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강현석,김기철,오성균 全北大學校 基礎科學硏究所 1986 基礎科學 Vol.9 No.1
For implementing to design data management in CAD environment, the design transaction must be managed effectively. In this paper, we compared the most known two techniques about this issue. And also we described a design transaction management which is adapted to our proposed design object management system.[8]
Estimating Moisture Content of Cucumber Seedling Using Hyperspectral Imagery
Kang, Jeong-Gyun,Ryu, Chan-Seok,Kim, Seong-Heon,Kang, Ye-Seong,Sarkar, Tapash Kumar,Kang, Dong-Hyeon,Kim, Dong Eok,Ku, Yang-Gyu Korean Society for Agricultural Machinery 2016 바이오시스템공학 Vol.41 No.3
Purpose: This experiment was conducted to detect water stress in terms of the moisture content of cucumber seedlings under water stress condition using a hyperspectral image acquisition system, linear regression analysis, and partial least square regression (PLSR) to achieve a non-destructive measurement procedure. Methods: Changes in the reflectance spectrum of cucumber seedlings under water stress were measured using hyperspectral imaging techniques. A model for estimating moisture content of cucumber seedlings was constructed through a linear regression analysis that used the moisture content of cucumber seedlings and a normalized difference vegetation index (NDVI). A model using PLSR that used the moisture content of cucumber seedlings and reflectance spectrum was also created. Results: In the early stages of water stress, cucumber seedlings recovered completely when sub-irrigation was applied. However, the seedlings suffering from initial wilting did not recover when more than 42 h passed without irrigation. The reflectance spectrum of seedlings under water stress decreased gradually, but increased when irrigation was provided, except for the seedlings that had permanently wilted. From the results of the linear regression analysis using the NDVI, the model excluding wilted seedlings with less than 20% (n=97) moisture content showed a precision ($R^2$ and $R^2_{\alpha}$) of 0.573 and 0.568, respectively, and accuracy (RE) of 4.138% and 4.138%, which was higher than that for models including all seedlings (n=100). For PLS regression analysis using the reflectance spectrum, both models were found to have strong precision ($R^2$) with a rating of 0.822, but accuracy (RMSE and RE) was higher in the model excluding wilted seedlings as 5.544% and 13.65% respectively. Conclusions: The estimation model of the moisture content of cucumber seedlings showed better results in the PLSR analysis using reflectance spectrum than the linear regression analysis using NDVI.
Ye Seong Kang,Seong Heon Kim,Jeong Gyun Kang,Tapash Kumar Sarkar,Young Seok Kwon,Sae Rom Jun,Won Jun Kim,Chan Seok Ryu 경상대학교 농업생명과학연구원 2017 농업생명과학연구 Vol.51 No.4
It is necessary to monitor growth status of the crops due to continuous change of climate causing insecurity in crop cultivation. Low altitude remote sensing(LARS) system is utilized to accurately monitor the growth status of the crops. In this study, models for monitoring fresh weight(FW), one of the major growth factors of Chinese cabbage, were developed with structural indices and simple ratio calculated from bands in remotely sensed canopies by NIR, RE(imaging sensor A) and multispec-4c sensors(imaging sensor B) equipped with fixed-wing UAV depending on vegetation stages of normal planting(NP) and delayed planting(DP) Chinese cabbages. In results of imaging sensor A, the estimation models using structural indices and simple ratio were divided into NP and DP due to different attribute of reflectance in canopies with changed environment condition depending on different planting dates. The estimation models using simple ratio calculated by red edge and visible bands of NP showed better performance than other models, but RMSE was high. The models using simple ratio calculated by same bands of DP were feasible to accurately estimate FW(R2 of more than 0.946 with RMSE of less than 169.5 g). In results of imaging sensor B, the estimation models using structural indices and simple ratio on DP were divided into low to intermediate FW and intermediate to high FW. As a result, estimation models of all structural indices and simple ratio in low to intermediate FW bands were advisable to estimate FW(R2 of more than 0.860 with RMSE of less than 104.7 g). Estimation models of those calculated by red edge and visible bands in intermediate to high FW were only possible to accurately estimate FW(R2 of more than 0.532 with RMSE of less than 400.7 g).
