Soybeans (Glycine max (L.) Merr.), a popular food resource worldwide, have various uses throughout the industry, from everyday foods and health functional foods to cosmetics. Soybeans are vulnerable to pests such as stink bugs, beetles, mites, and moths, which reduce yields. Riptortus pedestris (R. pedestris) has been reported to cause damage to pods and leaves throughout the soybean growing season. In this study, an experiment was conducted to detect R. pedestris according to three different environmental conditions (pod filling stage, maturity stage, artificial cage) by developing a surveillance platform based on an unmanned ground vehicle (UGV) GoPro CAM. Deep learning technology (MRCNN, YOLOv3, Detectron2)-based models used in this experiment can be quickly challenged (i.e., built with lightweight parameter) immediately through a web application. The image dataset was distributed by random selection for training, validation, and testing and then preprocessed by labeling the image for annotation. The deep learning model localized and classified the R. pedestris individuals through a bounding box and masking in the image data. The model achieved high performances, at 0.952, 0.716, and 0.873, respectively, represented through the calculated means of average precision (mAP) value. The manufactured model will enable the identification of R. pedestris in the field and can be an effective tool for insect forecasting in the early stage of pest outbreaks in crop production.

Starch is a major ingredient in rice, and the amylose content of starch significantly impacts rice quality. OsSS (starch synthase) is a gene family related to the synthesis of amylose and amylopectin, and 10 members have been reported. In the present study, a synteny analysis of a novel family member belonging to the OsSSIV subfamily that contained a starch synthase catalytic domain showed that three segmental duplications and multiple duplications were identified in rice and other species. Expression data showed that the OsSS gene family is involved in diverse expression patterns. The prediction of miRNA targets suggested that OsSS are possibly widely regulated by miRNA functions, with miR156s targeted to OsSSII-3, especially. Haplotype analysis exhibited the relationship between amylose content and diverse genotypes. These results give new insight and a theoretical basis for the improved amylose content and eating quality of rice.

At low temperatures, rice grains have a reduced germination rate and grow more slowly, which delays the emergence of rice seedlings from the paddy water surface and significantly increases seedling mortality. In this study, we conducted a shotgun proteomics analysis of geminated embryos and coleoptiles to compare the proteome expression pattern between the low-temperature resistant variety, Tong 88-7, and the low-temperature susceptible variety, Milyang 23. In a shotgun proteomics analysis of low-temperature resistant and susceptible embryos and coleoptiles in both cold and control temperatures, we discovered a total of 2626 non-redundant proteins, with a 0.01 false discovery rate. A comparison of protein expression patterns between resistant and susceptible embryos and coleoptiles under low-temperature and normal conditions revealed that 85 proteins and 196 proteins were expressed by the resistant and susceptible strains, respectively, in response to low temperature. Among them, 12 proteins overlapped. Proteins involved in stress responses, metabolism, and gene expression were expressed in both strains. Similar molecular functions of the response were detected, suggesting that the resistant and susceptible strain have a similar proteome response to cold temperatures. The resistance of Tong 88-7 to cold-water germination may result from the efficiency of these proteins, rather than activation of additional or different molecular processes. A comparison of protein expression between the resistant and susceptible strains’ responses revealed that the more successful low-temperature germination of Tong 88-7 was associated with gibberellin signaling, protein trafficking, and the ABA-mediated stress response.

Root network structure plays a crucial role in growth and development processes in rice. Longer, more branched root structures help plants to assimilate water and nutrition from soil, support robust plant growth, and improve resilience to stresses such as disease. Understanding the molecular basis of root development through screening of root-related traits in rice germplasms is critical to future rice breeding programs. This study used a small germplasm collection of 137 rice varieties chosen from the Korean rice core set (KRICE_CORE) to identify loci linked to root development. Two million high-quality single nucleotide polymorphisms (SNPs) were used as the genotype, with maximum root length (MRL) and total root weight (TRW) in seedlings used as the phenotype. Genome-wide association study (GWAS) combined with Principal Components Analysis (PCA) and Kinship matrix analysis identified four quantitative trait loci (QTLs) on chromosomes 3, 6, and 8. Two QTLs were linked to MRL and two were related to TRW. Analysis of Linkage Disequilibrium (LD) decay identified a 230 kb exploratory range for detection of candidate root-related genes. Candidates were filtered using RNA-seq data, gene annotations, and quantitative real-time PCR (qRT-PCR), and five previously characterized genes related to root development were identified, as well as four novel candidate genes. Promoter analysis of candidate genes showed that LOC_Os03g08880 and LOC_Os06g13060 contained SNPs with the potential to impact gene expression in root-related promoter motifs. Haplotype analysis of candidate genes revealed diverse haplotypes that were significantly associated with phenotypic variation. Taken together, these results indicate that LOC_Os03g08880 and LOC_Os06g13060 are strong candidate genes for root development functions. The significant haplotypes identified in this study will be beneficial in future breeding programs for root improvement.