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Milk thistle, Silybum marianum (L.), is a well-known medicinal plant used for the treatment of liver diseases due to its high content of silymarin. The seeds contain elaiosome, a fleshy structure attached to the seeds, which is believed to be a rich source of many metabolites including silymarin. Segmentation of elaiosomes using only image analysis is difficult, and this makes it impossible to quantify the elaiosome phenotypes. This study proposes a new approach for semi-automated detection and segmentation of elaiosomes in milk thistle seed using the Detectron2 deep learning algorithm. One hundred manually labeled images were used to train the initial elaiosome detection model. This model was used to predict elaiosome from new datasets, and the precise predictions were manually selected and used as new labeled images for retraining the model. Such semi-automatic image labeling, i.e., using the prediction results of the previous stage for retraining the model, allowed the production of sufficient labeled data for retraining. Finally, a total of 6,000 labeled images were used to train Detectron2 for elaiosome detection and attained a promising result. The results demonstrate the effectiveness of Detectron2 in detecting milk thistle seed elaiosomes with an accuracy of 99.9%. The proposed method automatically detects and segments elaiosome from the milk thistle seed. The predicted mask images of elaiosome were used to analyze its area as one of the seed phenotypic traits along with other seed morphological traits by image-based high-throughput phenotyping in ImageJ. Enabling high-throughput phenotyping of elaiosome and other seed morphological traits will be useful for breeding milk thistle cultivars with desirable traits.

The KIX domain, conserved among various nuclear and co-activator factors, acts as a binding site that interacts with other transcriptional activators and co-activators, playing a crucial role in gene expression regulation. In plants, the KIX domain is involved in plant hormone signaling, stress response regulation, cell cycle control, and differentiation, indicating its potential relevance to crop productivity. This study aims to identify and characterize KIX domains within the soybean (Glycine max L.) genome to predict their potential role in improving crop productivity. The conservation and evolutionary history of the KIX domains were explored in 59 plant species, confirming the presence of the KIX domains in diverse plants. Specifically, 13 KIX domains were identified within the soybean genome and classified into four main groups, namely GmKIX8/9, GmMED15, GmHAC, and GmRECQL, through sequence alignment, structural analysis, and phylogenetic tree construction. Association analysis was performed between KIX domain haplotypes and soybean seed-related agronomic traits using re-sequencing data from a core collection of 422 accessions. The results revealed correlations between SNP variations observed in GmKIX8-3 and GmMED15-4 and soybean seed phenotypic traits. Additionally, transcriptome analysis confirmed significant expression of the KIX domains during the early stages of soybean seed development. This study provides the first characterization of the structural, expression, genomic haplotype, and molecular features of the KIX domain in soybean, offering a foundation for functional analysis of the KIX domain in soybean and other plants.

Maize seeds harvested in the field have higher vitality than those harvested in greenhouses but have higher contamination rates in terms of fungal or bacterial infection. It is important to disinfect maize seeds before sowing because seeds are a source of infection and damage crop production. In this study, we aimed to provide an efficient seed sterilization method to manage fungal or bacterial infections of field-harvested maize seeds. The optimized sterilization protocol was set up according to the disinfectant liquid immersion time, inverting RPM (rotations per minute), number of seeds, and black layer removal. We put 20 grains of maize seeds in 100% commercial bleach disinfectant containing 4–5% NaClO and performed 20 min of inversion at 45 RPM. After standing without inverting for the next 25 min in the sterile hood, inversion at 45 RPM for another 40 min was performed. By using this protocol, microorganisms occurred at a low rate with an average of 11.7%. Moreover, it was shown that microorganisms occurred at the lowest rate (average of 0.29% of seeds) when the black layer was removed. In addition, this sterilization method did not affect the growth and development of maize plants. These results revealed that black layer removal from maize seeds is a highly efficient, easy, and inexpensive sterilization method and can be used for seeds of various maize lines.

Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is considered the most efficient method for crop producers. To accelerate breeding cycles, genetic engineering and high-throughput phenotyping technologies have replaced conventional breeding methods. However, the current novel phenotyping method still needs to be optimized by species and varieties. Therefore, we aimed to assess the most appropriate and effective phenotypes for evaluating drought stress by applying a high-throughput image-based method on the nested association mapping (NAM) population of soybeans. The acquired image-based traits from the phenotyping platform were divided into three large categories—area, boundary, and color—and demonstrated an aspect for each characteristic. Analysis on categorized traits interpreted stress responses in morphological and physiological changes. The evaluation of drought stress regardless of varieties was possible by combining various image-based traits. We might suggest that a combination of image-based traits obtained using computer vision can be more efficient than using only one trait for the precision agriculture.

Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

Abstract Background Internode elongation is an important agronomic trait in rice that determines culm length, which is related to lodging, panicle exsertion, and biomass. sui4 (shortened uppermost internode 4) mutants show reduced internode length and a dwarf phenotype due to shortened internodes; the uppermost internode is particularly severely affected. The present study was performed to identify the molecular nature and function of the SUI4 gene during internode elongation. Results Our previous study showed that the SUI4 gene was mapped to a 1.1-Mb interval on chromosome 7 (Ji et al. 2014). In order to isolate the gene responsible for the sui4 phenotype, genomic DNA resequencing of sui4 mutants and wild-type plants and reciprocal transformation of wild-type and mutant alleles of the putative SUI4 gene was performed. The data revealed that the causative mutation of sui4 was a T to A nucleotide substitution at the microRNA172 binding site of Os07g0235800, and that SUI4 is a new allele of the previously reported gene SUPERNUMERARY BRACT (SNB), which affects flower structure. In order to understand the effect of this mutation on expression of the SUI4/SNB gene, SUI4/SNB native promoter-fuzed GUS transgenics were examined, along with qRT-PCR analysis at various developmental stages. In sui4 mutants, the SUI4/SNB gene was upregulated in the leaves, culms, and panicles, especially when internodes were elongated. In culms, SUI4/SNB was expressed in the nodes and the lower parts of elongating internodes. In order to further explore the molecular nature of SUI4/SNB during internode elongation, RNA-seq and qRT-PCR analysis were performed with RNAs from the culms of sui4 mutants and wild-type plants in the booting stage. The data showed that in sui4 mutants, genes deactivating bioactive gibberellins and cytokinin were upregulated while genes related to cell expansion and cell wall synthesis were downregulated. Conclusion In summary, this paper shows that interaction between SUI4/SNB and microRNA172 could determine internode elongation during the reproductive stage in rice plants. Due to a mutation at the microRNA172 binding site in sui4 mutants, the expression of SUI4/SNB was enhanced, which lowered the activities of cell expansion and cell wall synthesis and consequently resulted in shortened internodes.

Grain size is a key factor influencing the grain yield in rice. To identify the as-yet-unknown genes regulating grain size in Korean japonica rice, we developed a recombinant inbred line population (n = 162) from a cross between Odae (large-grain) and Joun (small-grain), and measured six traits including the thousand-grain weights of unhulled and hulled seeds, grain area, grain length, grain width and grain length-to-width ratio using high-throughput image analysis at the F8 and F9 generations. A genetic map was constructed using 248 kompetitive allele-specific PCR (KASP) markers that were polymorphic between the parental genotypes, and 29 QTLs affecting the six traits were identified, of which 15 were stable in both F8 and F9 generations. Notably, three QTL clusters affecting multiple traits were detected on chromosomes 6, 7 and 11. We analyzed whole-genome resequencing data of Odae and Joun, and selected candidate genes for the stable QTLs in the identified clusters that have high- or moderate-impact variations between Odae and Joun and encode proteins the families of which have been reported to be related to grain size regulation. These results will facilitate the identification of genes underlying the QTLs and promote molecular breeding of high-yielding Korean japonica rice varieties.

