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.

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.

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.

The purpose of this study was to investigate alteration of chemical properties in red leaf lettuces(Lactuca sativa L.) exposed to digital music every day. The red leaf lettuces were cultivated in two hydroponic systems composed of two layers. In the first experiment, the red leaf lettuces with treatment were exposed to the digital music, while the lettuces under control condition were not exposed to the digital music. At harvest(6 weeks after planting), fresh weight and 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(4 weeks and 6 weeks after planting). The comparison of data for all experiment was also divided into upper and lower parts because of the difference of temperature in hydroponic systems. As a results, fresh weight and anthocyanin of the red leaf lettuces might be influenced by the difference of temperature variations. Chlorophyll of the red leaf lettuces was not easily influenced by digital music and difference of temperature. It was also shown that ascorbic acid as inactive molecule was not easily influenced by physical response like music.

We address improved plant image segmentation based on histograms which requires using a vegetation index and threshold. Image segmentation is the most important step for extracting targets, such as vegetation, from images; this affects successful detection of plant information. Forty-two field images were acquired from a soybean field using an RGB camera. Through K-means clustering analysis, we built a new vegetation index        and generated gray-scale images. Otsu and Triangle thresholds were used to convert contrast images to binary. Optimal threshold values were generally located between the Otsu and Triangle threshold values. The combined threshold method shows 98.79% and 0.95% of mean accuracy and standard deviation, respectively, whereas the Otsu and Triangle method results show 98.17±1.71% and 97.85±1.87%, respectively. These results show that the combined method has significant segmentation potential through one-way ANOVA. Then we compared the results with K-means clustering using two-sample t-test. The K-means method’s mean accuracy is 98.18±1.79%, with no significant difference between the proposed and K-means methods. However, the proposed method’s processing time is 0.60±0.01 s, i.e., twice faster than the K-means method (1.72±0.24 s).