respectively. In general, more fatty acids could be calibrated when the spectra were taken from lean meat, except with the SCiO Sensor. Measurements performed in the lean and fat zones delivered complementary information. These initial findings indicate the suitability of using miniaturized NIR sensors, which are faster, are less expensive, and enable on-site measurements, for analyzing fatty acids in Iberian ham.
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Jee, Seunghoon, and Moon Gi Kang. "Sensitivity Improvement of Extremely Low Light Scenes with RGB-NIR Multispectral Filter Array Sensor." Sensors 19, no. 5 (2019): 1256. http://dx.doi.org/10.3390/s19051256.
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Recently, several red-green-blue near-infrared (RGB-NIR) multispectral filter arrays (MFAs), which include near infrared (NIR) pixels, have been proposed. For extremely low light scenes, the RGB-NIR MFA sensor has been extended to receive NIR light, by adding NIR pixels to supplement for the insufficient visible band light energy. However, the resolution reconstruction of the RGB-NIR MFA, using demosaicing and color restoration methods, is based on the correlation between the NIR pixels and the pixels of other colors; this does not improve the RGB channel sensitivity with respect to the NIR channel sensitivity. In this paper, we propose a color restored image post-processing method to improve the sensitivity and resolution of an RGB-NIR MFA. Although several linear regression based color channel reconstruction methods have taken advantage of the high sensitivity NIR channel, it is difficult to accurately estimate the linear coefficients because of the high level of noise in the color channels under extremely low light conditions. The proposed method solves this problem in three steps: guided filtering, based on the linear similarity between the NIR and color channels, edge preserving smoothing to improve the accuracy of linear coefficient estimation, and residual compensation for lost spatial resolution information. The results show that the proposed method is effective, while maintaining the NIR pixel resolution characteristics, and improving the sensitivity in terms of the signal-to-noise ratio by approximately 13 dB.
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Kiki, Prawiroredjo, and Shintadewi Julian Engelin. "Effects of noises on near infrared sensor for blood glucose level measurement." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 3 (2020): 1459–66. https://doi.org/10.12928/TELKOMNIKA.v18i3.14760.
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This paper proposed the method of measuring glucose level in solution using near infrared light (NIR) and photodiode sensor. We studied noises that occurred on the output signal of NIR sensor in three different room conditions in order to know the effects on this sensor output voltage stability. The sensor’s circuit consisted of a 1450 nm NIR light emitting diode, a photodiode as the receiver, transimpedance amplifier, a notch filter, and a 4th order low pass filter. The results indicated that sunlight passing through windows was the most influencing factor caused the unstable sensor output voltage. Filters removed the effective voltages and the average sensor output voltages from the three rooms were 4.6825 V for air media, 2.2809 V for water media and 2.3368 V for glucose solution media. The output voltages tended to increase for one-hour measurement about 10 to 40 mV for air media, 40 to 90 mV for water media and 30 to 80 mV for glucose solution media. This sensor could only be used in a short time and suitable in a room without sunlight. Based on the voltage difference of the average sensor output voltage with water and glucose solution media, the sensor had the potential to be a blood glucose level meter.
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Jee, Seunghoon, and Moon Gi Kang. "Fusion Algorithm for Single Sensor Based RGB+NIR Multispectral Filter Array Sensor System." Journal of the Institute of Electronics and Information Engineers 55, no. 1 (2018): 90–96. http://dx.doi.org/10.5573/ieie.2018.55.1.90.
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Wang, Haiyan, Ronghua Liu, Lei Nie, et al. "Spectra selection methods: A novel optimization way for treating dynamic spectra and in-line near infrared modeling." Journal of Innovative Optical Health Sciences 13, no. 04 (2020): 2050015. http://dx.doi.org/10.1142/s1793545820500157.
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Near infrared (NIR) spectroscopy is now widely used in fluidized bed granulation. However, there are still some demerits that should be overcome in practice. Valid spectra selection during modeling process is now a hard nut to crack. In this study, a novel NIR sensor and a cosine distance method were introduced to solve this problem in order to make the fluidized process into “visualization”. A NIR sensor was fixed on the side of the expansion chamber to acquire the NIR spectra. Then valid spectra were selected based on a cosine distance method to reduce the influence of dynamic disturbances. Finally, spectral pretreatment and wavelength selection methods were investigated to establish partial least squares (PLS) models to monitor the moisture content. The results showed that the root mean square error of prediction (RMSEP) was 0.124% for moisture content model, which was much lower than that without valid spectra selection treatment. All results demonstrated that with the help of valid spectra selection treatment, NIR sensor could be used for real-time determination of critical quality attributes (CQAs) more accurately. It makes the manufacturing easier to understand than the process parameter control.
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Chia, Kim Seng, and Yit Peng Tan. "Design and Development of a Shortwave near Infrared Spectroscopy using NIR LEDs and Regression Model." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3070. http://dx.doi.org/10.11591/ijece.v7i6.pp3070-3075.