The soil-borne pathogenic Ralstonia solanacearum species complex (RSSC) is a group of plant pathogens that is economically destructive worldwide and has a broad host range, including various solanaceae plants, banana, ginger, sesame, and clove. Previously, Korean RSSC strains isolated from samples of potato bacterial wilt were grouped into four pathotypes based on virulence tests against potato, tomato, eggplant, and pepper. In this study, we sequenced the genomes of 25 Korean RSSC strains selected based on these pathotypes. The newly sequenced genomes were analyzed to determine the phylogenetic relationships between the strains with average nucleotide identity values, and structurally compared via multiple genome alignment using Mauve software. To identify candidate genes responsible for the host specificity of the pathotypes, functional genome comparisons were conducted by analyzing pan-genome orthologous group (POG) and type III secretion system effectors (T3es). POG analyses revealed that a total of 128 genes were shared only in tomato-non-pathogenic strains, 8 genes in tomato-pathogenic strains, 5 genes in eggplant-non-pathogenic strains, 7 genes in eggplant-pathogenic strains, 1 gene in pepper-non-pathogenic strains, and 34 genes in pepper-pathogenic strains. When we analyzed T3es, three host-specific effectors were predicted: RipS3 (SKWP3) and RipH3 (HLK3) were found only in tomato-pathogenic strains, and RipAC (PopC) were found only in eggplant-pathogenic strains. Overall, we identified host-specific genes and effectors that may be responsible for virulence functions in RSSC in silico. The expected characters of those genes suggest that the host range of RSSC is determined by the comprehensive actions of various virulence factors, including effectors, secretion systems, and metabolic enzymes.

Temperate japonica rice varieties exhibit wide variation in the phenotypes of several important agronomic traits, including disease resistance, pre-harvest sprouting resistance, plant architecture, and grain quality, indicating the presence of genes contributing to favorable agronomic traits. However, gene mapping and molecular breeding has been hampered as a result of the low genetic diversity among cultivars and scarcity of polymorphic DNA markers. Single nucleotide polymorphism (SNP)-based kompetitive allele-specific PCR (KASP) markers allow high-throughput genotyping for marker-assisted selection and quantitative trait loci (QTL) mapping within closely related populations. Previously, we identified 740,566 SNPs and developed 771 KASP markers for Korean temperate japonica rice varieties. However, additional markers were needed to provide sufficient genome coverage to support breeding programs. In this study, the 740,566 SNPs were categorized according to their predicted impacts on gene function. The high-impact, moderate-impact, modifier, and low-impact groups contained 703 (0.1%), 20,179 (2.7%), 699,866 (94.5%), and 19,818 (2.7%) SNPs, respectively. A subset of 357 SNPs from the high-impact group was selected for initial KASP marker development, resulting in 283 polymorphic KASP markers. After incorporation of the 283 markers with the 771 existing markers in a physical map, additional markers were developed to fill genomic regions with large gaps between markers, and 171 polymorphic KASP markers were successfully developed from 284 SNPs. Overall, a set of 1225 KASP markers was produced. The markers were evenly distributed across the rice genome, with average marker density of 3.3 KASP markers per Mbp. The 1225 KASP markers will facilitate QTL/gene mapping and marker-assisted selection in temperate japonica rice breeding programs.

Data phenotyping traits on soybean seeds such as shape and color has been obscure because it is difficult to define them clearly. Further, it takes too much time and effort to have sufficient number of samplings especially length and width. These difficulties prevented seed morphology to be incorporated into efficient breeding program. Here, we propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis. As results, quantitative values for colors and various types of morphological traits could be screened to create a standard for subsequent evaluation of the genotype. Phenotyping method in the current study could define the morphology and color of soybean seeds in highly accurate and reliable manner. Further, this method enables the measurement and analysis of large amounts of plant seed phenotype data in a short time, which was not possible before. Fast and precise phenotype data obtained here may facilitate Genome Wide Association Study for the gene function analysis as well as for development of the elite varieties having desirable seed traits.