Estimating Moisture Content of Cucumber Seedling Using Hyperspectral Imagery
( Jeong-gyun Kang ),( Chan-seok Ryu ),( Seong-heon Kim ),( Ye-seong Kang ),( Tapash Kumar Sarkar ),( Dong-hyeon Kang ),( Dong Eok Kim ),( Yang-gyu Ku ) 한국농업기계학회 2016 바이오시스템공학 Vol.41 No.4
This experiment was conducted to detect water stress in terms of the moisture content of cucumber seedlings under water stress condition using a hyperspectral image acquisition system, linear regression analysis, and partial least square regression (PLSR) to achieve a non-destructive measurement procedure. Methods: Changes in the reflectance spectrum of cucumber seedlings under water stress were measured using hyperspectral imaging techniques. A model for estimating moisture content of cucumber seedlings was constructed through a linear regression analysis that used the moisture content of cucumber seedlings and a normalized difference vegetation index (NDVI). A model using PLSR that used the moisture content of cucumber seedlings and reflectance spectrum was also created. Results: In the early stages of water stress, cucumber seedlings recovered completely when sub-irrigation was applied. However, the seedlings suffering from initial wilting did not recover when more than 42 h passed without irrigation. The reflectance spectrum of seedlings under water stress decreased gradually, but increased when irrigation was provided, except for the seedlings that had permanently wilted. From the results of the linear regression analysis using the NDVI, the model excluding wilted seedlings with less than 20% (n=97) moisture content showed a precision (R² and R²<sub>a</sub>) of 0.573 and 0.568, respectively, and accuracy (RE) of 4.138% and 4.138%, which was higher than that for models including all seedlings (n=100). For PLS regression analysis using the reflectance spectrum, both models were found to have strong precision (R²) with a rating of 0.822, but accuracy (RMSE and RE) was higher in the model excluding wilted seedlings as 5.544% and 13.65% respectively. Conclusions: The estimation model of the moisture content of cucumber seedlings showed better results in the PLSR analysis using reflectance spectrum than the linear regression analysis using NDVI.
김성헌(Seong-Heon Kim),강정균(Jeong-Gyun Kang),유찬석(Chan-Seok Ryu),강예성(Ye-Seong Kang),Tapash Kumar Sarkar,강동현(Dong Hyeon Kang),구양규(Yang-Gyu Ku),김동억(Dong-Eok Kim) (사)한국생물환경조절학회 2018 생물환경조절학회지 Vol.27 No.1
본 연구는 초분광 영상을 이용하여 오이 및 수박과 같은 박과 묘의 수분함량을 추정하기 위해 수행되었다. 오이와 수박 묘 샘플에 수분 스트레스를 가한 후 초분광 영상 취득 시스템을 이용하여 오이와 수박 묘 잎을 촬영하여 반사율을 계산하였고, 건조기를 이용하여 해당 모종의 수분함량을 측정하였다. 마지막으로 영상의 반사율과 수분함량을 이용하여 부분최소제곱회귀분석을 통해 수분함량 추정모델을 개발하였다. 오이 묘 수분함량 추정모델은 R² 0.73, RMSE 1.45%, RE 1.58%의 성능을 보였으며, 수박 묘 수분함량 추정모델은 R² 0.66, RMSE 1.06%, RE 1.14%의 성능을 보였다. 유효범위를 넘어가는 극단치를 제거하여 모델의 성능을 다시 분석한 결과, 오이 모델의 경우 R² 0.79, RMSE 1.10%, RE 1.20으로 상승하였다. 오이와 수박 묘를 함께 분석하여 모델을 제작한 결과, R² 0.67, RMSE 1.26, RE 1.36으로 분석되었다. 오이 모델이 수박 모델보다 비교적 높은 성능을 보였는데, 이러한 원인은 오이의 수분함량 변이가 넓게 분포되어 있었기 때문이라고 판단된다. 또한 데이터셋에서 유효범위를 넘어가는 극단치를 제거한 결과 오이 모델의 정확도 및 정밀도가 상승하였다. 