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<span>Near infrared (NIR) spectroscopic technology has been getting more attention in various fields. The development of a low cost NIR spectroscopy is crucial to reduce the financial barriers so that more NIR spectroscopic applications will be investigated and developed by means of the NIR spectroscopic technology. This study proposes an alternative to measure shortwave NIR spectrum using one collimating lens, two slits, one NIR transmission grating, one linear array sensor, and one microcontroller. Five high precision narrow bands NIR light emitting diodes (LEDs) were used to calibrate the proposed spectroscopy. The effects of the proposed two slits design, the distance between the grating and linear array sensor, and three different regression models were investigated. The accuracy of the proposed design was cross-validated using leave-one-out cross-validation. Results show that the proposed two slits design was able to eliminate unwanted signals substantially, and the cross-validation was able to estimate the best model with root mean squared error of cross-validation of 3.8932nm. Findings indicate that the cross-validation approach is a good approach to estimate the final model without over-fitting, and the proposed shortwave NIR spectroscopy was able to estimate the peak value of the acquired spectrum from NIR LEDs with RMSE of 1.1616nm.</span>
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Kim, Seng Chia, and Peng Tan Yit. "Design and Development of a Shortwave near Infrared Spectroscopy using NIR LEDs and Regression Model." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3070–75. https://doi.org/10.11591/ijece.v7i6.pp3070-3075.
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Near infrared (NIR) spectroscopic technology has been getting more attention in various fields. The development of a low cost NIR spectroscopy is crucial to reduce the financial barriers so that more NIR spectroscopic applications will be investigated and developed by means of the NIR spectroscopic technology. This study proposes an alternative to measure shortwave NIR spectrum using one collimating lens, two slits, one NIR transmission grating, one linear array sensor, and one microcontroller. Five high precision narrow bands NIR light emitting diodes (LEDs) were used to calibrate the proposed spectroscopy. The effects of the proposed two slits design, the distance between the grating and linear array sensor, and three different regression models were investigated. The accuracy of the proposed design was cross-validated using leave-one-out cross-validation. Results show that the proposed two slits design was able to eliminate unwanted signals substantially, and the cross-validation was able to estimate the best model with root mean squared error of cross-validation of 3.8932nm. Findings indicate that the cross-validation approach is a good approach to estimate the final model without over-fitting, and the proposed shortwave NIR spectroscopy was able to estimate the peak value of the acquired spectrum from NIR LEDs with RMSE of 1.1616nm.
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Acosta Ramirez, Ivon, Wali Sohail, Omer Sadak, and Nicole M. Iverson. "Design and Analysis of Rapid Production Single Wall Carbon Nanotube Sensor Platforms." ECS Meeting Abstracts MA2023-01, no. 9 (2023): 1149. http://dx.doi.org/10.1149/ma2023-0191149mtgabs.
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Single-wall carbon nanotubes (SWNT) have a strong and stable near-infrared (nIR) signal and can interact with target analytes selectively, even at the single molecule level, to alter fluorescence intensity and/or emission peak wavelength. SWNT have been employed as nIR optical sensors for detecting a variety of analytes. However, high cost, long fabrication time, and poor fluorescence yield limit the current methods for immobilizing SWNT sensors on solid substrates. Recently, our group reported a protocol for SWNT immobilization resulting in high fluorescence yield, signal longevity, fluorescence distribution, and quick sensing response time. However, it takes 5 days to fabricate these sensor arrays. We have improved our previously reported protocol to immobilize SWNT sensors with a method that takes only 2 days, results in a platform with similar surface morphology, and has a higher fluorescence intensity than the previous platforms without sacrificing the sensing capabilities.
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Jekal, Suk, Jiwon Kim, Dong-Hyun Kim, et al. "Synthesis of LiDAR-Detectable True Black Core/Shell Nanomaterial and Its Practical Use in LiDAR Applications." Nanomaterials 12, no. 20 (2022): 3689. http://dx.doi.org/10.3390/nano12203689.
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Light detection and ranging (LiDAR) sensors utilize a near-infrared (NIR) laser with a wavelength of 905 nm. However, LiDAR sensors have weakness in detecting black or dark-tone materials with light-absorbing properties. In this study, SiO2/black TiO2 core/shell nanoparticles (SBT CSNs) were designed as LiDAR-detectable black materials. The SBT CSNs, with sizes of 140, 170, and 200 nm, were fabricated by a series of Stöber, TTIP sol-gel, and modified NaBH4 reduction methods. These SBT CSNs are detectable by a LiDAR sensor and, owing to their core/shell structure with intrapores on the shell (ca. 2–6 nm), they can effectively function as both color and NIR-reflective materials. Moreover, the LiDAR-detectable SBT CSNs exhibited high NIR reflectance (28.2 R%) in a monolayer system and true blackness (L* < 20), along with ecofriendliness and hydrophilicity, making them highly suitable for use in autonomous vehicles.
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Nordberg, Å., M. Hansson, I. Sundh, E. Nordkvist, H. Carlsson, and B. Mathisen. "Monitoring of a biogas process using electronic gas sensors and near-infrared spectroscopy (NIR)." Water Science and Technology 41, no. 3 (2000): 1–8. http://dx.doi.org/10.2166/wst.2000.0049.
