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강예성,전새롬,장시형,박준욱,송혜영,류찬석 경상국립대학교 농업생명과학연구원 2020 농업생명과학연구 Vol.54 No.3
In this paper, the model for predicting yields of chinese cabbages of each cultivar (joined-up in 2015 and wrapped-up in 2016) was developed after the reflectance of hyperspectral imagery was merged as 10 nm, 25 nm and 50 nm of FWHM (full width at half maximum). Band rationing was employed to minimize the unstable reflectance of multi-temporal hyperspectral imagery. The stepwise analysis was employed to select key band ratios to predict yields in all cultivars. The key band ratios selected for each of FWHM were used to develop the yield prediction models of chinese cabbage for all cultivars (joined-up & wrapped-up) and each cultivar (joined-up, wrapped-up). Effective accumulated temperature (EAT) was added in the models to evaluate its improvement of performances. In all models, the performance of models was improved with adding of EAT. The models with EAT for each of FWHM showed the predictability of yields in all cultivars as R2≥0.80, RMSE≤694 g/plant and RE≤28.3%. Such as this result, if the yield can be predicted regardless of the cultivar, it is considered to be advantageous when predicting the yield over a wide area because it is not require a cultivar classification work as pre-processing in imagery.
Influence of Digital Music on Chemical Properties in Red Leaf Lettuce
강예성,김성헌,류찬석 경상대학교 농업생명과학연구원 2016 농업생명과학연구 Vol.50 No.5
The purpose of this study was to investigate alteration of chemical properties in red leaflettuces(Lactuca sativa L.) exposed to digital music every day. The red leaf lettuces werecultivated in two hydroponic systems composed of two layers. In the first experiment, the redleaf lettuces with treatment were exposed to the digital music, while the lettuces under controlcondition were not exposed to the digital music. At harvest(6 weeks after planting), fresh weightand chlorophyll content were measured and compared the treatment with the control group. Subsequently, red leaf lettuces of the next experiment were compared to fresh weight,chlorophyll, ascorbic acid and anthocyanin content during different vegetation growth stages(4weeks and 6 weeks after planting). The comparison of data for all experiment was also dividedinto upper and lower parts because of the difference of temperature in hydroponic systems. Asa results, fresh weight and anthocyanin of the red leaf lettuces might be influenced by thedifference of temperature variations. Chlorophyll of the red leaf lettuces was not easilyinfluenced by digital music and difference of temperature. It was also shown that ascorbic acidas inactive molecule was not easily influenced by physical response like music.
강예성,김성헌,강정균,홍영기,Tapash Kumar Sarkar,류찬석 경상대학교 농업생명과학연구원 2016 농업생명과학연구 Vol.50 No.6
Recently, remote sensing technology as a nondestructive method has been utilized to detectthe quantity and quality of crops using unmanned aerial system. To predict vegetation growth(leaf dry mass and nitrogen content) of soybean, two vegetation index(NDVI and Green NDVI)were calculated from images acquired by multi-spectral camera mounted on a UAV and eachprediction models between vegetation growth and index were evaluated. As a result, there wasno significant difference between vegetation growth and index when each vegetation stage foreach yellow and black bean were compared to each other. However, there was significantdifference between vegetation growth and index when all vegetation stage for each yellow andblack bean were compared to each other. Moreover, there was significant difference betweenvegetation growth and NDVI(r= 0.799 for leaf dry mass, r= 0.796 for nitrogen content), andGreen NDVI(r= 0.860 for leaf dry mass, r= 0.845 for nitrogen content) for all vegetation stageswith all soybeans. The accuracy and precision of Green NDVI model(R2= 0.740 for leaf drymass, R2= 0.714 for nitrogen content) were better than those of NDVI model regardless ofvarieties and vegetation growth. Therefore, Green NDVI has considerable potential to detect thequantity and quality of soybeans.
강예성,류찬석,김성헌,전새롬,장시형,박준우,Tapash Kumar Sarkar,송혜영 한국농업기계학회 2018 바이오시스템공학 Vol.43 No.2
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 R2 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 R2, 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 R2, 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.
