• Journal of Biosystems Engineering
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• 제38권3호
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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.

• 농업과학연구
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• 제50권3호
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• pp.527-538
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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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• 한국HCI학회 2008년도 학술대회 1부
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• pp.717-721
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• 2008

본 논문에서는 RGB 카메라와 LED 광원만을 이용하여 객체에 대한 반사 스펙트럼을 효율적으로 측정하는 영상 획득 시스템을 제안한다. 멀티스펙트럼 영상 획득 시스템은 LED 컨트롤러, LED 클러스터, RGB 카메라로 구성되고 전역 스펙트럼(full spectrum)의 영상을 실시간으로 획득하는 시스템이다. 제안된 시스템은 스펙트럼 기저 함수들의 선형 결함으로 전역 스펙트럼을 재구성하여 비교적 간단하면서도 높은 정확도를 보장해준다. 본 시스템의 효용성을 증명하기 위해 다양한 장면(scene)에 대한 반사 스펙트럼을 측정하고 이를 이용하여 여러 광원을 적용한 재조명 결과를 보여준다.

• 방송공학회논문지
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• 제22권2호
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• pp.207-213
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• 2017

다수의 조명을 이용한 멀티스펙트럴 이미징을 정확히 수행하기 위해서는 영상 내 조명의 세기가 균일해야 한다. 멀티스펙트럴 이미징이 아니더라도 정확한 색 정보가 필요한 영상 획득에서는 조명이 정확해야 하고, 정확한 조명 특성을 위해 평면 광원을 사용하거나 조명 캘리브레이션을 수행한다. 본 논문에서는 조명의 세기가 균일하지 않은 영상을 조명의 세기가 균일하도록 색상을 보정하는 방법을 제안한다. 우선 비균일 조명에서 얻은 두 영상으로 멀티스펙트럴 이미징을 수행하여 반사 스펙트럼을 획득하고 획득한 반사 스펙트럼을 형광등이나 태양광과 같은 평면광에서 획득한 영상의 조명 특성으로 재조명한다. 재조명으로 얻은 영상과 평면광 영상의 조도 분포의 차이를 이용해서 비균일 조명 영상을 균일한 영상에서 획득한 영상처럼 색상 보정을 수행한다. 실험 결과로 조명의 비균일성이 균일하게 보정되었는지 확인하고, 이 결과를 통해 영상의 색 정보를 취득하는 데 조명의 제약사항을 줄일 수 있을 것으로 기대된다.

• Applied Microscopy
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• 제51권
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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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• 제39권2호
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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.

• 한국인터넷방송통신학회논문지
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• 제22권5호
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• pp.93-97
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• 2022

적외선 영상은 온도 정보뿐만 아니라 파장 정보를 포함하고 있으며 이는 단일 파장의 적외선 카메라로는 분석이 불가능하다. 다중 파장 적외선 카메라는 적외선 영상에 포함된 광범위한 정보를 획득할 수 있다. 다중 파장 적외선 카메라의 한 종류인 이중대역 적외선 카메라는 시스템 구성을 쉽게 할 수 있다는 이점이 있다. 이중대역 적외선 카메라는 적외선 영상에 포함된 온도정보 뿐만 아니라 파장 정보를 획득할 수 있고 이를 통해 적외선 카메라의 탐지/식별 성능을 향상시킬 수 있다. 본 논문에서는 중적외선 대역과 원적외선 대역을 동시에 획득할 수 있는 이중대역 적외선 검출기를 이용한 적외선 카메라의 설계에 대하여 기술한다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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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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• 제37권4호
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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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• 제43권2호
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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.