• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.419-427
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• 2021

Due to the effects of climate change and the reduction of the labor force due to COVID-19, the crop yield, harvest time, and cultivated area are rapidly changing every year. In order to respond flexibly to this situation, attempts to apply smart farm technology based on ICT (Information and Communication Technology) to individual farms are increasing. On the other hand, various stakeholders are trying to predict the yield of crops using artificial intelligence and IoT technology, but accurate prediction is difficult due to the lack of learning data. In this study, in order to overcome the data collection problem limited to a specific institution, a smart farmer service technology based on community mapping was developed in which farmers directly participate, input and share accurate data to predict production. In the process, analysis was performed on napa cabbage, which is a vegetable with a large price change compared to production.

• Molecules and Cells
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• v.26 no.2
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• pp.146-151
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• 2008

The brown planthopper (BPH) is a major insect pest in rice, and damages these plants by sucking phloem-sap and transmitting viral diseases. Many BPH resistance genes have been identified in indica varieties and wild rice accessions, but none has yet been cloned. In the present study we report fine mapping of the region containing the Bph1 locus, which enabled us to perform marker-aided selection (MAS). We used 273 F8 recombinant inbred lines (RILs) derived from a cross between Cheongcheongbyeo, an indica type variety harboring Bph1 from Mudgo, and Hwayeongbyeo, a BPH susceptible japonica variety. By random amplification of polymorphic DNA (RAPD) analysis using 656 random 10-mer primers, three RAPD markers (OPH09, OPA10 and OPA15) linked to Bph1 were identified and converted to SCAR (sequence characterized amplified region) markers. These markers were found to be contained in two BAC clones derived from chromosome 12: OPH09 on OSJNBa0011B18, and both OPA10 and OPA15 on OSJNBa0040E10. By sequence analysis of ten additional BAC clones evenly distributed between OSJNBa0011B18 and OSJNBa0040E10, we developed 15 STS markers. Of these, pBPH4 and pBPH14 flanked Bph1 at distances of 0.2 cM and 0.8 cM, respectively. The STS markers pBPH9, pBPH19, pBPH20, and pBPH21 co-segregated with Bph1. These markers were shown to be very useful for marker-assisted selection (MAS) in breeding populations of 32 F6 RILs from a cross between Andabyeo and IR71190, and 32 F5 RILs from a cross between Andabyeo and Suwon452.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.26-26
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• 2017

Seed germination stimulated by light is said to be photoblastism. Photoblastism has not been reported in cereal crops, especially in the rice, but Korean weedy rice was reported to have photoblastism and longer mesocotyl than cultivar. Photoblastic weedy rice (PBR) was used to identify QTLs for photoblastism and mesocotyl length. In previous works, QTLs for photoblastism, pbr1 and pbr12 were identified on chromosomes 1 and 12 using 124 F4 lines from a cross between Ilpum and PBR using bulked segregant analysis. Two QTLs for mesocotyl elongation, qMel-1 and qMel-3 were mapped on chromosomes 1 and 3 120 F8 lines from the same cross. Of interest, the RM8260-RM246 region of pbr1 overlapped with a region of qMel-1. To know whether these two QTLs are functionally related, 110 F3 lines were developed from a cross between Ilpum and CR7124. CR7124 having photoblastism and long mesocotyl was selected from 120 F8 lines. 95 F3 lines were measured for germination rate in a light and dark condition and mesocotyl length. Mesocotyl length and germination rate in the dark condition in F3 lines showed significant correlation (r = 0.7, P < 0.0001). 95 $F_3$ lines were genotyped with RM7419 on chromosome 1. ANOVA showed that RM7419 was tightly linked to QTLs for photoblastism as well as mesocotyl length on chromosome 1 (P < 0.0001) indicating the tight linkage of two QTLs. Fine mapping of the two QTL is underway to analyze their functional relationship.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.259-259
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• 2022

Soil salinity is a major factor that reduces crop yields. The amount of soil affected by salinity is about 83 million hectares (FAO 2000), which is increasing due to the effects of climate change. In soybean [Glycine max (L.) Merr.], nutritional properties such as protein, starch, and sucrose content together with biomass and yield tends to reduce due to excessive salt. As a result of QTL mapping using the 169 F_2:3 population from the KA-1285 (salt-tolerant) × Daepung (salt-sensitive) in a previous study, two major QTLs (Gm03_39796778 and Gm03_40600088) related to salt tolerance were found on chromosome 3. In this study, the CDS region of the Gmsalt3 gene was analyzed using the ABI 3730x1 DNA Analyzer (Macrogen, Korea). The sequence of Gmsalt3 gene in KA-1285 was compared with Williams 82.a4.vl and PI483463 (Glycine soja). Two transversions were found at exon6 in KA-1285 and PI483463. Currently, whole genome sequencing and variation analysis using the Illumine Novaseq 6000 machine (Illumina, USA) are in progress. The results of this study can provide useful molecular markers for the selection of salt-tolerant soybeans and can be used as basic data for future salt-tolerant gene research.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.33-33
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• 2022