Early seedling establishment in rice (Oryza sativa L.), which is measured by primary/secondary tiller, shoot length, biomass, root-related traits, and leaf area index, is an important trait because it helps to compete for light, air, and water for better tolerating various abiotic stresses. Consequently, it can affect the yield. However, there are not many research studies on this subject. Furthermore, previous studies have only measured the target traits once. However, this does not reflect the variation of growth rate during the seedling stage. Thus, two data points, two weeks and four weeks after planting, were used in the current study. As a result, two QTL regions were detected for the growth differences via plant height and green area (reflecting tillering). We expect that these results can be utilized by breeders to evaluate and select vigorous seedlings for their breeding programs.

As an observation instrument of the longest record of tropospheric water vapor, radiosonde data provide upper-air pressure (geopotential height), temperature, humidity and wind. However, the data have some well-known elements related to inaccuracy. In this article, radiosonde precipitable water vapor (PWV) at Sokcho observatory was compared with global positioning system (GPS) PWV during each summertime of year 2007 and 2008 and the biases were calculated. As a result, the mean bias showed negative values regardless of the rainfall occurrence. In addition, on the basis of GPS PWV, the maximum root mean square error (RMSE) was 5.67 mm over the radiosonde PWV.

Precipitable water vapor (PWV) derived from a numerical weather prediction (NWP) model were compared to observations derived from ground-based Global Positioning System (GPS) receivers. The model data compared were from the Weather Research and Forecasting (WRF) model short-range forecasts on nested grids. The numerical experiments were performed by selecting the cloud microphysics schemes and for the comparisons, the Changma period of 2008 was selected. The observational data were derived from GPS measurements at 9-sites in South Korea over a 1-month period, in the middle of June-July 2008. In general, the WRF model demonstrated considerable skill in reproducing the temporal and spatial evolution of the PWV as depicted by the GPS estimations. The correlation between forecasts and GPS estimates of PWV depreciated slowly with increasing forecast times. Comparing simulations with a resolution of 18 km and 6 km showed no obvious PWV dependence on resolution. Besides, GPS and the model PWV data were found to be in quite good agreement with data derived from radiosondes. These results indicated that the GPS-derived PWV data, with high temporal and spatial resolution, are very useful for meteorological applications.

The GNSS (Global Navigation Satellite System) signal is delayed by the neutral atmosphere at the troposphere, so that the delay is one of major error sources for GNSS precise positioning. The tropospheric delay is an integrated refractive index along the path of GNSS signal. The refractive index is empirically related to standard meteorological variables, such as pressure, temperature and water vapor partial pressure, therefore the tropospheric delay could be calculated from them. In this paper, it is presented how to generate meteorological data where observation cannot be performed. KASI (Korea Astronomy & Space Science Institute) has operated 9 GPS (Global Positioning System) permanent stations equipped with co-located MET3A, which is a meteorological sensor. Meteorological data are generated from observations of MET3A by Ordinary Kriging. To compensate a blank of observation data, simple models which consider periodic characteristics for meteorological data, are employed.

As the next generation of global satellite navigation system, the Galileo project is about to witness an initial orbit validation stage as the successful test of navigation message transmission from Giove-A in 2007. The Space Geodesy division and the Radio Astronomy division of the Korea Astronomy & Space Science Institute had collaborated on the field survey for the Galileo Sensor Station (GSS) RF environment of the proposed site near Jeju Tamla University from August 3rd to August 5th, 2006. The power spectrums were measured in full-band (800 ˜ 2000 MHz) and in-band (E5, E6 and L1 band) in frequency domain for 24 hours respectively. Finally, we performed a time domain analysis to characterize strong in-band interference source based on the result of the previous step.