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The use of electronic gas sensors and near-infrared spectroscopy (NIR) to monitor the dynamics in a biogas process was evaluated using multivariate data analysis. The digester, a completely stirred 8 l tank reactor fed with a mixture of cellulose, albumin and minerals, was exposed to an overload of glucose after which monitoring of electronic gas sensor responses, NIR spectra as well as traditional chemical variables and analysis of microbial community structure were done. The responses from an array of electronic gas sensors consisting of MOS and MOSFET-sensors were correlated against volatile compounds in the headspace using partial least square (PLS) regressions. The root mean square error of prediction (RMSEP) was 0.15 g/l for acetate in the range of 0.14–1.72 g/l and the RMSEP for methane was 2.3% in the range of 27–73%. Selected wavelengths from the second derivative of the original NIR spectra (400–2500 nm) resulted in a PLS-model for predicting microbial biomass, measured as total phospholipid fatty acids, with a RMSEP of 9 nmol/ml in the range of 163–293 nmol/ml. The NIR model developed for acetate had a RMSEP of 0.20 g/l within the range of 0.14–1.72 g/l. The results clearly show that both NIR and an array of electronic gas sensors can provide simultaneous non-invasive in situ monitoring of important process variables in anaerobic digesters.
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Kruss, Sebastian. "(Invited) Near Infrared Fluorescence Lifetime Imaging of Biomolecules with Carbon Nanotubes." ECS Meeting Abstracts MA2023-01, no. 9 (2023): 1139. http://dx.doi.org/10.1149/ma2023-0191139mtgabs.
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Single wall carbon nanotubes (SWCNTs) are versatile building blocks for biosensors. Their near infrared (NIR) fluorescence enables detection of biomolecules in the optical tissue transparency window. Their surface chemistry can be tailored to selectively interact with analytes, which typically changes the fluorescence intensity. However, such signals are affected by external factors such as sample movement or fluctuations in excitation light. Here, we demonstrate fluorescence lifetime imaging microscopy (FLIM) of SWCNT-based sensors in the NIR as absolute and calibration-free method. For this purpose, we use a tailored laser scanning confocal microscope (LSCM) optimized for NIR signals (>800 nm) and time correlated single photon counting (TCSPC). (GT)10-DNA modified SWCNTs serve as sensor of the important neurotransmitter dopamine. The detected fluorescence signal > 900 nm increases in lifetime up to 25 %. These lifetime sensors are used as paint to cover cells and report extracellular dopamine in 3D. We therefore show the potential of using fluorescence lifetime in combination with confocal microscopy as readout for SWCNT-based sensors.
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Park, Younghyeon, and Byeungwoo Jeon. "An Acquisition Method for Visible and Near Infrared Images from Single CMYG Color Filter Array-Based Sensor." Sensors 20, no. 19 (2020): 5578. http://dx.doi.org/10.3390/s20195578.
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Near-infrared (NIR) images are very useful in many image processing applications, including banknote recognition, vein detection, and surveillance, to name a few. To acquire the NIR image together with visible range signals, an imaging device should be able to simultaneously capture NIR and visible range images. An implementation of such a system having separate sensors for NIR and visible light has practical shortcomings due to its size and hardware cost. To overcome this, a single sensor-based acquisition method is investigated in this paper. The proposed imaging system is equipped with a conventional color filter array of cyan, magenta, yellow, and green, and achieves signal separation by applying a proposed separation matrix which is derived by mathematical modeling of the signal acquisition structure. The elements of the separation matrix are calculated through color space conversion and experimental data. Subsequently, an additional denoising process is implemented to enhance the quality of the separated images. Experimental results show that the proposed method successfully separates the acquired mixed image of visible and near-infrared signals into individual red, green, and blue (RGB) and NIR images. The separation performance of the proposed method is compared to that of related work in terms of the average peak-signal-to-noise-ratio (PSNR) and color distance. The proposed method attains average PSNR value of 37.04 and 33.29 dB, respectively for the separated RGB and NIR images, which is respectively 6.72 and 2.55 dB higher than the work used for comparison.
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Ruswandi, Keinanjung, Fionando Emir Satriadana, Bintang Aprilio Putra, et al. "DRONE SEMPROT UNTUK DETEKSI PENYAKIT BUSUK PANGKAL BATANG PADA TANAMAN KELAPA SAWIT." Elektrika 16, no. 2 (2024): 93. http://dx.doi.org/10.26623/elektrika.v16i2.9521.
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Drone Spray for Base Rot Disease Detection in Oil Palm Plants aims to increase the effectiveness of disease detection and control using drone technology. Stem base rot disease, caused by the fungus Ganoderma boninense, threatens oil palm productivity in Indonesia. Drone technology with near-infrared (NIR) sensors and image processing is used to detect early signs of the disease, enabling timely preventive action. A drone equipped with a Raspberry Pi camera and NoIR V2 pro-duces high-resolution images for NIR reflectance analysis. The research included field observations, expert interviews, and sensor data collection for evaluation of the effectiveness of this technology. Results show that the use of drones can in-crease disease detection efficiency by 85%, reduce operational costs, and minimize health risks for workers. This drone technology has great potential to improve the productivity and sustainability of the palm oil industry in Indonesia. This research recommends the adoption of drone technology in the agricultural sector to address plant disease challenges more efficiently and effectively. Keywords: Disease, Drone, NIR, Oil Palm. ABSTRAK Drone Semprot untuk Deteksi Penyakit Busuk Pangkal Batang pada Tanaman Kelapa Sawit bertujuan meningkatkan efektivitas deteksi dan pengendalian penyakit menggunakan teknologi drone. Penyakit busuk pangkal batang, disebabkan oleh jamur Ganoderma boninense, mengancam produktivitas kelapa sawit di Indonesia. Teknologi drone dengan sensor inframerah dekat (NIR) dan image processing digunakan untuk mendeteksi tanda-tanda awal penyakit, memungkinkan tindakan preventif yang tepat waktu. Drone yang dilengkapi kamera Raspberry Pi dan NoIR V2 menghasilkan citra yang memiliki resolusi tinggi untuk analisis reflektansi NIR. Penelitian ini mencakup observasi lapangan, wawancara dengan pakar, dan pengumpulan data sensor untuk evaluasi efektivitas teknologi ini. Hasil menunjukkan bahwa penggunaan drone dapat meningkatkan efisiensi deteksi penyakit hingga 85%, mengurangi biaya operasional, dan meminimalkan risiko kesehatan bagi pekerja. Teknologi drone ini berpotensi besar dalam meningkatkan produktivitas dan keberlanjutan industri kelapa sawit di Indonesia. Penelitian ini merekomendasikan adopsi teknologi drone dalam sektor pertanian untuk mengatasi tantangan penyakit tanaman secara lebih efisien dan efektif.