지상 및 항공 초분광 이미지를 이용한 무와 배추 재배면적 예측을 위한 주요 파장 선정
강예성 ( Ye Seong Kang ),전새롬 ( Sae Rom Jun ),박준우 ( Jun Woo Park ),송혜영 ( Hye Young Song ),장시형 ( Si Hyeong Jang ),유찬석 ( Chan Seok Ryu ) 한국농업기계학회 2019 한국농업기계학회 학술발표논문집 Vol.24 No.2
본 연구에서는 지상에서 취득된 무와 배추의 초분광 이미지를 의사결정트리법으로 분석하여 생육 시기에 따라 무와 배추를 분류할 수 있는 주요 파장을 선정하였다. 선택된 주요 파장을 항공 초분광 이미지에 적용하여 무와 배추의 분광특성 차이를 이용하여 재배면적을 산출하고 실제로 조사된 재배면적과 비교하였다. 지상 초분광 이미지는 2015년 전라남도 무안군의 무와 배추 포장에서 11월 4일에 Specim PS (SPECIM, Finland)를 이용하여 2 m 높이에서 400 ~ 1000 nm를 분광 해상도 5.2 nm로 취득되었다. 항공 초분광 이미지는 2014년 10월 29일에 전라북도 고창군 대산면을 1500m 고도에서 CASI-1500 (ITRES, Canada)로 동일한 파장범위를 분광 해상도 28.8 nm로 취득되었다. 지상 초분광 이미지의 FWHM 5.2 nm는 항공 초분광 이미지의 FWHM과 비교적 넓은 FWHM으로 구성된 소형 다중분광 이미지 센서 개발을 고려하여 FWHM 25 nm로 평준화하였다. 높은 공간 해상도를 가진 지상 초분광 이미지를 이용하여 무와 배추를 분류하기 위한 의사결정트리 (학습 30%:검증 70%)를 수행한 결과, 중심 파장이 715 nm인 red edge (RE) 영역만이 선택되었고 분류 정확도로 overall accuracy (OA)는 94.7%와 kappa coefficient (KC)는 85.4%였다. 동일한 방법으로 항공 초분광 이미지를 분류한 결과에서도 RE영역에서 중심 파장 701 nm 만 선정되었고 OA는 87.8%와 KC 70.5%였다. 중심 파장이 715 nm 이고 FWHM이 25 nm 인 센서로 항공기 이미지에서 재배면적을 예측한 결과 무 56.2 ha와 배추 68.2 ha로 지상에서 조사된 무 56.3 ha와 배추 68.1 ha의 면적과 0.1 ha 차이였지만 무와 배추 포장의 분류정확도는 각각 80.7% 및 84.7%로 나타났다.
강예성 ( Ye-seong Kang ),박준우 ( Jun-woo Park ),장시형 ( Si-hyeong Jang ),강경석 ( Kyeong-suk Kang ),김태양 ( Tae-yang Kim ),유찬석 ( Chan-seok Ryu ),전새롬 ( Sae-rom Jun ) 한국농업기계학회 2020 한국농업기계학회 학술발표논문집 Vol.25 No.2
본 연구에서는 많은 구조물로 인해 광 균일성을 유지하기 어려운 온실 내에서 작물의 생육 상태를 예측하는 고품질의 분광 영상을 취득하기 위해 인공조명을 이용한 광 균일화 연구를 수행하였다. 실험은 국립식량과학원 온실에서 내부로 입사되는 태양광을 차광하기 위한 99% 차광막을 설치하였고 정육면체 3x3x3 m 자동취득 영상장치 (Hortizen Co. Ltd., Korea)에 1000 w 할로겐 램프를 설치하여 진행하였다. 할로겐 램프는 사방면으로 2개씩 총 8개 장착하였고, 그 간격은 1 m였다. 할로겐 램프는 수직 기준으로 각도별 (0도, 10도, 15도, 45)로 조절한 후 위치별 및 파장별 (400-1000 nm) 반사값을 확인하기 위해 초분광 (FX 10, Specim, Finland) 영상을 취득하였고, 광센서를 이용하여 조명의 광량을 조사하였다. 영상장치 아래에 18% white reference board (EzyBalance, Lastolite Ltd., England)를 좌우 위, 좌우 아래와 중앙에 총 5개를 설치하여 위치별 초분광 영상 반사값을 취득하였다. 광량 조사는 영상장치 아래에서 가로, 세로 0.5 m 간격으로 49개 위치의 광센서 (PM6612, Peakmeter Co. Ltd., China)를 이용하여 취득된 광량 데이터로 조명의 공간적 광 분포를 나타내었다. 최종적으로 각도별 반사값과 광량은 위치 사이에 표준편차를 나타내어 최대한 균일한 광 조건에서 분광 영상을 취득하기에 가장 유리한 각도를 선정하였다. 각도별 초분광 영상의 위치 사이에 반사값의 파장별 표준편차 결과 모든 파장에서 45도, 15도 및 0도 순으로 낮았다. 특히, 600-800 nm에서 가장 큰 차이를 보였으며, 그 반사값은 45도에서 60 이상, 15도에서 약 29-33 사이와 0도에서 26 이하의 표준편차를 보였다. 각도별 광량 분포 결과 0도에서는 410 lux, 10도에서는 582 lux와 15도에서는 694 lux의 표준편차를 보였다. 반사값과 광 분포 모든 결과에서 할로겐 램프가 수직 방향으로 각도가 0일 때 여러 위치 사이에 표준편차가 가장 작아 다른 각도에 비해 광이 균일하다고 판단된다. 추후 다양한 면적 및 높이에서 최적의 할로겐 램프 개수 및 각도를 구명하여 온실과 같은 불안정한 광 조건을 극복할 수 있는 중요한 비전 영역의 기초 기술로 삼을 필요가 있다.
