• 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.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.141-150
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• 2018

Crop type classification is essential for supporting agricultural decisions and resource monitoring. Remote sensing techniques, especially using hyperspectral imagery, have been effective in agricultural applications. Hyperspectral imagery acquires contiguous and narrow spectral bands in a wide range. However, large dimensionality results in unreliable estimates of classifiers and high computational burdens. Therefore, reducing the dimensionality of hyperspectral imagery is necessary. In this study, the Random Forest (RF) classifier was utilized for dimensionality reduction as well as classification purpose. RF is an ensemble-learning algorithm created based on the Classification and Regression Tree (CART), which has gained attention due to its high classification accuracy and fast processing speed. The RF performance for crop classification with airborne hyperspectral imagery was assessed. The study area was the cultivated area in Chogye-myeon, Habcheon-gun, Gyeongsangnam-do, South Korea, where the main crops are garlic, onion, and wheat. Parameter optimization was conducted to maximize the classification accuracy. Then, the dimensionality reduction was conducted based on RF variable importance. The result shows that using the selected bands presents an excellent classification accuracy without using whole datasets. Moreover, a majority of selected bands are concentrated on visible (VIS) region, especially region related to chlorophyll content. Therefore, it can be inferred that the phenological status after the mature stage influences red-edge spectral reflectance.

• Journal of Crop Science and Biotechnology
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• v.10 no.1
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• pp.3-7
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• 2007

Classification of blast resistance type of 129 Korean rice cultivars was carried out based on reaction pattern to 10 Japanese blast pathogen isolates(Pyricularia oryzae). The cultivars were divided into 11 groups based on the presumed resistance genes as follows; Pia type(19 cultivars), Pita-2 type(4), Pik type(3), Pib type(5), Piz type(11), Pik-s type(8), Pik and Pii type(4), Pia and Pita type(8), Pia and Pik type(6), Pita, Pik and Pii type(4) and no-grouping type(57). These results would provide important information to rice breeding for durable and broad resistance to rice blast.

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.04a
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• pp.138-139
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• 2000

• Advances in nano research
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• v.14 no.3
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

• The Korean Journal of Ecology
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• v.6 no.2
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• pp.114-127
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• 1983

The production and productivity of $C_3 and C_4$ type plants in Korea was studcied In the areas, in which the summer temperature is above $30^{\circ}C,;C_3$ type plants showed“M” type productivity curves exhibiting two peaks in spring and autumn, and C4 type plants showd “Bell” type productivity curves which show one peak in summer(Figs. 1,2,3,4,5). From the result of researching the standing crop of $C_3 and C_4$type plants dcuring August and September in which the standing crop reaches the highest peak, the dominant plants in the natural grass vegetation were almost all of $C_4$ type plants, showing the high standing crop, while the standing crop of $C_3$ type plants shows its high peak in the humid areas, riversides and dams.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.43 no.4
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• pp.205-208
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• 1998

Silica bodies (phytoliths) are becoming of wide use for pedology, archaeology, paleobotany and paleoecology in botany. This study investigated morphological differences of silica bodies in the lamina of wild, indica type, and japonica type rice. Phytoliths in the epidermis of lamina showed noticeable difference among tested plants. Besides, there were also significant differences in the shape and distribution of the silica bodies around stomata and trichomes. Silica bodies in the lamina of the rice plants could be used to classify subspecies of Oryza genus.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.28 no.2
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• pp.233-239
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• 1983

In order to obtain basic date for the cultivation and the breeding of double zero varieties of rapeseed, spring nature grade were investigated. Spring nature grade could be classified to 8 classes by the method of material maturity. The double zero varieties, Ⅶ group of spring adaptability has been developed from the cross "Oro" and "Midas" classified as summer type. A significant negative correlation was found between the spring adaptability and seed yield. On the other hand, a highly significant negative correlation was found between maturation and spring adaptability, cold resistnce and spring adaptability, plant from and spring adaptability. Ⅵ and Ⅶ group close to summer type showed spring growing adaptability, and 0, I and II group were appeared as winter type, autumn growing adaptability with cold tolerance.with cold tolerance.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.617-620
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• 2017

The periodic monitoring of crop conditions and timely estimation of crop yield are of great importance for supporting agricultural decision-makings, as well as for effectively coping with food security issues. Remote sensing has been regarded as one of effective tools for crop condition monitoring and crop type classification. Since 2010, RDA (Rural Development Administration) has been developing technology for monitoring on crop condition using remote sensing and model. These special papers address recent state-of-the-art of remote sensing and geospatial technologies for providing operational agricultural information, such as, crop yield estimation methods using remote sensing data and process-oriented model, crop classification algorithm, monitoring and prediction of weather and climate based on remote sensing data,system design and architecture of crop monitoring system, history on rice yield forecasting method.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.41 no.3
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• pp.332-339
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• 1996

Electrophoretic method was utilized to classify 100 varieties of sweet potato germplasm maintained at the National Crop Experiment Station of Korea in 1993. The esterase isozyme patterns in the leaves were classified into 14 different types. Type Ⅸ included the most of the varieties (46) tested and Ⅶ, I, III, Ⅷ, II and V types of all included 47 varieties in order. The other 7 varieties had different band pattern with each other. Type I having many kind of band pattern included Shinyulmi, Beniastma and High starch which had the dry type of tuberous roots varieties. The esterase isozymes pattern in the tuberous roots were classified with 18 kinds of types. The C type included 22 varieties and B, K, A, E, I and N in order. The proteins pattern in the tuberous roots were classified with 7 kinds of types. I type included 36 varieties, and IV type included 27 varieties and II, III, Ⅶ and Ⅵ types in order.