Morphological variation was investigated among 31 maize landrace accessions collected from the fields of various farmers in South Sudan by examining 7 quantitative agronomic characteristics. A significant positive correlation was found between days to tasseling (DT) and days to silking (DS), between plant height (PH) and ear height (EH) and stem width (SW), and between EH and SW and leaf length (LL). First and second principal components accounted for 69% of the total variance (40.9% and 28.6%, respectively). PH, EH, SW, LL, and leaf width (LW) were clearly characterized in a positive direction on the first axis. DT and DS were clearly characterized in a positive direction on the second axis. A scatter plot based on phenotypic data revealed the existence of 3 groups based on the most discriminating characteristics: Group I included 5 maize landrace accessions, Group II comprised 14 maize landrace accessions, and Group III included 10 maize landrace accessions. In principal component analysis, generally the investigated genotypes were not clearly grouped into their geographical origins owing to a weak geographic relationship among the accessions. In conclusion, even though the morphological characterization studies were conducted in the South Korean climate, this study revealed significant phenotypic variation among the explored maize landrace accessions collected from South Sudan. Therefore, this information about phenotypic divergence may be very useful for future breeding research programs as well as for genetic improvement of South Sudan maize accessions.
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Drought tolerance is derived from complex quantitative traits that are associated with different shoot and root morphological characters. This study assessed the genetic and phenotypic variation of 12 maize inbred lines and performed association analysis of 11 drought-related traits using 360 simple sequence repeats (SSRs), detecting 1,604 alleles, with an average of 4.4 alleles per locus. The average values of gene diversity (GD) and polymorphism information content (PIC) were 0.648 and 0.598, respectively. In principal component analysis (PCA), shoot fresh weight (SFW), shoot dry weight (SDW), stem weight (SW), leaf weight (LW), root fresh weight (RFW), root dry weight (RDW), and leaf area (LA) traits contributed greatly to the PCA. Association analysis was performed using a general linear model with a Q-matrix (Q GLM) and a mixed linear model with Q and K-matrices (Q + K MLM). Twelve SSR markers for drought tolerance trait were detected by Q GLM, and all maize inbred lines were clearly divided into two groups in accordance with their drought tolerance. Duplicated significant marker-trait associations (SMTAs) between Q GLM and Q + K MLM identified eight marker-trait associations involving four SSR markers that were associated with the traits of SW, SFW, RFW, and RDW with a significant level of
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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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