• Proceedings of the Korean Society of Crop Science Conference
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• 1998.04a
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• pp.199-200
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• 1998

• Proceedings of the Korean Society of Crop Science Conference
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• 1997.05a
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• pp.56-57
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• 1997

• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.178-179
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• 2004

• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.258-259
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• 2004

• Proceedings of the Korean Society of Crop Science Conference
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• 1997.05a
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• pp.54-55
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• 1997

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.1
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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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• v.12 no.2
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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.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.287-294
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• 2021

The objective of this study was conducted to calculate the damage of whole crop maize in accordance with abnormal climate using the forage yield prediction model through machine learning. The forage yield prediction model was developed through 8 machine learning by processing after collecting whole crop maize and climate data, and the experimental area was selected as Gyeonggi-do. The forage yield prediction model was developed using the DeepCrossing (R²=0.5442, RMSE=0.1769) technique of the highest accuracy among machine learning techniques. The damage was calculated as the difference between the predicted dry matter yield of normal and abnormal climate. In normal climate, the predicted dry matter yield varies depending on the region, it was found in the range of 15,003~17,517 kg/ha. In abnormal temperature, precipitation, and wind speed, the predicted dry matter yield differed according to region and abnormal climate level, and ranged from 14,947 to 17,571, 14,986 to 17,525, and 14,920 to 17,557 kg/ha, respectively. In abnormal temperature, precipitation, and wind speed, the damage was in the range of -68 to 89 kg/ha, -17 to 17 kg/ha, and -112 to 121 kg/ha, respectively, which could not be judged as damage. In order to accurately calculate the damage of whole crop maize need to increase the number of abnormal climate data used in the forage yield prediction model.

• Journal of Microbiology and Biotechnology
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• v.26 no.12
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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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• v.22 no.9
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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.