• 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.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.485-496
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• 2023

In this study, we aimed to apply multispectral imaging (713 - 920 nm, 10 bands) for maturity classification and recognition of oriental melons grown in hydroponic greenhouses. A total of 20 oriental melons were selected, and time series multispectral imaging of oriental melons was 7 - 9 times for each sample from April 21, 2023, to May 12, 2023. We used several approaches, such as Savitzky-Golay (SG), standard normal variate (SNV), and Combination of SG and SNV (SG + SNV), for pre-processing the multispectral data. As a result, 713 - 759 nm bands were preprocessed with SG for the maturity classification of oriental melons. Additionally, a Light Gradient Boosting Machine (LightGBM) was used to train the recognition model for oriental melon. R² of recognition model were 0.92, 0.91 for the training and validation sets, respectively, and the F-scores were 96.6 and 79.4% for the training and testing sets, respectively. Therefore, multispectral imaging in the range of 713 - 920 nm can be used to classify oriental melons maturity and recognize their fruits.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.717-721
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• 2008

This paper proposes an image system that can efficiently measure the spectral reflectance of a scene using RGB cameras and LED light sources. Multispectral imaging system is composed of LED controllers, LED clusters and RGB cameras. It captures full-spectral images at real-time. The system adopts a simple, empirical linear model to estimate the full spectral reflectance at each pixel. Since the model is linear, the reconstruction is efficient and stable. We estimated the spectral reflectance of various scenes using the system and showed the effectiveness of the proposed system.

• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.207-213
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• 2017

In order to accurately perform multispectral imaging using a multiplexed illumination, intensity of illumination in a scene must be uniform. For image acquisition that requires accurate color information, even if not multispectral imaging, the illumination information must be accurate, and a flat light source or illumination calibration is performed for accurate illumination characteristics. In this paper, we propose a method of color correction to uniformly illuminate an image with non-uniform illumination intensity. The proposed method uses multispectral imaging instead of illumination calibration for color correction. First of all, we perform multispectral imaging with two images obtained from non-uniformity illumination to acquire spectral reflectance. The obtained reflection spectrum is relit as the illumination characteristic of the image obtained from general planar light such as fluorescent light or sunlight. By comparing the image obtained by relighting with the uniformly illuminated image, the non-uniformity of the illumination is confirmed, and the color correction is performed as the image obtained from the uniform image. It is expected that the experimental results will confirm whether the non-uniformity of the illumination is uniformly corrected and reduce the restriction of illumination in obtaining the color information of the image.

• Applied Microscopy
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• v.51
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• pp.12.1-12.12
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• 2021

Intravital video microscopy permits the observation of microcirculatory blood flow. This often requires fluorescent probes to visualize structures and dynamic processes that cannot be observed with conventional bright-field microscopy. Conventional light microscopes do not allow for simultaneous bright-field and fluorescent imaging. Moreover, in conventional microscopes, only one type of fluorescent label can be observed. This study introduces multispectral intravital video microscopy, which combines bright-field and fluorescence microscopy in a standard light microscope. The technique enables simultaneous real-time observation of fluorescently-labeled structures in relation to their direct physical surroundings. The advancement provides context for the orientation, movement, and function of labeled structures in the microcirculation.

• Journal of Biosystems Engineering
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• v.39 no.2
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• pp.142-149
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• 2014

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defective apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.93-97
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• 2022

Infrared scenes usually contain also spectral information which cannot be resolved using normal single-band infrared cameras. Multispectral infrared imaging cameras give access to the comprehensive information contained within infrared scenes. A Dual-band infrared Camera, a type of multispectral infrared imaging cameras, has the advantage of simple system. A Dual-band Infrared Camera gives access to the spectral information as wells as the temperature information within infrared scenes. Multispectral imaging generally increases the detection and identification performance of a Dual-band Infrared Camera. This paper describes a design of an infrared Camera using a Dual-band Infrared Detector to simultaneously receive infrared radiation from the medium-wave infrared/long-wave infrared(MWIR/LWIR) bands.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.755-764
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• 1996

A multispectral imaging system with selected wavelength optical filter was demonstrated feasible for food safety inspection. Intensified multispectral images of carcasses were obtained with visible/near-infrared optical filters(542-847 nm wavelengths) and analyzed. The analysis of textural features based on co-occurrence matrices was conducted to determine the feasibility of a multispectral image analyses for discriminating unwholesome poultry carcasses from wholesome carcasses. The mean angular second moment of the wholesome carcasses scanned at 542 nm wavelength was lower than that of septicemic (P$\leq$0.0005) and cadaver(P$\leq$0.0005) carcasses. On the other hand, for the carcasses scanned at 700nm wavelength , the feature values of septicemic and cadaver carcasses were significantly (P$\leq$0.0005) different from wholesome carcasses. The discriminant functions for classifying poultry carcasses into three classes (wholesome, septicemic , cadaver) were developed using linear and quadr tic covariance matrix analysis method. The accuracy of the quadratic discriminant models, expressed in rates of correct classification, were over 90% for the classification of wholesome, septicemic, and cadaver carcasses when textural features from the spectral images scanned at the wavelength of 542 and 700nm were utilized.

• Journal of Biosystems Engineering
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• v.37 no.4
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• pp.265-270
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• 2012

Purpose: An analytic method to design excitation and emission filters of a multispectral fluorescence imaging system is proposed and was demonstrated in an application to poultry fecal inspection Methods: A mathematical model of a multispectral imaging system is proposed and its system parameters, such as excitation and emission filters, were optimally determined by linear discriminant analysis (LDA). An alternating scheme was proposed for numerical implementation. Fluorescence characteristics of organic materials and feces of poultry carcasses are analyzed by LDA to design the optimal excitation and emission filters for poultry fecal inspection. Results: The most appropriate excitation filter was UV-A (about 360 nm) and blue light source (about 460 nm) and band-pass filter was 660-670 nm. The classification accuracy and false positive are 98.4% and 2.5%, respectively. Conclusions: The proposed method is applicable to other agricultural products which are distinguishable by their spectral properties.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.138-147
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• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.