Summary: China is the origin and evolutionary centre of Oriental pears. Pyrus betuleafolia is a wild species native to China and distributed in the northern region, and it is widely used as rootstock. Here, we report the de novo assembly of the genome of P. betuleafolia‐Shanxi Duli using an integrated strategy that combines PacBio sequencing, BioNano mapping and chromosome conformation capture (Hi‐C) sequencing. The genome assembly size was 532.7 Mb, with a contig N50 of 1.57 Mb. A total of 59 552 protein‐coding genes and 247.4 Mb of repetitive sequences were annotated for this genome. The expansion genes in P. betuleafolia were significantly enriched in secondary metabolism, which may account for the organism's considerable environmental adaptability. An alignment analysis of orthologous genes showed that fruit size, sugar metabolism and transport, and photosynthetic efficiency were positively selected in Oriental pear during domestication. A total of 573 nucleotide‐binding site (NBS)‐type resistance gene analogues (RGAs) were identified in the P. betuleafolia genome, 150 of which are TIR‐NBS‐LRR (TNL)‐type genes, which represented the greatest number of TNL‐type genes among the published Rosaceae genomes and explained the strong disease resistance of this wild species. The study of flavour metabolism‐related genes showed that the anthocyanidin reductase (ANR) metabolic pathway affected the astringency of pear fruit and that sorbitol transporter (SOT) transmembrane transport may be the main factor affecting the accumulation of soluble organic matter. This high‐quality P. betuleafolia genome provides a valuable resource for the utilization of wild pear in fundamental pear studies and breeding.

Background: 'Kuerlexiangli’ (Pyrus sinkiangensis Yu), a native pear of Xinjiang, China, is an important agricultural fruit and primary export to the international market. However, fruit with persistent calyxes affect fruit shape and quality. Although several studies have looked into the physiological aspects of the calyx abscission process, the underlying molecular mechanisms remain unknown. In order to better understand the molecular basis of the process of calyx abscission, materials at three critical stages of regulation, with 6000 × Flusilazole plus 300 × PBO treatment (calyx abscising treatment) and 50 mg^.L^-1GA₃ treatment (calyx persisting treatment), were collected and cDNA fragments were sequenced using digital transcript abundance measurements to identify candidate genes. Results: Digital transcript abundance measurements was performed using high-throughput Illumina GAII sequencing on seven samples that were collected at three important stages of the calyx abscission process with chemical agent treatments promoting calyx abscission and persistence. Altogether more than 251,123,845 high quality reads were obtained with approximately 8.0 M raw data for each library. The values of 69.85%-71.90% of clean data in the digital transcript abundance measurements could be mapped to the pear genome database. There were 12,054 differentially expressed genes having Gene Ontology (GO) terms and associating with 251 Kyoto Encyclopedia of Genes and Genomes (KEGG) defined pathways. The differentially expressed genes correlated with calyx abscission were mainly involved in photosynthesis, plant hormone signal transduction, cell wall modification, transcriptional regulation, and carbohydrate metabolism. Furthermore, candidate calyx abscission-specific genes, e.g. Inflorescence deficient in abscission gene, were identified. Quantitative real-time PCR was used to confirm the digital transcript abundance measurements results. Conclusions: We identified candidate genes that showed highly dynamic changes in expression during the calyx abscission process. These genes are potential targets for future functional characterization and should be valuable for exploration of the mechanisms of calyx abscission, and eventually for developing methods based on small molecule application to induce calyx abscission in fruit production.

Flesh texture is an important quality trait and is related to people’s preference for fruit, especially for crisp pears. Puncture tests were carried out on 156 crisp pear fruit germplasm samples to analyze the diversity level of texture traits, to clarify the correlation between sensory description evaluation and instrumental traits, and to explore the effects of fruit ripening, size, and shelf life on the change in flesh texture. The results showed that puncture parameters were significantly different between crisp pear cultivars, and the work associated with the flesh limit compression force had the highest coefficient of variation (0.281). There was a significant correlation between puncture parameters and sensory evaluation scores. The correlation between sensory score and flesh firmness was the highest, with a correlation coefficient of 0.708, indicating that hardness can significantly influence the sensory evaluation of texture. Cluster analysis based on sensory evaluation and puncture determination could divide the germplasm resources of crisp pear into five texture categories: loosen, crunchy, crisp, tight–crisp, and dense. A comprehensive texture score model, constructed by principal component analysis, showed consistency with sensory evaluation scores and proved that the combination of a puncture test and sensory evaluation is the best way to identify and evaluate the texture of crisp pear. Further analysis of the influencing factors of flesh texture showed that fruit maturity and shelf life had significant effects on flesh quality. This study provides an important reference for the standardization, evaluation, and utilization of crisp pear variety resources.

