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FT-NIR을 이용한 상추(Lactuca sativa L) 종자의 비파괴 선별 기술에 관한 연구
안치국(Chi-Kook Ahn),조병관(Byoung-Kwan Cho),강점순(Jum-Soon Kang),이강진(Kang-Jin Lee) 충남대학교 농업과학연구소 2012 농업과학연구 Vol.39 No.1
Nondestructive evaluation of seed viability is one of the highly demanding technologies for seed production industry. Conventional seed sorting technologies, such as tetrazolium and standard germination test are destructive, time consuming, and labor intensive methods. Near infrared spectroscopy technique has shown good potential for nondestructive quality measurements for food and agricultural products. In this study, FT-NIR spectroscopy was used to classify normal and artificially aged lettuce seeds. The spectra with the range of 1100~2500 nm were scanned for lettuce seeds and analyzed using the principal component analysis(PCA) method. To classify viable seeds from nonviable seeds, a calibration modeling set was developed with a partial least square(PLS) method. The calibration model developed from PLS resulted in 98% classification accuracy with the Savitzky-Golay 1st derivative preprocessing method. The prediction accuracy for the test data set was 93% with the MSC(Multiplicative Scatter Correction) preprocessing method. The results show that FT-NIR has good potential for discriminating non-viable lettuce seeds from viable ones.
초분광 반사광 영상을 이용한 무(Raphanus sativus L) 종자의 발아와 불발아 비파괴 판별
안치국 ( Chi Kook Ahn ),모창연 ( Chang Yeun Mo ),강점순 ( Jum Soon Kang ),조병관 ( Byoung Kwan Cho ) 한국농업기계학회 2012 바이오시스템공학 Vol.37 No.6
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.
초분광 반사광 영상을 이용한 배추 종자(Brassica campestris L) 비파괴 품질 측정기술 개발
안치국,백인석,모창연,강석원,김문성,조병관 한국산업식품공학회 2012 산업 식품공학 Vol.16 No.3
본 연구에서는 가시광 및 근적외선 초분광 반사광 영상 시스템을 이용하여 배추의 건전종자와 퇴화종자를 선별할 수 있는 기술 개발에 관한 연구를 수행하였다. 초분광 반사광 영상을 이용하여 배추의 건전종자와 퇴화종자를 선별할 수 있는 최적의 반사광 파장 조합을 구명하고 이를 이용하여 퇴화종자를 검출할 수 있는 초분광 영상 알고리즘을 제시하였다. 본 논문의 전체적인 결론을 요약하면 다음과 같다. 가) 배추의 건전종자와 퇴화종자를 구별하기 위해 초분광 반사광 스펙트럼을 이용하여 PLS-DA 모델을 개발하고 성능평가를 수행하였다. Calibration set의 분류 정확도 97.6%이고 test set의 분류 정확도는 96.9% 이었다. 나) 배추의 건전종자와 퇴화종자를 분류하는데 가장 큰 영향을 미치는 파장대는 680 nm로 확인 되었으며, 이는 배추종자가 퇴화하는 과정에서 발생하는 chlorophyll 변화의 영향으로 사료된다. 다) 개발한 PLS-DA모델의 beta coefficient를 적용한 PLS의 영상을 이용하여 건전종자와 퇴화종자를 선별한 결과 분류정확도 96.8%로 육안 및 일반 컬러 카메라로 선별하기 힘든 배추의 퇴화종자 검출이 가능한 것을 확인할 수 있었다. Cabbage (Brassica campestris L) is an important crop for Asian countries, and especially so for Korea, Japan and China. In order to achieve uniform and high-yield rates of cabbage product, seed lot quality needs to be controlled. Non-destructive evaluation of seed viability is an important technique for investigating seed quality. Hyperspectral imaging technique, which combines the features of imaging and spectroscopy, has been considered one of the most powerful nondestructive evaluation methods allowing comprehensive analysis of the physical and biochemical characteristics of materials. In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the evaluation of seed viability. For the investigation of viable and non-viable seeds, some viable seeds were artificially aged. Hyperspectral reflectance technique was used to discriminate aged cabbage seeds from normal seeds. The PLSDA and simple image threshold methods were applied to investigate the feasibility of distinguishing the aged seeds from the normal seeds. The discrimination accuracy was 96.7% for the calibration set and 96.9% for the test set. The resultant images from the PLS-DA method showed high classification performance in distinguishing the nonviable from the viable seeds, which is an impossible task by naked eye and by conventional color cameras. Hyperspectral reflectance imaging has good potential for discriminating nonviable cabbage seeds from massive amounts of viable cabbage seeds.
FT-NIR을 이용한 상추(Lactuca sativa L) 종자의 비파괴 선별 기술에 관한 연구
안치국(Chi-Kook Ahn),조병관(Byoung-Kwan Cho),강점순(Jum-Soon Kang),이강진(Kang-Jin Lee) 충남대학교 농업과학연구소 2012 Korean Journal of Agricultural Science Vol.39 No.1
Nondestructive evaluation of seed viability is one of the highly demanding technologies for seed production industry. Conventional seed sorting technologies, such as tetrazolium and standard germination test are destructive, time consuming, and labor intensive methods. Near infrared spectroscopy technique has shown good potential for nondestructive quality measurements for food and agricultural products. In this study, FT-NIR spectroscopy was used to classify normal and artificially aged lettuce seeds. The spectra with the range of 1100~2500 nm were scanned for lettuce seeds and analyzed using the principal component analysis(PCA) method. To classify viable seeds from nonviable seeds, a calibration modeling set was developed with a partial least square(PLS) method. The calibration model developed from PLS resulted in 98% classification accuracy with the Savitzky-Golay 1st derivative preprocessing method. The prediction accuracy for the test data set was 93% with the MSC(Multiplicative Scatter Correction) preprocessing method. The results show that FT-NIR has good potential for discriminating non-viable lettuce seeds from viable ones.
