Genomic analyses of diverse wild and cultivated accessions provide insights into the evolutionary history of jujube.

The Chinese jujube (Ziziphus jujuba Mill.), a member of the Rhamnaceae family, is an important perennial fruit tree crop of substantial economic, ecological and nutritional value, and is also used as a traditional herbal medicine. Here, we report the resequencing of 493 jujube accessions, including 202 wild and 291 cultivated accessions at >16× depth. Our population genomic analyses revealed that the Shanxi-Shaanxi area of China was jujube's primary domestication centre and that jujube was then disseminated into East China before finally extending into South China. Divergence events analysis indicated that Ziziphus acidojujuba and Ziziphus jujuba diverged around 2.7 Mya, suggesting the interesting possibility that a long pre-domestication period may have occurred prior to human intervention. Using the large genetic polymorphism data set, we identified a 15-bp tandem insertion in the promoter of the jujube ortholog of the POLLEN DEFECTIVE IN GUIDANCE 1 (POD1) gene, which was strongly associated with seed-setting rate. Integrating genome-wide association study (GWAS), transcriptome data, expression analysis and transgenic validation in tomato, we identified a DA3/UBIQUITIN-SPECIFIC PROTEASE 14 (UBP14) ortholog, which negatively regulate fruit weight in jujube. We also identified candidate genes, which have likely influenced the selection of fruit sweetness and crispness texture traits among fresh and dry jujubes. Our study not only illuminates the genetic basis of jujube evolution and domestication and provides a deep and rich genomic resource to facilitate both crop improvement and hypothesis-driven basic research, but also identifies multiple agriculturally important genes for this unique perennial tree fruit species.

RT-DETR-SoilCuc: detection method for cucumber germinationinsoil based environment.

Existing seed germination detection technologies based on deep learning are typically optimized for hydroponic breeding environments, leading to a decrease in recognition accuracy in complex soil cultivation environments. On the other hand, traditional manual germination detection methods are associated with high labor costs, long processing times, and high error rates, with these issues becoming more pronounced in complex soil-based environments. To address these issues in the germination process of new cucumber varieties, this paper utilized a Seed Germination Phenotyping System to construct a cucumber germination soil-based experimental environment that is more closely aligned with actual production. This system captures images of cucumber germination under salt stress in a soil-based environment, constructs a cucumber germination dataset, and designs a lightweight real-time cucumber germination detection model based on Real-Time DEtection TRansformer (RT-DETR). By introducing online image enhancement, incorporating the Adown downsampling operator, replacing the backbone convolutional block with Generalized Efficient Lightweight Network, introducing the Online Convolutional Re-parameterization mechanism, and adding the Normalized Gaussian Wasserstein Distance loss function, the training effectiveness of the model is enhanced. This enhances the model's capability to capture profound semantic details, achieves significant lightweighting, and enhances the model's capability to capture embryonic root targets, ultimately completing the construction of the RT-DETR-SoilCuc model. The results show that, compared to the RT-DETR-R18 model, the RT-DETR-SoilCuc model exhibits a 61.2% reduction in Params, 61% reduction in FLOP, and 56.5% reduction in weight size. Its mAP@0.5, precision, and recall rates are 98.2%, 97.4%, and 96.9%, respectively, demonstrating certain advantages over the You Only Look Once series models of similar size. Germination tests of cucumbers under different concentrations of salt stress in a soil-based environment were conducted, validating the high accuracy of the RT-DETR-SoilCuc model for embryonic root target detection in the presence of soil background interference. This research reduces the manual workload in the monitoring of cucumber germination and provides a method for the selection and breeding of new cucumber varieties.

YOLOv8-segANDcal: segmentation, extraction, and calculation of soybean radicle features.

The high-throughput and full-time acquisition of images of crop growth processes, and the analysis of the morphological parameters of their features, is the foundation for achieving fast breeding technology, thereby accelerating the exploration of germplasm resources and variety selection by crop breeders. The evolution of embryonic soybean radicle characteristics during germination is an important indicator of soybean seed vitality, which directly affects the subsequent growth process and yield of soybeans. In order to address the time-consuming and labor-intensive manual measurement of embryonic radicle characteristics, as well as the issue of large errors, this paper utilizes continuous time-series crop growth vitality monitoring system to collect full-time sequence images of soybean germination. By introducing the attention mechanism SegNext_Attention, improving the Segment module, and adding the CAL module, a YOLOv8-segANDcal model for the segmentation and extraction of soybean embryonic radicle features and radicle length calculation was constructed. Compared to the YOLOv8-seg model, the model respectively improved the detection and segmentation of embryonic radicles by 2% and 1% in mAP50-95, and calculated the contour features and radicle length of the embryonic radicles, obtaining the morphological evolution of the embryonic radicle contour features over germination time. This model provides a rapid and accurate method for crop breeders and agronomists to select crop varieties.