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Deb, Moumita, Mei-Yu Chen, Po-Yi Chang, et al. "SnO2-Based Ultra-Flexible Humidity/Respiratory Sensor for Analysis of Human Breath." Biosensors 13, no. 1 (2023): 81. http://dx.doi.org/10.3390/bios13010081.
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Developing ultraflexible sensors using metal oxides is challenging due to the high-temperature annealing step in the fabrication process. Here, we demonstrate the ultraflexible relative humidity (RH) sensor on food plastic wrap by using 808 nm near-infrared (NIR) laser annealing for 1 min at a low temperature (26.2–40.8 °C). The wettability of plastic wraps coated with sol-gel solution is modulated to obtain uniform films. The surface morphology, local temperature, and electrical properties of the SnO2 resistor under NIR laser irradiation with a power of 16, 33, and 84 W/cm2 are investigated. The optimal device can detect wide-range RH from 15% to 70% with small incremental changes (0.1–2.2%). X-ray photoelectron spectroscopy reveals the relation between the surface binding condition and sensing response. Finally, the proposed sensor is attached onto the face mask to analyze the real-time human breath pattern in slow, normal, and fast modes, showing potential in wearable electronics or respiration monitoring.
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De Ridder, Francesca, Rie Braspenning, Juan S. Ordonez, et al. "Early feasibility study with an implantable near-infrared spectroscopy sensor for glucose, ketones, lactate and ethanol." PLOS ONE 19, no. 5 (2024): e0301041. http://dx.doi.org/10.1371/journal.pone.0301041.
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Objective To evaluate the safety and performance of an implantable near-infrared (NIR) spectroscopy sensor for multi-metabolite monitoring of glucose, ketones, lactate, and ethanol. Research design and methods This is an early feasibility study (GLOW, NCT04782934) including 7 participants (4 with type 1 diabetes (T1D), 3 healthy volunteers) in whom the YANG NIR spectroscopy sensor (Indigo) was implanted for 28 days. Metabolic challenges were used to vary glucose levels (40–400 mg/dL, 2.2–22.2 mmol/L) and/or induce increases in ketones (ketone drink, up to 3.5 mM), lactate (exercise bike, up to 13 mM) and ethanol (4–8 alcoholic beverages, 40-80g). NIR spectra for glucose, ketones, lactate, and ethanol levels analyzed with partial least squares regression were compared with blood values for glucose (Biosen EKF), ketones and lactate (GlucoMen LX Plus), and breath ethanol levels (ACE II Breathalyzer). The effect of potential confounders on glucose measurements (paracetamol, aspartame, acetylsalicylic acid, ibuprofen, sorbitol, caffeine, fructose, vitamin C) was investigated in T1D participants. Results The implanted YANG sensor was safe and well tolerated and did not cause any infectious or wound healing complications. Six out 7 sensors remained fully operational over the entire study period. Glucose measurements were sufficiently accurate (overall mean absolute (relative) difference MARD of 7.4%, MAD 8.8 mg/dl) without significant impact of confounders. MAD values were 0.12 mM for ketones, 0.16 mM for lactate, and 0.18 mM for ethanol. Conclusions The first implantable multi-biomarker sensor was shown to be well tolerated and produce accurate measurements of glucose, ketones, lactate, and ethanol. Trial registration Clinical trial identifier: NCT04782934.
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Xu, Lisheng, and John R. Schlup. "Application of Near-Infrared Attenuated Total Reflectance Spectroscopy for Monitoring Epoxy Resin/Amine Cure Reactions." Applied Spectroscopy 50, no. 1 (1996): 109–14. http://dx.doi.org/10.1366/0003702963906654.
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Product quality in composite processing will be improved by dependable methods for monitoring the process. In this paper, the feasibility of using near-infrared attenuated total reflection (NIR ATR) spectroscopy as a sensor for monitoring epoxy resin cure is demonstrated. An ATR crystal serves as a contact sensor, which has several potential advantages over embedded optic fiber techniques previously reported. NIR ATR spectra obtained from several epoxy/amine systems show that both primary amine and epoxy functional groups have well-isolated absorption peaks in the near-infrared region. The utility of NIR ATR spectroscopy for in situ cure monitoring is demonstrated by following the reaction between phenyl glycidyl ether and butylamine at ambient temperature.