The Precipitable Water Vapor (PWV) from GPS with high resolution in terms of time and space might reduce the limitations of the numerical weather prediction (NWP) model for easily variable phenomena, such as precipitation and cloud. We have converted to PWV from Global Positioning System (GPS) data of Korea Astronomy and Space Science Institute (KASI) and Ministry of Maritime Affairs & Fisheries (MOMAF). First of all, we have selected the heavy rainfall case of having a predictability limitation in time and space due to small-scale motion. In order to evaluate the effect for GPS PWV, we have executed the sensitivity experiment with PWV from GPS data over Korean peninsula in the Weather Research & Forecasting 3-Dimensional Variational (WRF-3DVAR). We have also suggested the direction of further research for an improvement of the predictability of NWP model on the basis of this case.

KASINICS (Korea Astronomy and Space Science Institute Near Infrared Camera System) is equipped with a InSb array which can observe 1--5~μm bands in near-infrared. The absorption and emission by telluric water vapor becomes serious in the bands longer than 3~μm. We measured PWV (Precipitable Water Vapor) levels above Bohyusan Optical Astronomy Observatory and Sobaeksan Optical Astronomy Observatory from July 2006 to August 2007 using the GPS PWV measurement system of KASI. We found that monthly averaged PWVs are lower than the prediction using dew-point temperature and as low as above Kitt Peak from September to February.

Recently introduced, several explicit solutions of relative motion between neighboring elliptic satellite orbits are reviewed. The performance of these solutions is compared with an analytic solution of the general linearized equation of motion. The inversion solution by the Hill-Clohessy-Wiltshire equations is used to produce the initial condition of numerical results. Despite the difference of the reference orbit, the relative motion with the relatively small eccentricity shows the similar results on elliptic case and circular case. In case of the 'chief' satellite with the relatively large eccentricity, HCW equation with the circular reference orbit has relatively larger error than other elliptic equation of motion does.

The International GNSS Service (IGS) has prepared for a transition from the relative phase center variation (PCV) to the absolute PCV, because the terrestrial scale problem of the absolute PCV was resolved by estimating the PCV of the GPS satellites. Thus, the GPS data will be processed by using the absolute PCV which will be an IGS standard model in the near future. It is necessary to compare and analyze the results between the relative PCV and the absolute PCV for the establishment of the reliable processing strategy. This research analyzes the effect caused by the absolute PCV via the GPS network data processing. First, the four IGS stations, Daejeon, Suwon, Beijing and Wuhan, are selected to make longer baselines than 1000 km, and processed by using the relative PCV and the absolute PCV to examine the effect of the antenna raydome. Beijing and Wuhan stations of which the length of baselines are longer than 1000 km show the average difference of 1.33 cm in the vertical component, and 2.97 cm when the antenna raydomes are considered. Second, the 7 permanent GPS stations among the total 9 stations, operated by Korea Astronomy and Space Science Institute, are processed by applying the relative PCV and the absolute PCV, and their results are compared and analyzed. An insignificant effect of the absolute PCV is shown in Korea regional network with the average difference of 0.12 cm in the vertical component.

In order to determine the plausibility of GPS-derived Integrated Water Vapour (IWV) as a fog detector, the relationship between GPS IWV and meteorological observations during fog was studied in a time domain. In this research, it is assumed that fog is a simple function of water vapour and cloud water based on non-precipitation warm cloud in the bulk water-continuity model and that, during fog, the atmosphere over the fog layer is horizontally homogeneous. The case of dense fog was chosen, for which the visibility observed by the human eye ('synop') was < 1 km with the regular observation at co-located GPS sites. Preliminary results showed that GPS IWV could detect the microphysical and dynamical process of fog, such as conversion of the water vapour into cloud water and inflow of water vapour into GPS observation sites. Furthermore, it could potentially be found that GPS IWV reflected unceasingly and quantitatively the state of fog after saturation. To mitigate limitations of the temporal resolution and the inaccuracy of 'synop' visibility data, a GPS receiver and a visibility meter were operated at the same site simultaneously. This experiment revealed definitively that typical radiation fog was more suitable than other types of fog for detection by GPS IWV. Based on these results, it is suggested that GPS IWV can be considered as a supplementary technique in detecting fog processes and improving fog predictability. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]