• KOREAN JOURNAL OF CROP SCIENCE
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• v.45 no.5
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• pp.316-321
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• 2000

Blast severity data of 39 rice varieties at 11 sites in Korea from 1997 to 1999 were analyzed using AMMI model and pattern analysis. Genotype x Environment (G$\times$E) interaction sum of squares (SS) accounted for 12 % of the total SS. Eight genotype groups and seven location groups were identified based on blast reaction pattern. The data obtained from over 21 sites with 44 test varieties from 1981 to 1996 were also considered. These were compared with the 1997-1999 data using the G$\times$E analysis results. Majority of the variability in the Korean Rice Blast Nursery (KRBN) were attributable to variations due to genotypes. Variations of G$\times$E interaction were maintained though test sites were reduced from 21 to 11 sites. Broadly compatible biological discriminative varieties identified were Nagdongbyeo and Akibare while broadly incompatible biological discriminative varieties identified were Hangangchalbyeo and Seogwangbyeo. Key sites for future evaluation work could be selected from location groups. Each location group should be represented by the site with the strongest interaction pattern. Blast responses in Cheolwon, Gyehwa, Suwon, Iksan, and Icheon showed different patterns from other locations.

• 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.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.102-102
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• 2017

In this study, QTL analysis of allelopathy was conducted. A total of 171 of F8 RILs developed from the cross between Nongan(low allelopathic cultivar) and Sathi(high allelopathic cultivar) were used . the performance of allelopathy were evaluated using 'ECAM(Equal Compartment Agar Method)', where the root length of lettuce cultivated with the RILs were measured. The distribution of the performance was followed as normal distribution. In order to identify the location of QTLs related to allelopathy, QTL-seq with BSA(Bulked-segregant analysis) was performed with 20 highest and 10 lowest RILs. As a result, Two Sliding window coordinate region of candidate QTLs were detected on Chr4 (5,050,001 - 14,800,000, 18,650,001 - 22,500,000), Chr8 (2,550,001 - 8,250,000, 21,150,001 - 26,800,000) and One region on Chr7 (1 - 3,300,000), Chr9 (1 - 13,300,000) respectively.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.10a
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• pp.319-319
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• 2017

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.10a
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• pp.221-221
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• 2017

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.sup1
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• pp.30.2-30.2
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• 2009

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.04a
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• pp.165-165
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• 2018

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.121-121
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• 2022

When 7 kg·10a^-1, which is less than the nitrogen standard application amount of 9 kg·10a^-1, is applied, the protein content is lowered and the palatibility is improved. In order to examine the applicability of nitrogen fertilization of 7 kg·10a^-1 nationwide, soil samples were collected from 240 paddy fields in 8 provinces in 2021, and the organic matter content, effective phosphoric acid, and effective silicic acid were analyzed for each sample. As a result of one-way ANOVA analysis between samples collected for each province, there was no significant difference in the content of organic matter, effective phosphoric acid, and effective silicic acid except for some provinces. The contents of organic matter was higher than the appropriate level(25 ~ 30 g·kg^-1) except for Gyeongsangbuk-do, the effective phosphoric acid was higher than the appropriate level(80~120 mg·kg^-1) in all provinces, and the effective silicic acid was lower than the appropriate level(157 ~ 180 mg·kg^-1) except for Gyeonggi-do, Jeollanam-do and Gyeongsangnam-do. As a result of analyzing the recommended fertilization amount based on the nitrogen application amount of 7 kg·10a^-1, 68.3% ofthe 240 samples were able to give nitrogen fertilizer less than 7.5 kg·10a^-1, and the rest had to be given more than that to satisfy the standard fertilization amount. As a result of this study, 68.3% of rice paddies nationwide can be cultivated with a standard fertilization amount of 7 kg·10a^-1, however it was thought that continuous nutrient management would be required for other paddies.

• 한국약용작물학회:학술대회논문집
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• 2010.10a
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• pp.311-311
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• 2010

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.