Rice (Oryza sativa L.) is a widely studied domesticated model plant. Seed awning is an unfavorable trait during rice harvesting and processing. Hence, awn was one of the target characters selected during domestication. However, the genetic mechanisms underlying awn development in rice are not well understood. In this study, we analyzed the genes for awn development using a mapping population derived from a cross between the Korean indica cultivar 'Milyang23' and NIL4/9 (derived from a cross between 'Hwaseong' and O. minuta). Two quantitative trait loci (QTLs), qAwn4 and qAwn9 were mapped on chromosome 4 and 9, respectively, increased awn length in an additive manner. Through comparative sequencing analyses parental lines, LABA1 was determined as the causal gene underlying qAwn4. qAwn9 was mapped to a 199-kb physical region between markers RM24663 and RM24679. Within this interval, 27 annotated genes were identified, and five genes, including a basic leucine zipper transcription factor 76 (OsbZIP76), were considered candidate genes for qAwn9 based on their functional annotations and sequence variations. Haplotype analysis using the candidate genes revealed tropical japonica specific sequence variants in the qAwn9 region, which partly explains the non-detection of qAwn9 in previous studies that used progenies from interspecific crosses. This provides further evidence that OsbZIP76 is possibly a causal gene for qAwn9. The O. minuta qAwn9 allele was identified as a major QTL associated with awn development in rice, providing an important molecular target for basic genetic research and domestication studies. Our results lay the foundation for further cloning of the awn gene underlying qAwn9.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.667-677
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• 2020

Crop water stress can be detected based on soil moisture content, crop physiological characteristics and remote-sensing technology. The detection of crop water stress is an important issue for the accurate assessment of yield decline. The crop water stress index (CWSI) has been introduced based on the difference between leaf and air temperature. In this paper, drone-based thermal infrared image was used to map of crop water stress in water control plot (WCP) and water deficit plot (WDP) over spring chinese cabbage fields. The spatial distribution map of CWSI was in strong agreement with the abnormal growth response factors (plant height, plant diameter, and measured value by chlorophyll meter). From these results, CWSI can be used as a good method for evaluation of crop abnormal growth monitoring.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.207-207
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• 2022

Eating and cooking quality (ECQ) is one of the most complex quantitative traits in rice. The understanding of genetic regulation of transcript expression levels attributing to phenotypic variation in ECQ traits is limited. We integrated whole-genome resequencing, transcriptome, and phenotypic variation data from 84 Japonica accessions to build a transcriptome-wide association study (TWAS) based regulatory network. All ECQ traits showed a large phenotypic variation and significant phenotypic correlations among the traits. TWAS analysis identified a total of 285 transcripts significantly associated with six ECQ traits. Genome-wide mapping of ECQ-associated transcripts revealed 66,905 quantitative expression traits (eQTLs), including 21,747 local eQTLs, and 45,158 trans-eQTLs, regulating the expression of 43 genes. The starch synthesis-related genes (SSRGs), starch synthase IV-1 (SSIV-1), starch branching enzyme 1 (SBE1), granule-bound starch synthase 2 (GBSS2), and ADP-glucose pyrophosphorylase small subunit 2a (OsAGPS2a) were found to have eQTLs regulating the expression of ECQ associated transcripts. Further, in co-expression analysis, 130 genes produced at least one network with 22 master regulators. In addition, we developed CRISPR/Cas9-edited glbl mutant lines that confirmed the role of alpha-globulin (glbl) in starch synthesis to validate the co-expression analysis. This study provided novel insights into the genetic regulation of ECQ traits, and transcripts associated with these traits were discovered that could be used in further rice breeding.

• Plant Breeding and Biotechnology
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• v.6 no.4
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• pp.391-403
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• 2018

Genome resequencing by next-generation sequencing technology can reveal numerous single nucleotide polymorphisms (SNPs) within a closely-related cultivar group, which would enable the development of sufficient SNP markers for mapping and the identification of useful genes present in the cultivar group. We analyzed genome sequence data from 13 Korean japonica rice varieties and discovered 740,566 SNPs. The SNPs were distributed at 100-kbp intervals throughout the rice genome, although the SNP density was uneven among the chromosomes. Of the 740,566 SNPs, 1,014 SNP sites were selected on the basis of polymorphism information content (PIC) value higher than 0.4 per 200-kbp interval, and 506 of these SNPs were converted to Kompetitive Allele-Specific PCR (KASP) markers. The 506 KASP markers were tested for genotyping with the 13 sequenced Korean japonica rice varieties, and polymorphisms were detected in 400 KASP markers (79.1%) which would be suitable for genetic analysis and molecular breeding. Additionally, a genetic map comprising 205 KASP markers was successfully constructed with 188 $F_2$ progenies derived from a cross between the varieties, Junam and Nampyeong. In a phylogenetic analysis with 81 KASP markers, 13 Korean japonica varieties showed close genetic relationships and were divided into three groups. More KASP markers are being developed and these markers will be utilized in gene mapping, quantitative trait locus (QTL) analysis, marker-assisted selection and other strategies relevant to crop improvement.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.43 no.4
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• pp.199-204
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• 1998

This study was conducted to investigate the chromosomal locations and effects of quantitative trait loci (QTL) associated with chemical properties of rice (Oryza sativa L.). One hundred sixty four recombinant inbred lines (MGRILs) of $F_{11}$ were derived from the cross between Milyang 23, Tongil type, and Gihobyeo, japonica type. They were evaluated for 7 traits of chemical property in rice. Transgressive segregation was observed for all traits examined. Eight significant QTLs were detected (LOD$\geq$2.0) for five traits, including two QTLs for amylose content, two QTLs for potassium content, one QTL for ratio of magnesium to potassium, one QTL for fat content and two QTLs for ash content. Phenotypic variation explained by each QTL ranged from 7.2% to 14.4%. However, no significant QTL was detected for magnesium and protein contents. In amylose content and ash content M alleles originated from Milyang 23 were responsible for increasing these traits and J alleles originated from Gihobyeo also responsible for increasing these traits. Pleiotropic effects of single QTLs on different traits are observed.