Barnyard grass is a major noxious weed in paddy fields. Accurate and efficient identification of barnyard grass is crucial for precision field management. However, existing deep learning models generally suffer from high parameter counts and computational complexity, limiting their practical application in field scenarios. Moreover, the morphological similarity, overlapping, and occlusion between barnyard grass and rice pose challenges for reliable detection in complex environments. To address these issues, this study constructed a barnyard grass detection dataset using high-resolution images captured by a drone equipped with a high-definition camera in rice experimental fields in Haicheng City, Liaoning Province. A lightweight field barnyard grass detection model, YOLOv8n-SSDW, was proposed to enhance detection precision and speed. Based on the baseline YOLOv8n model, a novel Separable Residual Coord Conv (SRCConv) was designed to replace the original convolution module, significantly reducing parameters while maintaining detection accuracy. The Spatio-Channel Enhanced Attention Module (SEAM) was introduced and optimized to improve sensitivity to barnyard grass edge features. Additionally, the lightweight and efficient Dysample upsampling module was incorporated to enhance feature map resolution. A new WIoU loss function was developed to improve bounding box classification and regression accuracy. Comprehensive performance analysis demonstrated that YOLOv8n-SSDW outperformed state-of-the-art models. Ablation studies confirmed the effectiveness of each improvement module. The final fused model achieved lightweight performance while improving detection accuracy, with a 2.2% increase in mAP_50, 3.8% higher precision, 0.6% higher recall, 10.6% fewer parameters, 9.8% lower FLOPs, and an 11.1% reduction in model size compared to the baseline. Field tests using drones combined with ground-based computers further validated the model’s robustness in real-world complex paddy environments. The results indicate that YOLOv8n-SSDW exhibits excellent accuracy and efficiency. This study provides valuable insights for barnyard grass detection in rice fields.

Cut flower ornamental kale (Brassica oleracea var. acephala) is a biennial cultivar, which completes a sexual reproductive generation in two years. Isolated microspore culture (IMC) can accelerate plant homozygosity instead of self-pollinations. However, the application of IMC in cut flower ornamental kale was rare since its low rate of embryogenesis. It is proved that histone acetylation might affect the gene expression in microspores and led to the transformation of microspores from pollen development pathway to embryogenesis. In this paper, microspores, derived from three varieties of cut flower ornamental kale, Crane Bicolor (CB), Crane Pink (CP) and Crane Feather Queen (CFQ), were treated with histone deacetylation inhibitor (HDACI) trichostatin A (TSA). Results revealed that the appropriate concentration of TSA was 10 nM for CB with obtaining 5.39 embryos per bud, while for CP and CFQ was 5 nM with acquiring 10.89 and 16.99 embryos per bud, respectively. TSA treatment also reduced the embryonic mortality, of which 10 nM TSA treatments CB was the optimal and the embryonic mortality decreased to 25.01%. The double haploid (DH) proportion of regenerated plants reached 37.3%. These results contribute to improving the technology for IMC in cut flower ornamental kale.

Leafy head formation is one of the most important characteristics of Chinese cabbage, and the process is regulated by a series of genes and environmental factors. In this study, a non-heading short leaf mutant slm was identified from an ethyl methane sulfonate mutagenesis (EMS) population of the heading Chinese cabbage line FT. The most significant phenotypic characteristics of slm was shortening leaves and increasing leaf numbers, which led to failure to form a leafy head. Genetic analysis showed that a single recessive gene Brslm was responsible for the mutant phenotype. Mutmap analysis suggested that Brslm was located on chromosome A07, and four candidate genes were predicted. KASP analysis demonstrated that BraA07g039390.3C was the target gene of the candidates. BraA07g039390.3C is a homologous to Arabidopsis CLV1 encoding receptor kinase with an extracellular leucine-rich domain. Sequencing analysis revealed that a single SNP from G to A occurred in 904th nucleotide of Brclv1, which resulted in the change of the 302nd amino acid from Asp to Asn. The SNP was co-segregated with the mutant phenotype in F₂ individuals and located on the conserved domains. These results indicated that BrCLV1 was the mutant gene for slm which led to shortening leaves and increasing leaf numbers, disrupting the leafy heading formation in FT. These findings contribute to revealing the BrCLV1 function in leafy head formation in Chinese cabbage.