The conventional soybean breeding program by single seed descent method required around 8 to 9 years to develop a cultivar. Through the advancement of breeding techniques, such as marker-assisted selection, required resources could be significantly saved, but the generation advancement step still slows down the entire soybean breeding program time course. In this study, 28 soybean varieties were tested to find the optimal speed breeding conditions for soybeans that could rapidly advance one generation with 2 light sources, 3 light conditions, and 2 planting densities. Plants were kept under short-day conditions (9 hours light/15 hours dark). We optimized the growth conditions for shortening the period of soybean generation progression based on speed breeding. The optimizing conditions are as follows. (1) Irradiation using LED light source for 9 hours, (2) 506 mmol/(m2∙s) of PPFD at 30 cm from the ground, (3) Planting density of 5 cm × 5 cm, (4) temperature of 25℃ ± 2℃ and (5) humidity of 50% ± 10%. If this condition is used, soybeans can be advanced by one generation within an average of 73 days. It is possible to advance five generations a year using only indoor speed-breeding system. Furthermore, if it includes the development of lines in the field, four generation per year, which is advance three generations using indoor speed-breeding system and one generation in the field, is allowed to increase soybean breeding speed with minimum input.
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From an agricultural point of view, deep learning models can be used in a variety of way to study the agricultural properties of soybean. Object detection can be performed using image or video data on phenotypic traits of soybean. In this project, a study on the phenotype analysis about soybean seed was conducted by artificial intelligence (AI) based on the YOLOv5 model. In model summary, layers and parameters were calculated as 243 and 7020913, respectively. Means of average precision (mAP)@[0.5: 0.95] was recorded as 0.835, 0.739, 0.785 for each class, and Daewonkong (DW) with yellow seed coat color was calculated as the highest value, and landrace with black seed coat color (NG2) revealed the lowest value. As a result of prediction performance in the confusion matrix, each class of DW, NG2, and inbreeding line with green seed coat color (NGT) showed significant correlation of true positive (TP) in the matrix with the same output value for the input value.
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Salinity stress is one of the important abiotic stresses in crops. In this study, ten different concentrations of NaCl solutions were tested to determine the optimal level of NaCl concentration for salinity tolerance test at the germination stage in peanut, and 0.6% NaC1 was suitable for the test. A total of 249 peanut accessions were tested with 0.6% NaC1 and radical root lengths of the accessions were measured. The results showed that there were significant genetic variations on the tolerance to salinity stress among the tested accessions. Through a Genome-Wide Association Study (GWAS) using the Axiom_Arachis array with 58K SNPs, three putative SNPs with significant relation to radicle root length were identified on chromosomes Aradu.A03, Araip.B01, and Araip.B05.
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The
objective
of this study was to develop high-throughput SNP or SNP-based markers by re-sequencing of two peanut cultivars, ‘K-Ol’ and ‘Pungan’. The whole genome re-sequencing for the two cultivars was performed to produce sequences of 35.3 × 109 bp with 350 × 106 reads and 32.0 × 109 bp with 318 × 106 reads, respectively. As compared with the peanut reference genome, the distribution of homozygous and heterozygous SNPs on each chromosome showed very similar patterns between ‘K-Ol’ and ‘Pungan’, and most of them were in intergenic-region regardless of the peanut cultivars and reference genome type. The SNPs identified between the two peanut cultivars were evenly distributed across chromosomes of peanut diploid A and B reference genomes. It indicated that these SNPs could be available to construct a genetic map using the segregating population derived from a cross between ‘K-Ol’ and ‘Pungan’. Total 61 CAPS marker were developed and tested for their availability. Of the CAPS markers, 60 CAPS markers produced normal PCR products and 18 out of them presented polymorphism among 6 peanut varieties. Results of the present study could provide useful genetic resources to facilitate marker-assisted selection for breeding programs as well as germplasm screening for peanut.
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