삼진 CMOS 컴팩모델을 기반으로 한 새로운 삼진 곱셈기 설계
강예성(Yesung Kang),김선민(Sunmin Kim),김경록(Kyungrok Kim),강석형(Seokhyeong Kang) 대한전자공학회 2017 대한전자공학회 학술대회 Vol.2017 No.6
Multiple-valued logic (MVL) has potential advantages for energy-efficient design by reducing a circuit complexity. Because of physical device and circuit realization issues, however, there are relatively small number of researches on MVL circuit designs. We design a novel ternary multiplier based on a ternary CMOS (T-CMOS) compact model. The proposed ternary multiplier design achieves significant total power reduction and performance improvement over conventional ternary design.
초분광 이미지를 이용한 배나무 화상병에 대한 최적 분광 밴드 선정
강예성 ( Ye-seong Kang ),박준우 ( Jun-woo Park ),장시형 ( Chan-seok Ryu ),송혜영 ( Si-hyeong Jang ),강경석 ( Hye-young Song ),유찬석 ( Kyung-suk Kang ),김성헌 ( Seong-heon Kim ),전새롬 ( Sae-rom Jun ),강태환 ( Tae-hwan Kang ),김 한국농림기상학회 2021 한국농림기상학회지 Vol.23 No.1
In this study, the possibility of discriminating Fire blight (FB) infection tested using the hyperspectral imagery. The reflectance of healthy and infected leaves and branches was acquired with 5 nm of full width at high maximum (FWHM) and then it was standardized to 10 nm, 25 nm, 50 nm, and 80 nm of FWHM. The standardized samples were divided into training and test sets at ratios of 7:3, 5:5 and 3:7 to find the optimal bands of FWHM by the decision tree analysis. Classification accuracy was evaluated using overall accuracy (OA) and kappa coefficient (KC). The hyperspectral reflectance of infected leaves and branches was significantly lower than those of healthy green, red-edge (RE) and near infrared (NIR) regions. The bands selected for the first node were generally 750 and 800 nm; these were used to identify the infection of leaves and branches, respectively. The accuracy of the classifier was higher in the 7:3 ratio. Four bands with 50 nm of FWHM (450, 650, 750, and 950 nm) might be reasonable because the difference in the recalculated accuracy between 8 bands with 10 nm of FWHM (440, 580, 640, 660, 680, 710, 730, and 740 nm) and 4 bands was only 1.8% for OA and 4.1% for KC, respectively. Finally, adding two bands (550 nm and 800 nm with 25 nm of FWHM) in four bands with 50 nm of FWHM have been proposed to improve the usability of multispectral image sensors with performing various roles in agriculture as well as detecting FB with other combinations of spectral bands.