• Journal of Sensor Science and Technology
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• v.21 no.4
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• pp.270-275
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• 2012

In this study, we have developed a mobile-based visible/NIR(Near InfraRed) imaging equipment for the animal testing. This equipment can provide visible, NIR and merged image through the viewer program. Especially, merged image help researcher to understand visual messages at animal in-vivo test. Also, it is available to send real-time images through the smart phone. Researcher can communicate with another researcher who is a long distance away. Also, the equipment was made with portable small size to enable it to commercialize.

• Journal of Biosystems Engineering
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• v.26 no.2
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• pp.169-176
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• 2001

Defect of apple was depreciated the product value and causes storage disease seriously. To detect the defect of ‘Fuji’apple with machine vision system, the optical characteristics of defect should be investigated. In this research, absorbance spectra of defect were acquired by spectrophotometer in the range of visible and NIR region(400∼1,100nm) and L*a*b* color values were also acquired by colorimeter. NIR machine vision system was constructed with B&W camera, frame grabber, 16 tungsten-halogen lamps, variable focal length lens and NIR bandpass filter which was mounted to lens outward. Average gray values of defect at 15 NIR wavelength were acquired and the significant NIR wavelength was selected by comparing Mahalanobis distance between sound and defective apple. As the result of Mahalanobis distance analysis, the significant wavelength to discriminate the defectives in ‘Fuji’apple were found to be 720nm for scab and 970nm for bruise and cuts and 920nm was also effective regardless of defective types.

• Applied Chemistry for Engineering
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• v.33 no.1
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• pp.17-27
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• 2022

Fluorescence imaging is widely used to image cells or small animals due to its high temporal and spatial resolution. Because conventional fluorescence imaging uses visible light, the penetration depth of light within the tissue is low, phototoxicity may occur due to visible light, and the detection sensitivity is lowered due to interference by background autofluorescence. In order to overcome this limitation, long-wavelength light should be used, and fluorescence imaging using near-infrared-I (NIR-I) in the region of 700~900 nm has been developed. To further improve imaging quality, researchers are interested in using a longer wavelength light, near-infrared-II (NIR-II) ranging from 1000 to 1700 nm. In the NIR-II region, light scattering is further minimized, and the penetration depth of light in the tissue is improved up to about 10 mm, and autofluorescence of the tissue is reduced, enabling high sensitivity and resolution fluorescence imaging. In this review, among various NIR-II fluorescence imaging probes, inorganic nanoparticle-based probes with excellent photostability and easily tunable emission wavelength were described, focusing on single-walled carbon nanotubes, quantum dots, and lanthanide nanoparticles.

• Journal of Biosystems Engineering
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• v.37 no.6
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• pp.411-419
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• 2012

Purpose: Nondestructive evaluation of seed viability is a highly demanded technique in the seed industry. In this study, hyperspectral imaging system was used for discrimination of viable and non-viable radish seeds. Method: The spectral data with the range from 400 to 1000 nm measured by hyperspectral reflectance imaging system were used. A calibration and a test models were developed by partial least square discrimination analysis (PLS-DA) for classification of viable and non-viable radish seeds. Either each data set of visible (400~750 nm) and NIR (750~1000 nm) spectra and the spectra of the combined spectral ranges were used for developing models. Results: The discrimination accuracy of calibration was 84% for visible range and 76.3% for NIR range. The discrimination accuracy of test was 84.2% for visible range and 75.8% for NIR range. The discrimination accuracies of calibration and test with full range were 92.2% and 92.5%, respectively. The resultant images based on the optimal PLS-DA model showed high performance for the discrimination of the nonviable seeds from the viable seeds with the accuracy of 95%. Conclusions: The results showed that hyperspectral reflectance imaging has good potential for discriminating nonviable radish seeds from massive amounts of viable seeds.

• Korean Journal of Optics and Photonics
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• v.32 no.6
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• pp.286-295
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• 2021

A modified catadioptric omnidirectional optical system (MCOOS) using an RGB/NIR CMOS sensor is optically designed for a capsule endoscope with the front field of view (FOV) in visible light (RGB) and side FOV in visible and near-infrared (NIR) light. The front image is captured by the front imaging lens system of the MCOOS, which consists of an additional three lenses arranged behind the secondary mirror of the catadioptric omnidirectional optical system (COOS) and the imaging lens system of the COOS. The side image is properly formed by the COOS. The Nyquist frequencies of the sensor in the RGB and NIR spectra are 90 lp/mm and 180 lp/mm, respectively. The overall length of 12 mm, F-number of 3.5, and two half-angles of front and side half FOV of 70° and 50°-120° of the MCOOS are determined by the design specifications. As a result, a spatial frequency of 154 lp/mm at a modulation transfer function (MTF) of 0.3, a depth of focus (DOF) of -0.051-+0.052 mm, and a cumulative probability of tolerance (CPT) of 99% are obtained from the COOS. Also, the spatial frequency at MTF of 170 lp/mm, DOF of -0.035-0.051 mm, and CPT of 99.9% are attained from the front-imaging lens system of the optimized MCOOS.

