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 추 정모델은 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 1.36으 로 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 모델 들은 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 활용 될 수 있을 것으로 사료된다.

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).

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.