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Rohe, Th, W. Becker, A. Krey, H. Nägele, S. Kölle, and N. Eisenreich. "In-Line Monitoring of Polymer Extrusion Processes by NIR Spectroscopy." Journal of Near Infrared Spectroscopy 6, no. 1 (1998): 325–32. http://dx.doi.org/10.1255/jnirs.153.
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NIR spectroscopy has become an analytical tool frequently used in many chemical production processes. Its use in polymer processing applications such as polymer extrusion would greatly increase product quality. Parameters of interest in this application are composition of the processed polymer, moisture or reaction status in reactive polymeric systems, as well as rheological parameters such as melt flow index (MFI) or viscosity. The measurement of NIR spectra could provide a way to control the processes. For this purpose a transmission sensor was developed for the application of NIR spectroscopy to extrusion processes. This sensor includes fibre optical probes and a measuring cell that can be adapted to the various extruders for in-line measurements. In contrast to mid-infrared sensors, it uses optical quartz components, which provides a low-cost solution. Extrusion processes at temperatures up to 300°C and pressures up to 35 MPa have been investigated. The application of multivariate analysis for PE/PP blending demonstrated the performance of the system with respect to process monitoring. In this case deviations between predicted and actual polymer composition were below 2%. Together with an AOTF spectrometer that was also developed, the complete system is suitable for harsh industrial environments and could lead to improved extrusion processes.
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Karim, Md Rejaul, Md Asrakul Haque, Shahriar Ahmed, et al. "Effects of Sensor Speed and Height on Proximal Canopy Reflectance Data Variation for Rice Vegetation Monitoring." Agronomy 15, no. 3 (2025): 618. https://doi.org/10.3390/agronomy15030618.
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Sensing distance and speed have crucial effects on the data of active and passive sensors, providing valuable information relevant to crop growth monitoring and environmental conditions. The objective of this study was to evaluate the effects of sensing speed and sensor height on the variation in proximal canopy reflectance data to improve rice vegetation monitoring. Data were collected from a rice field using active and passive sensors with calibration procedures including downwelling light sensor (DLS) calibration, field of view (FOV) alignment, and radiometric calibration, which were conducted per official guidelines. The data were collected at six sensor heights (30–130 cm) and speeds (0–0.5 ms–1). Analyses, including peak signal-to-noise ratio (PSNR) and normalized difference vegetation index (NDVI) calculations and statistical assessments, were conducted to explore the impacts of these parameters on reflectance data variation. PSNR analysis was performed on passive sensor image data to evaluate image data variation under varying data collection conditions. Statistical analysis was conducted to assess the effects of sensor speed and height on the NDVI derived from active and passive sensor data. The PSNR analysis confirmed that there were significant impacts on data variation for passive sensors, with the NIR and G bands showing higher noise sensitivity at increased speeds. The NDVI analysis showed consistent patterns at sensor heights of 70–110 cm and sensing speeds of 0–0.3 ms–1. Increased sensing speeds (0.4–0.5 ms–1) introduced motion-related variability, while lower heights (30–50 cm) heightened ground interference. An analysis of variance (ANOVA) indicated significant individual effects of speed and height on four spectral bands, red (R), green (G), blue (B), and near-infrared (NIR), in the passive sensor images, with non-significant interaction effects observed on the red edge (RE) band. The analysis revealed that sensing speed and sensor height influence NDVI reliability, with the configurations of 70–110 cm height and 0.1–0.3 ms–1 speed ensuring the stability of NDVI measurements. This study notes the importance of optimizing sensor height and sensing speed for precise vegetation index calculations during field data acquisition for agricultural crop monitoring.
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Ivanov, Artem, and Arne Kulinna. "Implementation of a mobile spectrometer using a near infrared MEMS Fabry–Pérot interferometer sensor." tm - Technisches Messen 89, no. 1 (2021): 60–69. http://dx.doi.org/10.1515/teme-2021-0091.
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Abstract Miniaturised MEMS-based Fabry-Pérot interferometer (FPI) spectral sensors allow the design of compact spectrometers in the near infrared (NIR) range. These small-size instruments can be used for quality control of alimentation products, sorting of plastics and fabrics in respect to the material composition or defining genuineness of goods. This article describes design details and achieved results in development of an inexpensive user friendly hand-held NIR spectrometer incorporating a MEMS-FPI sensor with the spectral range of 1550–1850 nm. Implemented electronic circuitry as well as the optical configuration of the device are discussed, used electronic components and the background for the choice of the light source are presented. Furthermore, the associated software for device operation and data visualisation is described. Achieved technical parameters of the device are discussed and illustrated by examples of acquired spectra. Shared experience in operating a MEMS-FPI sensor could be especially useful for designers targeting low-cost instruments for use by general public.
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Chen, Yuwei, Chenxi Li, Bo Gao, Huangrong Xu, and Weixing Yu. "A Non-Invasive and Highly Accurate Multi-Wavelength Light Near-Infrared Glucose Sensor Using A Multilevel Metric Learning–Back Propagation Network." Applied Sciences 15, no. 10 (2025): 5652. https://doi.org/10.3390/app15105652.