The accurate and efficient identification of pear varieties is paramount to the intelligent advancement of the pear industry. This study introduces a novel approach to classifying pear varieties by recognizing their leaves. We collected leaf images of 33 pear varieties against natural backgrounds, including 5 main cultivation species and inter-species selection varieties. Images were collected at different times of the day to cover changes in natural lighting and ensure model robustness. From these, a representative dataset containing 17,656 pear leaf images was self-made. YOLOv10 based on the PyTorch framework was applied to train the leaf dataset, and construct a pear leaf identification and classification model. The efficacy of the YOLOv10 method was validated by assessing important metrics such as precision, recall, F1-score, and mAP value, which yielded results of 99.6%, 99.4%, 0.99, and 99.5%, respectively. Among them, the precision rate of nine varieties reached 100%. Compared with existing recognition networks and target detection algorithms such as YOLOv7, ResNet50, VGG16, and Swin Transformer, YOLOv10 performs the best in pear leaf recognition in natural scenes. To address the issue of low recognition precision in Yuluxiang, the Spatial and Channel reconstruction Convolution (SCConv) module is introduced on the basis of YOLOv10 to improve the model. The result shows that the model precision can reach 99.71%, and Yuluxiang’s recognition and classification precision increased from 96.4% to 98.3%. Consequently, the model established in this study can realize automatic recognition and detection of pear varieties, and has room for improvement, providing a reference for the conservation, utilization, and classification research of pear resources, as well as for the identification of other varietal identification of other crops.

Located in the southwestern region of China, the Yunnan–Kweichow Plateau, is closely related to the origins of Pyrus L. Despite this important status, there has been relatively little population genetics research focused on the wild Pyrus species in this area. To address this gap in knowledge, the present study was conducted investigating wild Pyrus species distributions in the Yunnan–Kweichow Plateau region. These analyses entailed the collection of 80 accessions, whole-genome resequencing, and the detection of variants including SNPs, InDels, SVs, and CNVs. Genetic structure analyses revealed clear differences between P. pashia and P. calleryana, where the former was additionally subdivided into five groups. The genetic structure of these accessions was closely aligned with their geographic distribution, highlighting the fragmented nature of wild Pyrus populations on the Yunnan–Kweichow Plateau. Analyses of genetic diversity suggested that the central and easter portions of Yunnan Province are key centers of Pyrus diversity on the Yunnan–Kweichow Plateau, whereas the highest degree of differentiation was observed for wild Pyrus in the southwest and northwest regions. Demographic analyses indicated that wild Pyrus populations on the Yunnan–Kweichow Plateau are currently in a state of population contraction, with evidence of migration events between these populations.

Introduction: Wild Ussurian Pear germplasm resource has rich genetic diversity, which is the basis for genetic improvement of pear varieties. Accurately and efficiently identifying wild Ussurian Pear accession is a prerequisite for germplasm conservation and utilization.Methods: We proposed YOLOv10n-MCS, an improved model featuring: (1) Mixed Local Channel Attention (MLCA) module for enhanced feature extraction, (2) Simplified Spatial Pyramid Pooling-Fast (SimSPPF) for multi-scale feature capture, and (3) C2f_SCConv backbone to reduce computational redundancy. The model was trained on a self-made dataset of 16,079 wild Ussurian Pear leaves images.Results: Experiment results demonstrate that the precision, recall, mAP50, parameters, FLOPs, and model size of YOLOv10n-MCS reached 97.7(95% CI: 97.18 to 98.16)%, 93.5(95% CI: 92.57 to 94.36)%, 98.8(95% CI: 98.57 to 99.03)%, 2.52M, 8.2G, and 5.4MB, respectively. The precision, recall, and mAP50 are significant improved of 2.9%, 2.3%, and 1.5% respectively over the YOLOv10n model (p<0.05). Comparative experiments confirmed its advantages in precision, model complexity, model size, and other aspects.Discussion: This lightweight model enables real-time wild Ussurian Pear identification in natural environments, providing technical support for germplasm conservation and crop variety identification.