초분광 반사광 영상을 이용한 상추(Lactuca sativa L) 종자의 활력 비파괴측정기술 개발에 관한 연구
안치국(Chi-Kook Ahn),조병관(Byoung-Kwan Cho),모창연(Chang Yeun Mo),Moon S. Kim 한국비파괴검사학회 2012 한국비파괴검사학회지 Vol.32 No.5
본 연구에서는 초분광 반사광 영상기술을 이용하여 비파괴적으로 상추의 건전종자와 퇴화종자를 선별하는 기술을 개발하고자 하였다. 750~1000nm의 근적외선 초분광 반사광 영상의 분광데이터를 이용하여 상추의 발아종자와 불발아 종자를 판별하는 PLS-DA 모델을 개발하고 개발된 모델의 성능 평가를 실시하였다. 모델 calibration의 판별 정확도는 81.6%였으며, test의 결과는 81.2%의 판별 정확도를 보였다. 또한 개발된 PLS-DA 모델을 적용한 초분광 반사광 영상을 이용하여 대량의 불발아 종자를 동시에 영상으로 검출 가능한 영상처리 알고리즘을 개발하였다. 초분광 반사광 영상에 PLS-DA 모델이 적용된 영상을 이용한 검출 정확도는 91%로 나타났으며, 이는 초분광 반사광 영상을 이용하여 대량의 상추 종자의 비파괴 품질선별에 이용될 수 있음을 보여 주었다. In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.
안치국 ( Chi-kook Ahn ),한철우 ( Cheol-woo Han ),김은국 ( Eun-kuk Kim ),천근녕 ( Geun Nyung Chun ),임지섭 ( Ji-seob Lim ),김영태 ( Young Tae Kim ) 한국농업기계학회 2019 한국농업기계학회 학술발표논문집 Vol.24 No.1
본 연구는 시설 원예용 스마트 팜에서 활용되는 외부환경 조절에 사용되어지는 ICT 기자재 중 풍향 및 풍속 센서에 대한 성능시험 방법 및 시스템 등 검인증 메뉴얼을 개발하는데 있다. 현재 스마트온실에 사용되는 센서인터페이스에 대한 표준규격(KS X 3266)과 세계기상기구(WMO) 제반 규정을 고려하여 적합한 시험방법 및 평가시스템(기준기 및 환경발생장치)를 구축하였다. 스마트온실에 사용되는 센서 인터페이스에 대한 표준 규격은 측정범위 풍향 (0~360° (방위각)) 및 풍속 (0~40 m/s), 출력신호 전압신호(0~5 V), 전류신호 (4~20mA), 디지털신호RS485, 9600 bpm), 전원전압(5~24VDC) 및 결선 형식에 대해 정의하였으며 세계기상기구에서는 관측센서 표준규격으로 측정방식, 민감도, 온도특성, 비선형, 안정도, 불확도 및 운영환경 등에 대해여 고려하였다. 이에 본 연구에서 개발된 메뉴얼은 위의 범위 이상에서 센서의 정확도를 평가할 수 있도록 시중에 유통되는 센서 보다 분해능 및 정밀도, 데이터수집속도가 고성능인 기준기 (측정범위: 0~360°, 0~60 m/s, 분해능: 0.1° 및 0.01 m/s, 정확도:±0.3 m/s, ± 2° RMSE from 1.0m/s)를 설정하고 시험방법에 적합한 환경발생장치 (기동풍속(1m/s 이하))를 이용하여 기준기와 시험 센서 간에 비교 시험이 가능하도록 설계되었다.
초분광 반사광 영상을 이용한 배추 종자(Brassica campestris L) 비파괴 품질 측정기술 개발
안치국 ( Chi Kook Ahn ),백인석 ( In Suck Baek ),모창연 ( Chang Yeun Mo ),강석원 ( Suk Won Kang ),김문성 ( Moon S. Kim ),조병관 ( Byoung Kwan Cho ) 한국산업식품공학회 2012 산업 식품공학 Vol.16 No.3
Cabbage (Brassica campestris L) is an important crop for Asian countries, and especially so for Korea, Japan and China. In order to achieve uniform and high-yield rates of cabbage product, seed lot quality needs to be controlled. Non-destructive evaluation of seed viability is an important technique for investigating seed quality. Hyperspectral imaging technique, which combines the features of imaging and spectroscopy, has been considered one of the most powerful nondestructive evaluation methods allowing comprehensive analysis of the physical and biochemical characteristics of materials. In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the evaluation of seed viability. For the investigation of viable and non-viable seeds, some viable seeds were artificially aged. Hyperspectral reflectance technique was used to discriminate aged cabbage seeds from normal seeds. The PLSDA and simple image threshold methods were applied to investigate the feasibility of distinguishing the aged seeds from the normal seeds. The discrimination accuracy was 96.7% for the calibration set and 96.9% for the test set. The resultant images from the PLS-DA method showed high classification performance in distinguishing the nonviable from the viable seeds, which is an impossible task by naked eye and by conventional color cameras. Hyperspectral reflectance imaging has good potential for discriminating nonviable cabbage seeds from massive amounts of viable cabbage seeds.