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Non-invasive near-infrared (NIR) human glucose sensors have attracted great interest in managing diabetes mellitus and those with complex sensing backgrounds due to glucose absorption spectrum overlap. Here, we propose a non-invasive and highly accurate multi-wavelength light NIR glucose sensor using a multilevel metric learning-back propagation network, i.e., “HMML-BP”, based on the narrowband multi-wavelength light NIR system. Our human glucose sensing method combines the advantages of this system and an HMML-BP network. The latter is composed of multilevel metric learning modules and a BP network to predict blood glucose concentrations. The narrowband multi-wavelength light NIR sensing system consists of six-channel NIR filters with center wavelengths of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm and a spectral resolution below 12 nm. The six NIR channels measured were first entered into the MML modules to build 3D multi-wavelength light data. Next, 3D multi-wavelength light data were optimized by stochastic neighbor embedding. Diffusion maps and factor analysis algorithms were used to retain effective NIR information. Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. Our blood glucose sensing method provides a new way of utilizing multi-wavelength light and hyperspectral information for smart human glucose monitoring.
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Wang, Ke, Chengxiang Peng, and Zuoxun Hou. "Dark Noise Suppression of NIR Response Enhanced Si-CMOS Sensor." Photonics 9, no. 5 (2022): 307. http://dx.doi.org/10.3390/photonics9050307.
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We studied the effect of laser fluence on the dark noise performance of a laser-microstructured Si-based CMOS image sensor. The absorption characteristics and crystal properties of the microstructured sensor fabricated under different process conditions were investigated. Furthermore, a short-time etching method capable of improving the electrical performance of the laser-microstructured sensor was proposed. By removing amorphous silicon (a-Si) containing a large number of defects in the photosensitive surface of the microstructured Si-based CMOS image sensor, the etching method can effectively suppress the dark noise of the laser-microstructured Si-photodetector while maintaining the near-infrared response enhancement effect of the Si-photodetector irradiated by fs-laser. The results of the near-infrared imaging test show that on the basis of imaging brightness enhancement, the contrast ratio of the image formed by the CMOS image sensor in the microstructured region etched by RIE under short exposure time is significantly improved.
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Gaydon, James W., Hylke J. Glass, and Richard D. Pascoe. "Method for near Infrared Sensor-Based Sorting of a Copper Ore." Journal of Near Infrared Spectroscopy 17, no. 4 (2009): 177–94. http://dx.doi.org/10.1255/jnirs.849.
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Sorting of minerals based on near infrared (NIR) analysis is promising because many minerals have distinct “fingerprints” in the NIR region. An experimental system was customised in order to obtain accurate NIR reflectance spectra. As a test application, the NIR spectra of transects across pre-classified copper ore particles were measured. Matrices containing correlation coefficients of particle pixel–pixel spectra were subjected to principal component analysis. In addition to providing insight into the homogeneity of the particle surface, the results were used to identify key spectral features which could be used to sort product from waste particles. With a selected spectral range, it was found that the classification improved when two standard pixels relating to product and waste were inserted into the transects. While testing an equal number of particles from each rock type, it was found that a correct classification was made in 82% of all rocks. It was found that moisture had little to no effect on the sorting method.
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Tian, Ying, Xinyu You, and Xiuhui Huang. "SDAE-BP Based Octane Number Soft Sensor Using Near-infrared Spectroscopy in Gasoline Blending Process." Symmetry 10, no. 12 (2018): 770. http://dx.doi.org/10.3390/sym10120770.
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As the most important properties in the gasoline blending process, octane number is difficult to be measured in real time. To address this problem, a novel deep learning based soft sensor strategy, by using the near-infrared (NIR) spectroscopy obtained in the gasoline blending process, is proposed. First, as a network structure with hidden layer as symmetry axis, input layer and output layer as symmetric, the denosing auto-encoder (DAE) realizes the advanced expression of input. Additionally, the stacked DAE (SDAE) is trained based on unlabeled NIR and the weights in each DAE is recorded. Then, the recorded weights are used as the initial parameters of back propagation (BP) with the reason that the SDAE trained initial weights can avoid local minimums and realizes accelerate convergence, and the soft sensor model is achieved with labeled NIR data. Finally, the achieved soft sensor model is used to estimate the real time octane number. The performance of the method is demonstrated through the NIR dataset of gasoline, which was collected from a real gasoline blending process. Compared with PCA-BP (the dimension of datasets of BP reduced by principal component analysis) soft sensor model, the prediction accuracy was improved from 86.4% of PCA-BP to 94.8%, and the training time decreased from 20.1 s to 16.9 s. Therefore, SDAE-BP is proposed as a novel method for rapid and efficient determination of octane number in the gasoline blending process.
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Lin, Lei, Yong He, Zhitao Xiao, Ke Zhao, Tao Dong, and Pengcheng Nie. "Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy." Applied Sciences 9, no. 8 (2019): 1654. http://dx.doi.org/10.3390/app9081654.
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Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13–30% moisture content were detected, the coefficient of determination R2 was 0.936, and the sum of squares for error (SSE) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process.
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Dippold, Elisabeth Johanna, and Fuan Tsai. "Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation." Sensors 24, no. 7 (2024): 2358. http://dx.doi.org/10.3390/s24072358.
