In higher plants, light-harvesting chlorophyll a/b binding (Lhc) proteins play a vital role in photosynthetic processes and are widely involved in the regulation of plant growth, development, and response to abiotic stress. However, the Lhc gene family has not been well identified in peaches (Prunus persica L.). In this study, 19 PpLhc genes were identified in the peach genome database, which were unevenly distributed on all chromosomes. Phylogenetic analysis demonstrated that PpLhc proteins could be divided into three major subfamilies, each of whose members had different exon–intron structures but shared similar conserved motifs. A total of 17 different kinds of cis-regulatory elements were identified in the promoter regions of all PpLhc genes, which could be classified into three categories: plant growth and development, stress response, and phytohormone response. In addition, transcriptomic data analysis and RT-qPCR results revealed that the expression profiles of some PpLhc genes changed under drought treatment, suggesting the crucial roles of Lhc genes in the regulation of plant tolerance to drought stress. Taken together, these findings will provide valuable information for future functional studies of PpLhc genes, especially in response to drought stress.

Hyperspectral imaging (HSI) has broad applications for detecting the soluble solids content (SSC) of fruits. This study explores the integration of HSI with machine learning and deep learning to predict SSC in two mandarin varieties: Ponkan and Tianchao. Traditional machine learning models (support vector machines and partial least squares regression) and deep learning models (convolutional neural networks, long short-term memory, and Transformer architectures) were evaluated for SSC prediction performance. Combined models that integrated different deep learning architectures were also explored. Results revealed varietal differences in prediction performance. For Ponkan mandarins, the best SSC prediction model was achieved by partial least squares regression, outperforming deep learning models. In contrast, for Tianchao mandarins, the deep learning model based on convolutional neural network slightly surpassed the traditional model. SHapley Additive exPlanations (SHAP) analysis indicated that the influential wavelengths varied between varieties, suggesting differences in key spectral features for SSC prediction. These findings highlight the potential of combining HSI with advanced modeling for citrus SSC prediction, while emphasizing the need for variety-specific models. Future research should focus on developing more robust and generalized prediction models by incorporating a broader range of citrus varieties and exploring the impact of varietal characteristics on model performance.

The cystathionine-β-synthase (CBS) domain is present in the proteins of all living organisms and functions as an energy sensor, regulating protein activity through the binding capacity of its adenosine ligands. The role of the CBS gene in plant growth and development, as well as tolerance to abiotic stresses, remains largely unknown, especially in grapevine. In our study, 32 members of the CBS gene family were obtained that were distributed on 15 chromosomes. The results of the structural and evolutionary tree analyses indicated that the VvCBS gene family exhibits diverse intron-exon patterns and highly conserved motifs. Furthermore, the phylogenetic classification of the VvCBS genes revealed the presence of three subfamilies. Subcellular localization analyses showed that the VvCBS genes are mainly located in the plasma membrane region. The secondary structure of the VvCBS protein mainly consists of α-helices, extended strands, β-turns, and random coils. The VvCBS gene family exhibited four co-linear gene pairs, while the numbers for Arabidopsis thaliana and rice were 21 and 7, respectively. The promoter cis-acting element analysis revealed the presence of light-responsive, hormone-responsive, stress-responsive, and growth- and development-related elements in the VvCBS family. The expression characterization demonstrated that 12 VvCBS genes exhibited high expression levels in all grape tissues. Additionally, the RT-qPCR expression analyses showed that the 32 VvCBS exhibited different responses to a variety of abiotic stresses (cold, drought, salt), suggesting that they were functionally differentiated. VvCBS27 was cloned from ‘Pinot Noir’ of grapevine with a coding sequence of 624 bp. Subcellular localization showed that VvCBS27 protein was mainly located in the cytoplasm, cell membrane, and nucleus. This study lays a foundation for elucidating the function of grape CBS protein.