결론적으로 오이 및 수박 묘 수분함량 추정모델들의 추정선의 기울기 차가 크지 않고, 서로 교차되기 때문에 두 모델들은 모두 수분함량을 추정하는데 있어서 유의한 것으로 판단된다. 또한 샘플의 변수가 넓게 분포된 변이를 갖는다면 추정모델의 정확도와 정밀도는 분명 상승할 것이며, 개선된 모델을 이용하면 저가형 센서를 개발하는데 활용될 수 있을 것으로 사료된다. This research was conducted to estimate moisture content in cucurbitaceae seedlings, such as cucumber and watermelon, using hyperspectral imagery. Using a hyperspectral image acquisition system, the reflectance of leaf area of cucumber and watermelon seedlings was calculated after providing water stress. Then, moisture content in each seedling was measured by using a dry oven. Finally, using reflectance and moisture content, the moisture content estimation models were developed by PLSR analysis. After developing the estimation models, performance of the cucumber showed 0.73 of R², 1.45% of RMSE, and 1.58% of RE. Performance of the watermelon showed 0.66 of R², 1.06% of RMSE, and 1.14% of RE. The model performed slightly better after removing one sample from cucumber seedlings as outlier and unnecessary. Hence, the performance of new model for cucumber seedlings showed 0.79 of R², 1.10% of RMSE, and 1.20% of RE. The model performance combined with all samples showed 0.67 of R², 1.26% of RMSE, and 1.36% of RE. The model of cucumber showed better performance than the model of watermelon. This is because variables of cucumber are consisted of widely distributed variation, and it affected the performance. Further, accuracy and precision of the cucumber model were increased when an insignificant sample was eliminated from the dataset. Finally, it is considered that both models can be significantly used to estimate moisture content, as gradients of trend line are almost same and intersected. It is considered that the accuracy and precision of the estimating models possibly can be improved, if the models are constructed by using variables with widely distributed variation. The improved models will be utilized as the basis for developing low-priced sensors.
고정익 무인기로 획득한 다중분광 영상을 이용한 무의 생육 추정
강예성 ( Ye Seong Kang ),김성헌 ( Seong Heon Kim ),강정균 ( Jeong Gyun Kang ),전새롬 ( Sae Rom Jun ),김원준 ( Won Jun Kim ),타파스쿠마 ( Tapash Kumar Sarkar ),유찬석 ( Chan Seok Ryu ) 한국농업기계학회 2016 한국농업기계학회 학술발표논문집 Vol.21 No.2
본 연구는 고정익 무인기(eBee, Sensefly, Swiss)에 탑재된 다중분광센서(Multispec4C, Airinov, Switzerland)로 취득한 무 포장의 영상(공간분해능:6cm)을 이용하여 산출한 식생지수로 잎 생체중과 수확량을 추정하기 위해 수행되었다. 다중분광센서의 Green, Red, Red edge, NIR 파장영역을 이용하여 무의 캐노피를 생육단계별로 촬영하였고 5개 무의 잎에 평균 반사값으로 NDVI와 개체영역을 지정하면서 생길 수 있는 토양의 영향을 최소화하기 SAVI 및 OSAVI를 각각 산출하였다. 산출된 식생지수(NDVI, SAVI, OSAVI)와 측정한 생육 데이터를 이용하여 상관 및 회귀분석을 하였다. 무의 수확량 추정모델을 분석한 결과 무의 생육 기간이 길어짐(50일 이후)에 따라 NDVI, SAVI 및 OSAVI값이 포화되는 경향이 나타나 모델의 정확도 및 정밀도가 낮아졌다. 따라서 생육기간이 50일 이하인 데이터를 이용하여 다시 분석한 결과 NDVI를 이용한 모델에서는 정확도와 정밀도가 높아졌으나 SAVI 및 OSAVI의 결과보다는 낮았다. SAVI를 이용한 추정모델은 정확도(R2)가 0.718, 정밀도(RMSE)가 162.4g로 나타났고, OSAVI를 이용한 추정모델은 정확도(R2)가 0.653, 정밀도(RMSE)가 180.1g로 나타났다. 무 잎의 생체중은 무의 수확량과 높은 상관성 r=0.997을 보였기 때문에 무의 수확량 추정모델과 비슷한 경향을 보였다. 결과적으로 무의 수확량과 무 잎의 생체중을 추정하기 위한 모델에서 생육기간에 따라 큰 성능차이를 보였고 그 중 식생지수 SAVI를 이용한 모델이 다른 식생지수를 이용한 모델보다 개체영역별 무의 생육을 추정하기에 가장 좋은 모델의 결과를 보였다.