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The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for reducing interferences in the SfM process. The first stage includes cross-sensor training, model selection and the evaluation of image-to-image RGB to color infrared (CIR) translation with Generative Adversarial Networks (GANs). The second stage includes feature detection with multiple feature detector operators, feature removal with respect to the NDVI-based vegetation classification, masking, matching, pose estimation and triangulation to generate sparse 3D point clouds. The materials utilized in both stages are a publicly available RGB-NIR dataset, and satellite and UAV imagery. The experimental results indicate that the cross-sensor and category-wise validation achieves an accuracy of 0.9466 and 0.9024, with a kappa coefficient of 0.8932 and 0.9110, respectively. The histogram-based evaluation demonstrates that the predicted NIR band is consistent with the original NIR data of the satellite test dataset. Finally, the test on the UAV RGB and artificially generated NIR with a segmentation-driven two-view SfM proves that the proposed framework can effectively translate RGB to CIR for NDVI calculation. Further, the artificially generated NDVI is able to segment and classify vegetation. As a result, the generated point cloud is less noisy, and the 3D model is enhanced.
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Menesatti, P., F. Pallottino, S. Figorilli, F. Antonucci, R. Tomasone, and C. Costa. "Multi-sensor imaging retrofit system to test precision agriculture machine-based applications." Advances in Animal Biosciences 8, no. 2 (2017): 189–92. http://dx.doi.org/10.1017/s2040470017000577.
Full textAbstract:
An increasing number of farm machines nowadays implement precision agriculture technologies. Most of these operate through proximal sensing using optical sensors (i.e. NIR or Vis-NIR). Imaging techniques in this context have received minor consideration due to the complex analysis of the data but on the other side offer great flexibility. This study reports a preliminary pilot imaging multi-sensor retrofit system to be applied independently on a wide range of agricultural machines and able to test different monitoring or control image-based applications for precision agriculture. The process, based on RGB image, was tested for in-field discrimination of weeds in lettuce and broccoli crops. It works by discriminating and extracting single plants from the soil and weeds. However, to be truly implementable, the experimental code should be optimized in order to shorten the time needed for acquisition and processing.
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Sakarya, Ufuk, İsmail Hakkı Demirhan, Hüsne Seda Deveci, et al. "ABSOLUTE RADIOMETRIC CALIBRATION OF THE GÖKTÜRK-2 SATELLITE SENSOR USING TUZ GÖLÜ (LANDNET SITE) FROM NDVI PERSPECTIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 373–77. http://dx.doi.org/10.5194/isprsarchives-xli-b1-373-2016.
Full textAbstract:
TÜBİTAK UZAY has conducted a research study on the use of space-based satellite resources for several aspects of agriculture. Especially, there are two precision agriculture related projects: HASSAS (Widespread application of sustainable precision agriculture practices in Southeastern Anatolia Project Region (GAP) Project) and AKTAR (Smart Agriculture Feasibility Project). The HASSAS project aims to study development of precision agriculture practice in GAP region. Multi-spectral satellite imagery and aerial hyperspectral data along with ground measurements was collected to analyze data in an information system. AKTAR aims to develop models for irrigation, fertilization and spectral signatures of crops in Inner Anatolia. By the end of the project precision agriculture practices to control irrigation, fertilization, pesticide and estimation of crop yield will be developed. Analyzing the phenology of crops using NDVI is critical for the projects. For this reason, absolute radiometric calibration of the Red and NIR bands in space-based satellite sensors is an important issue. The Göktürk-2 satellite is an earth observation satellite which was designed and built in Turkey and was launched in 2012. The Göktürk-2 satellite sensor has a resolution 2.5 meters in panchromatic and 5 meters in R/G/B/NIR bands. The absolute radiometric calibration of the Göktürk-2 satellite sensor was performed via the ground-based measurements - spectra-radiometer, sun photometer, and meteorological station- in Tuz Gölü cal/val site in 2015. In this paper, the first ground-based absolute radiometric calibration results of the Göktürk-2 satellite sensor using Tuz Gölü is demonstrated. The absolute radiometric calibration results of this paper are compared with the published cross-calibration results of the Göktürk-2 satellite sensor utilizing Landsat 8 imagery. According to the experimental comparison results, the Göktürk-2 satellite sensor coefficients for red and NIR bands estimated in this work sustained to agree within 2% of calibration coefficients estimated in the cross-calibration results.
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Sakarya, Ufuk, İsmail Hakkı Demirhan, Hüsne Seda Deveci, et al. "ABSOLUTE RADIOMETRIC CALIBRATION OF THE GÖKTÜRK-2 SATELLITE SENSOR USING TUZ GÖLÜ (LANDNET SITE) FROM NDVI PERSPECTIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 373–77. http://dx.doi.org/10.5194/isprs-archives-xli-b1-373-2016.
