• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.384-392
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• 2022

The objective of this study was to estimated nitrogen content and chlorophyll using RGB, Hyperspectral sensors to diagnose of nitrogen nutrition in apple tree leaves. Spectral data were acquired through image processing after shooting with high resolution RGB and hyperspectral sensor for two-year-old 'Hongro/M.9' apple. Growth data measured chlorophyll and leaf nitrogen content (LNC) immediately after shooting. The growth model was developed by using regression analysis (simple, multi, partial least squared) with growth data (chlorophyll, LNC) and spectral data (SPAD meter, color vegetation index, wavelength). As a result, chlorophyll and LNC showed a statistically significant difference according to nitrogen fertilizer level regardless of date. Leaf color became pale as the nutrients in the leaf were transferred to the fruit as over time. RGB sensor showed a statistically significant difference at the red wavelength regardless of the date. Also hyperspectral sensor showed a spectral difference depend on nitrogen fertilizer level for non-visible wavelength than visible wavelength at June 10th and July 14th. The estimation model performance of chlorophyll, LNC showed Partial least squared regression using hyperspectral data better than Simple and multiple linear regression using RGB data (Chlorophyll R²: 81%, LNC: 81%). The reason is that hyperspectral sensor has a narrow Full Half at Width Maximum (FWHM) and broad wavelength range (400-1,000 nm), so it is thought that the spectral analysis of crop was possible due to stress cause by nitrogen deficiency. In future study, it is thought that it will contribute to development of high quality and stable fruit production technology by diagnosis model of physiology and pest for all growth stage of tree using hyperspectral imagery.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.

• THE JOURNAL OF KOREAN ORIENTAL ONCOLOGY
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• v.4 no.1
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• pp.37-53
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• 1998

The sprig of Jinryungtang Gagambang has been used for curing as a traditional medicine without any experimental evidence to support the rational basis for their clinical use. This experiment was carried out to evaluate the possible therapeutic or antitumoral effects of Jinryungtang Gagambang extract against cancer, and to study some mechanisms responsible for its effect. The cytotoxic and antitumor effects were evaluated on human cell liens (A549, hep3B, Caki-1, Sarcoma 180) after exposure to Jinryungtang Gagambang extract using in ILS, colony forming efficency and SRB assay which were regarded as a valuable method for cytotoxic and antitumor effects of unknown compound on tumor cell lines. The results obtained in this studies were as follows. 1. As a result of exposure to Jinryungtang Gagambang extract, the proliferation of A549, hep3B, Caki-1, good correlations were shown from the results of SRB assay and those of clogenetic assay. 2. The oral administration of Jinryungtang Gagambang extract showed significant effects of increase of MST(mean survival time) and ILS(increased life span) depending on the increasing concentration. 3. Against squamous cell carcinoma induced by MCA, Jinryungtang Gagambang decreased not only the frequency of tumor production but also the number and weight of tumors per tumor bearing mice(TBM). Jinryungtang Gagambang also significantly suppressed the development of 3LL cell-implanted tumors by frequency and their size, and some developed tumors were regressed by the continuous treatment of Jinryungtang Gagambang extract into TBM. 4. Jinryungtang Gagambang extract also increased NK cell activities. According to the above results, it could be suggested that Jinryungtang Gagambang extract has prominent antiutmor effect.