• Korean Journal of Environmental Agriculture
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• v.38 no.1
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• pp.34-37
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• 2019

BACKGROUND: A region can be divided into agroclimatic zones based on homogeneity in weather variables that have greatest influence on crop growth and yield. The agro-climatic zone has been used to identify yield variability and limiting factors for crop growth. This study was conducted to classify agro-climatic zones in the state of Mato Grosso in Brazil for predicting crop productivity and assessing crop suitability etc. METHODS AND RESULTS: For agro-climatic zonation, monthly mean temperature, precipitation, and solar radiation data from Global Modeling and Assimilation Office (GMAO) of National Aeronautics and Space Administration (NASA, USA) between 1980 and 2010 were collected. Altitude and vegetation fraction of Brazil from Weather Research and Forecasting (WRF) were also used to classify them. The criteria of agro-climatic classification were temperature in the hottest month ($30^{\circ}C$), annual precipitation (600 mm and 1000 mm), and altitude (200 m and 500 m). The state of Mato Gross in Brazil was divided into 9 agro-climatic zones according to these criteria by using matrix classification method. CONCLUSION: The results could be useful as information for estimating agro-meteorological characteristics and predicting crop development and crop yield in the state of Mato Grosso in Brazil.

• Plant Breeding and Biotechnology
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• v.6 no.4
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• pp.321-336
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• 2018

Sesame (Sesamum indicum L.) is an important oilseed crop grown in tropical and subtropical areas. The objective of this study was to investigate the genetic relationships among 129 sesame landraces and cultivars using simple sequence repeat (SSR) markers. Out of 70 SSRs, 23 were found to be informative and produced 157 alleles. The number of alleles per locus ranged from 3 - 14, whereas polymorphic information content ranged from 0.33 - 0.86. A distance-based phylogenetic analysis revealed two major and six minor clusters. The population structure analysis using a Bayesian model-based program in STRUCTURE 2.3.4 divided 129 sesame accessions into three major populations (K = 3). Based on pairwise comparison estimates, Pop1 was observed to be genetically close to Pop2 with $F_{ST}$ value of 0.15, while Pop2 and Pop3 were genetically closest with $F_{ST}$ value of 0.08. Analysis of molecular variance revealed a high percentage of variability among individuals within populations (85.84%) than among the populations (14.16%). Similarly, a high variance was observed among the individuals within the country of origins (90.45%) than between the countries of origins. The grouping of genotypes in clusters was not related to their geographic origin indicating considerable gene flow among sesame genotypes across the selected geographic regions. The SSR markers used in the present study were able to distinguish closely linked sesame genotypes, thereby showing their usefulness in assessing the potentially important source of genetic variation. These markers can be used for future sesame varietal classification, conservation, and other breeding purposes.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.445-455
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• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1061-1070
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• 2021

In this manuscript, we tried to improve the performance of the FC-DenseNet by applying an attention gate for the classification of cropping areas. The attention gate module could facilitate the learning of a deep learning model and improve the performance of the model by injecting of spatial/spectral weights to each feature map. Crop classification was performed in the onion and garlic regions using a proposed deep learning model in which an attention gate was added to the skip connection part of FC-DenseNet. Training data was produced using various PlanetScope satellite imagery, and preprocessing was applied to minimize the problem of imbalanced training dataset. As a result of the crop classification, it was verified that the proposed deep learning model can more effectively classify the onion and garlic regions than existing FC-DenseNet algorithm.

• Korean Journal of Breeding Science
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• v.43 no.2
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• pp.133-138
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• 2011

The groundnut or cultivated peanut (Arachis hypogaea L.) in Korea consists of 36 domestic varieties which have been developed and registered as cultivars for the public during last 25 years. To screen and identify of Korean peanut varieties and genetic resources, we present a simple and reliable method. A methodology based on simple sequence repeat (SSR) markers developed and widely used for prominent gene identification and variety discrimination. For identification of those 36 Korean peanut varieties, 238 unique peanut SSR markers were selected from some previously reported results, synthesized and used for polymerase chain reaction (PCR). Data were taken through acryl amide gel electrophoresis and changed into proper formats for application of data mining analysis using Biomine (all-in-one functional genomics data mining program). Consequently, twelve SSR primers were investigated and revealed the differences between those 36 varieties. These primer pairs amplified 27 alleles with an average of 2.3 allele per primer pair. In addition, those results showed genetic relationship by classification method within 36 varieties. The approach described here could be applied to monitoring of our varieties and adapting to peanut breeding program.

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.04a
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• pp.110-111
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• 2000

• Proceedings of the Korean Society of Crop Science Conference
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• 2001.09a
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• pp.158-159
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• 2001

• 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.

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.11a
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• pp.186-187
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• 2000

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.11a
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• pp.188-190
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• 2000