The ripening process of Chinese bayberries (Myrica rubra) is intricate, involving a multitude of molecular interactions. Here, we integrated transcriptomic and metabolomic analysis across three developmental stages of the Myrica rubra (M. rubra) to elucidate these processes. A differential gene expression analysis categorized the genes into four distinct groups based on their expression patterns. Gene ontology and pathway analyses highlighted processes such as cellular and metabolic processes, including protein and sucrose metabolism. A metabolomic analysis revealed significant variations in metabolite profiles, underscoring the dynamic interplay between genes and metabolites during ripening. Flavonoid biosynthesis and starch and sucrose metabolism were identified as key pathways, with specific genes and metabolites playing crucial roles. Our findings provide insights into the molecular mechanisms governing fruit ripening in M. rubra and offer potential targets for breeding strategies aimed at enhancing fruit quality.

Fruit development and softening play pivotal roles in determining fruit quality and post-harvest shelf life in Chinese bayberry (Myrica rubra). However, the specific role of beta (β)-galactosidase, particularly β-galactosidase of M. rubra (MrBGAL), in facilitating fruit softening remains unclear. In this study, we aimed to address this gap by investigating the involvement of MrBGALs genes in fruit softening. We identified all 15 MrBGALs and conducted a comprehensive analysis, including phylogenetic relationships, gene structure, protein motifs, co-linearity, and expression patterns. Using phylogenetic analysis, we classified all MrBGALs into five distinct groups. Additionally, cis-element prediction and comparative genome analysis provided insightful clues about the functionality of MrBGALs. Transcriptome data revealed unique expression patterns of MrBGALs throughout various fruit development stages. These findings introduce valuable candidate genes that can contribute to unraveling the functions and molecular mechanisms governing fruit development and softening in Chinese bayberry.

The accurate assessment of Chinese bayberry (Myrica rubra) maturity is critical for intelligent harvesting. This study proposes a novel cascaded framework combining instance segmentation and multi-feature regression for accurate maturity detection. First, a lightweight SOLOv2-Light network is employed to segment each fruit individually, which significantly reduces computational costs with only a marginal drop in accuracy. Then, a multi-feature extraction network is developed to fuse deep semantic, color (LAB space), and multi-scale texture features, enhanced by a channel attention mechanism for adaptive weighting. The maturity ground truth is defined using the a*/b* ratio measured by a colorimeter, which correlates strongly with anthocyanin accumulation and visual ripeness. Experimental results demonstrated that the proposed method achieves a mask mAP of 0.788 on the instance segmentation task, outperforming Mask R-CNN and YOLACT. For maturity prediction, a mean absolute error of 3.946% is attained, which is a significant improvement over the baseline. When the data are discretized into three maturity categories, the overall accuracy reaches 95.51%, surpassing YOLOX-s and Faster R-CNN by a considerable margin while reducing processing time by approximately 46%. The modular design facilitates easy adaptation to new varieties. This research provides a robust and efficient solution for in-field bayberry maturity detection, offering substantial value for the development of automated harvesting systems.