• 한국작물학회:학술대회논문집
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• 한국작물학회 1998년도 춘계 학술대회지
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• pp.199-200
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• 1998

• 한국작물학회:학술대회논문집
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• 한국작물학회 1997년도 춘계 학술대회지
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• pp.56-57
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• 1997

• 한국작물학회:학술대회논문집
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• 한국작물학회 2004년도 춘계 학술대회지
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• pp.178-179
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• 2004

• 한국작물학회:학술대회논문집
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• 한국작물학회 2004년도 춘계 학술대회지
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• pp.258-259
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• 2004

• 한국작물학회:학술대회논문집
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• 한국작물학회 1997년도 춘계 학술대회지
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• pp.54-55
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• 1997

• 한국농공학회지
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• 제41권1호
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• pp.52-59
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• 1999

Paddy rice is semi-tropical crop and requires warmirrigation water. If mean water temperature at the water source during the growing period is below 18$^{\circ}C$, sime kinds of water warming mechanism should be taken. In this study irrigation water temperature is measured and preventive measures to cold water damage on paddy rice are suggested. Field observations were performed at 100ha field area downtream of the Unmoon reservoir during the growing season of 1997. Land use, canal system, water temperature at irrigation canals. reservoir, and paddy fields were observed. In addition, growth and yield of the rice at selected plots were observed. Accordingly to the record, cold water damage occurred in this area due to the cold irrigation water supply in 1996. It did not occur because of the effective irrigation water management practice in 1997. However, several preventive measures such as pontoon intake system, using existing weir and construting a new warming pond, are suggested to prevent cold water damage in the future. If a new warming pond is construted to raise irrigation water temperature by 2 $^{\circ}C$, a pond area of 2.94 ha is required.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.381-394
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• 2005

In order to estimate the damage of orchards due' to natural disasters such as typhoon, severe rain, freezing or frost, it is necessary to estimate the number of fruit bearing before and after the damage. To estimate the fruit bearing after the damages are easily done by delegations, but it cost too high to survey every insured farm household and calculate the fruit bearing before the damage. In this article, we suggest to use a random coefficient model to predict the numbers of fruit bearing in the orchards before the damage based on the tree age and the area information.

• 한국초지조사료학회지
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• 제41권4호
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• pp.287-294
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• 2021

본 연구는 기계학습을 통한 수량예측모델을 이용하여 이상기상에 따른 WCM의 DMY 피해량을 산출하기 위한 목적으로 수행하였다. 수량예측모델은 WCM 데이터 및 기상 데이터를 수집 후 가공하여 8가지 기계학습을 통해 제작하였으며 실험지역은 경기도로 선정하였다. 수량예측모델은 기계학습 기법 중 정확성이 가장 높은 DeepCrossing (R²=0.5442, RMSE=0.1769) 기법을 통해 제작하였다. 피해량은 정상기상 및 이상기상의 DMY 예측값 간 차이로 산출하였다. 정상기상에서 WCM의 DMY 예측값은 지역에 따라 차이가 있으나 15,003~17,517 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 DMY 예측값은 지역 및 각 이상기상 수준에 따라 차이가 있었으며 각각 14,947~17,571 kg/ha, 14,986~17,525 kg/ha 및 14,920~17,557 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 피해량은 각각 -68~89 kg/ha, -17~17 kg/ha 및 -112~121 kg/ha 범위로 피해로 판단할 수 없는 수준이었다. WCM의 정확한 피해량을 산출하기 위해서는 수량예측모델에 이용하는 이상기상 데이터 수의 증가가 필요하다.

• Journal of Microbiology and Biotechnology
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• 제26권12호
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• pp.2138-2140
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• 2016

The goal of this study was to identify a source of natural plant compounds with inhibitory activity against pepper mild mottle virus (PMMoV). We showed, using a half-leaf assay, that murrayafoline-A (1) and isomahanine (2) isolated from the aerial parts of Glycosmis stenocarpa have inhibitory activity against PMMoV through curative, inactivation, and protection effects. Using a leaf-disk assay, we confirmed that 2 inhibited virus replication in Nicotiana benthamiana. Using electron microscopy, we found that a mixture of the virus with 2 resulted in damage to the rod-shaped virus.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.