• Proceedings of the Korean Society of Crop Science Conference
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• 1997.10a
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• pp.110-111
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• 1997

• 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 Medicinal Crop Science
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• v.21 no.4
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• pp.268-275
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• 2013

This text was analyzed and investigated the distribution of medicinal plants in Cheongoksan Bonghwagun Korea, in order to search the medicinal resources that are used in modern medicine. Medicinal plants of the Korean Pharmacopoeia (10th edition) distributed in Cheongoksan Bonghwagun were consisted of 93 taxa ; 82 species, 10 varieties, 1 forma of 79 genus, 50 families. In medicinal plants of the Korean Pharmacopoeia, rate of native species and exotic species was 89.2% (83 taxa) and 10.8% (10 taxa) respectively. Family classification was the most of compositae of 8 taxa, and life form classification was most of herb of hemicryptophyte species. The classification by using parts were 34 taxa of root use and the classification of efficacy utilization was 24 taxa of Cheongyeolyak (heat-clearing drug) use.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.689-698
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• 2017

In this paper, a new classification method based on the combination of semi-supervised learning with spatial similarity of adjacent pixels is presented for crop classification in inaccessible areas. Iterative classification based on semi-supervised learning is applied to extract reliable training data from both the initial classification result with a small number of training data, and classification results of adjacent pixels are also considered to extract new training pixels with less uncertainty. To evaluate the applicability of the proposed method, a case study of the classification of field crops was carried out using multi-temporal Landsat-8 OLI acquired in the Daehongdan region, North Korea. From a case study, the misclassification of crops and forests, and isolated pixels in the initial classification result were greatly reduced by applying the proposed semi-supervised learning method. In addition, the combination of classification results of adjacent pixels for the extraction of new training data led to the great reduction of both misclassification results and isolated pixels, compared to the initial classification and traditional semi-supervised learning results. Therefore, it is expected that the proposed method would be effectively applied to classify areas in which it is difficult to collect sufficient training data.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1269-1276
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• 2023

Crop estimation is essential for the multinational meal and powerful demand due to its numerous aspects like soil, rain, climate, atmosphere, and their relations. The consequence of climate shift impacts the farming yield products. We operate the dataset with temperature, rainfall, humidity, etc. The current research focuses on feature selection with multifarious classifiers to assist farmers and agriculturalists. The crop yield estimation utilizing the feature selection approach is 96% accuracy. Feature selection affects a machine learning model's performance. Additionally, the performance of the current graph classifier accepts 81.5%. Eventually, the random forest regressor without feature selections owns 78% accuracy and the decision tree regressor without feature selections retains 67% accuracy. Our research merit is to reveal the experimental results of with and without feature selection significance for the proposed ten algorithms. These findings support learners and students in choosing the appropriate models for crop classification studies.

• 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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.3
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• pp.314-319
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• 2008

Near infrared (NIR) spectroscopy was used to classify normal and artificially aged nonviable corn (Zea mays L., cv. 'Suwon19') seeds. The spectra at 1100-2500nm were scanned with normal and artificially aged single seeds and analyzed by principle component analysis (PCA). To discriminate normal seeds from artificially aged seeds, a calibration modeling set was developed with a discriminant partial least square 2 (PLS 2) method. The calibration model derived from PLS 2 resulted in 100% classification accuracy of normal and artificially aged (aged) seeds from the raw, the 1st and 2nd derivative spectra. The prediction accuracy of the unknown normal seeds was 88, 100 and 97% from the raw, the $1^{st}$ and $2^{nd}$ derivative spectra, and that of the unknown aged seeds was 100% from all the raw, the $1^{st}$ and $2^{nd}$ derivative spectra, respectively. The results showed a possibility to separate corn seeds into viable and non-viable using NIR spectroscopy.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.665-673
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• 2010

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.04a
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• pp.100-101
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• 2000

• Proceedings of the Korean Society of Crop Science Conference
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• 1997.05a
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• pp.22-23
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• 1997