Full textAbstract:
TÜBİTAK UZAY has conducted a research study on the use of space-based satellite resources for several aspects of agriculture. Especially, there are two precision agriculture related projects: HASSAS (Widespread application of sustainable precision agriculture practices in Southeastern Anatolia Project Region (GAP) Project) and AKTAR (Smart Agriculture Feasibility Project). The HASSAS project aims to study development of precision agriculture practice in GAP region. Multi-spectral satellite imagery and aerial hyperspectral data along with ground measurements was collected to analyze data in an information system. AKTAR aims to develop models for irrigation, fertilization and spectral signatures of crops in Inner Anatolia. By the end of the project precision agriculture practices to control irrigation, fertilization, pesticide and estimation of crop yield will be developed. Analyzing the phenology of crops using NDVI is critical for the projects. For this reason, absolute radiometric calibration of the Red and NIR bands in space-based satellite sensors is an important issue. The Göktürk-2 satellite is an earth observation satellite which was designed and built in Turkey and was launched in 2012. The Göktürk-2 satellite sensor has a resolution 2.5 meters in panchromatic and 5 meters in R/G/B/NIR bands. The absolute radiometric calibration of the Göktürk-2 satellite sensor was performed via the ground-based measurements - spectra-radiometer, sun photometer, and meteorological station- in Tuz Gölü cal/val site in 2015. In this paper, the first ground-based absolute radiometric calibration results of the Göktürk-2 satellite sensor using Tuz Gölü is demonstrated. The absolute radiometric calibration results of this paper are compared with the published cross-calibration results of the Göktürk-2 satellite sensor utilizing Landsat 8 imagery. According to the experimental comparison results, the Göktürk-2 satellite sensor coefficients for red and NIR bands estimated in this work sustained to agree within 2% of calibration coefficients estimated in the cross-calibration results.
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Merotto JR., A., C. Bredemeier, R. A. Vidal, I. C. G. R. Goulart, E. D. Bortoli, and N. L. Anderson. "Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture." Planta Daninha 30, no. 2 (2012): 437–47. http://dx.doi.org/10.1590/s0100-83582012000200024.
Full textAbstract:
Several tools of precision agriculture have been developed for specific uses. However, this specificity may hinder the implementation of precision agriculture due to an increasing in costs and operational complexity. The use of vegetation index sensors which are traditionally developed for crop fertilization, for site-specific weed management can provide multiple utilizations of these sensors and result in the optimization of precision agriculture. The aim of this study was to evaluate the relationship between reflectance indices of weeds obtained by the GreenSeekerTM sensor and conventional parameters used for weed interference quantification. Two experiments were conducted with soybean and corn by establishing a gradient of weed interference through the use of pre- and post-emergence herbicides. The weed quantification was evaluated by the normalized difference vegetation index (NDVI) and the ratio of red to near infrared (Red/NIR) obtained using the GreenSeekerTM sensor, the visual weed control, the weed dry matter, and digital photographs, which supplied information about the leaf area coverage proportions of weed and straw. The weed leaf coverage obtained using digital photography was highly associated with the NDVI (r = 0.78) and the Red/NIR (r = -0.74). The weed dry matter also positively correlated with the NDVI obtained in 1 m linear (r = 0.66). The results indicated that the GreenSeekerTM sensor originally used for crop fertilization could also be used to obtain reflectance indices in the area between rows of crops to support decision-making programs for weed control.
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Kamatchi, S., and M. Sundararajan. "Dyadic wavelet analysis and detection of sinusitis using near infrared sensor." International Journal of Otorhinolaryngology and Head and Neck Surgery 4, no. 2 (2018): 542. http://dx.doi.org/10.18203/issn.2454-5929.ijohns20180722.
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<p class="abstract"><strong>Background:</strong> Sinusitis is a chronic infection or inflammation which affects the paranasal sinus cavities and the associated nasal cavities. As the symptoms of sinusitis greatly resemble upper respiratory infections, diagnosing sinusitis clinically is a major issue.<strong> </strong>Though imaging techniques serves as a standard in confirming the diagnosis of chronic sinusitis, the availability at the primary care settings, affordability and diagnosing acute cases calls upon an alternative technique in practice. Recent researches confirming the diagnosis of sinusitis using Near-infrared imaging gives us hope in taking up the research using optical sensing. The objective of the study was to successfully diagnose sinusitis using NIR-LED optical sensor and to signal process the data obtained from the patients using Dyadic Wavelet Transform (DyWT) to confirm and to validate diagnosis using regression analysis. The study also correlates the plain radiographic findings with the NIR device sensing to make the device feasible.</p><p class="abstract"><strong>Methods:</strong> This was a one year pilot study (June 2014–May 2015) conducted with forty patients suspected of sinusitis and with clinical history along with ten healthy individuals as controls. </p><p class="abstract"><strong>Results:</strong> Patients age ranged from 18-65 years were included in the study. Results from NIR sensing device well correlate with the radiographic examination of the registered candidates. The regression result perfectly matches with the dyadic wavelet results of the patients, confirming the diagnosing of sinusitis using near- infrared sensor. Radiographic examination well correlates with the results from the NIR diagnostic device providing a valuable evidence of the hardware.</p><p class="abstract"><strong>Conclusions:</strong> NIR-LED sensor device can provide qualitative evidence in differentiating the mild and severe patients based on air-fluid level present in the sinus. The results strongly recommend that NIR sensing device can be a best alternative in case of frequently sinus affected patients and for the unaffordable patients without the risk of radiation.</p>
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Rady, Ahmed, Joel Fischer, Stuart Reeves, Brian Logan, and Nicholas James Watson. "The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods." Sensors 20, no. 1 (2019): 230. http://dx.doi.org/10.3390/s20010230.
Full textAbstract:
Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.