• Journal of Biosystems Engineering
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• v.38 no.3
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• pp.199-207
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• 2013

Purpose: Tomatoes, an important agricultural product in fresh-cut markets, are sometimes a source of foodborne illness, mainly Salmonella spp. Growth cracks on tomatoes can be a pathway for bacteria, so its detection prior to consumption is important for public health. In this study, multispectral Visible/Near-Infrared (NIR) reflectance imaging techniques were used to determine optimal wavebands for the classification of defect tomatoes. Methods: Hyperspectral reflectance images were collected from samples of naturally cracked tomatoes. To classify the resulting images, the selected wavelength bands were subjected to two-band permutations, and a supervised classification method was used. Results: The results showed that two optimal wavelengths, 713.8 nm and 718.6 nm, could be used to identify cracked spots on tomato surfaces with a correct classification rate of 91.1%. The result indicates that multispectral reflectance imaging with optimized wavebands from hyperspectral images is an effective technique for the classification of defective tomatoes. Conclusions: Although it can be susceptible to specular interference, the multispectral reflectance imaging is an appropriate method for commercial applications because it is faster and much less expensive than Near-Infrared or fluorescence imaging techniques.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.642-644
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• 2003

SAC is a compact electro-optical camera for imaging in visible-NIR spectral ranges. SAC provides highresolution images over the wide geometric and spectral ranges: 10 m ground sample distance (GSD) and 50 km swath width in the spectral ranges of 520 ${\sim}$ 890 nm. SAC is designed to produce high quality images: modulation transfer function (MTF) of more than 15 %; signal-to-noise ratio (SNR) of more than 100. The missions of SAC incorporate various imaging operations: multi-spectral imaging; super swath-width imaging with cameras in parallel; along-track stereo imaging with slanted 2 cameras.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.172-174
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• 2003

Using MODIS data and several different compositing algorithms utilizing the average cloud free days in a compositing period, maximum ndvi, or dual maximum NDVI/minimum blue, multi resolution composites (250m, 500m, 1km) have been produced for Southeast Asia, with spectral bands ranging from the visible to short-wave infrared with a single band in the thermal (for land and sea surface temperature). A total of nine composites have been produced for the months of May and August in 2003, including blue, green, red, NIR, three in the SWIR, and several to specifically monitor vegetation health.

• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.413-420
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• 2012

Spectroscopic measurement method based on visible and near-infrared wavelengths was prominent technology for rapid and non-destructive evaluation of internal quality of fruits. Reflectance measurement was performed to evaluate firmness, soluble solid content, and acid content of truss tomatoes by hyperspectral reflectance imaging system. The Vis/NIR reflectance spectra was acquired from truss tomatoes sorted by 6 ripening stages. The multivariable analysis based on partial least square (PLS) was used to develop regression models with several preporcessing methods, such as smoothing, normalization, multiplicative scatter correction (MSC), and standard normal variate (SNV). The best model was selected in terms of coefficient of determination of calibration ($R_c^2$) and full cross validation ($R_{cv}^2$), and root mean standard error of calibration (RMSEC) and full cross validation (RMSECV). The results of selected models were 0.8976 ($R_p^2$), 6.0207 kgf (RMSEP) with gaussian filter of smoothing, 0.8379 ($R_p^2$), $0.2674^{\circ}Bx$ (RMSEP) with the mean of normalization, and 0.7779 ($R_p^2$), 0.1033% (RMSEP) with median filter of smoothing for firmness, soluble solid content (SSC), and acid content, respectively. Results show that Vis / NIR hyperspectral reflectance imaging technique has good potential for the measurement of internal quality of truss tomato.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1354-1363
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• 1993

Optical methods were developed to examine their feasibility for quality evaluation of peanut with pod. Surface color and internal quality of peanut were measured without contact. The surface color of peanut was measured by light reflectance at a region of visible wavelengths. Its characteristic was high correlated with a visual grading of peanut. A trial machine for the color grading of peanut was developed using an optical sensor and it was considered to compare with the visual grading. The spectral reflectance at a region of near infrared wavelengths from 1,200 to 2,500nm was measured , and the chemical components of peanut were related to spectral reflectance at special wavelengths. The protein, fat and moisture contents of peanut were estimated by the near infrared methods. An infrared imaging method was developed to evaluate the internal quality of peanut with pod. As thermal characteristic of peanut with pod was deeply related to internal quality , the quality of peanut can be evaluated by temperature changes on the surface of peanut. Measurement of surface color, near infrared reflectance and thermal imaging were shown to be very effective in grading of peanut with pod.