Search Results (1,764)

Background: Polyploidization generates extensive gene redundancy, but how duplicated metabolic genes are retained and subsequently diversified remains poorly understood. UDP-glycosyltransferases (UGTs) provide a suitable system for examining this process because they participate in specialized metabolism, plant development, and environmental responses. This study aimed to characterize the retention, expansion, and regulatory divergence of the UGT family in allotetraploid Panax ginseng at subgenome resolution. Methods: We integrated telomere-to-telomere (T2T) genome annotation, phylogenetic and chromosomal analyses, duplication classification, collinearity and Ka/Ks analyses, promoter cis-acting element prediction, developmental co-expression networks, and transcriptomic responses to biotic and abiotic treatments. Results: A total of 212 PgUGT genes were identified, including 104 and 108 members in the A and B subgenomes, respectively. The family exhibited an overall near-mirrored retention pattern between the two subgenomes, accompanied by local copy-number asymmetry. Whole-genome and segmental duplication accounted for 64.2% of the family, and 99.5% of the gene pairs with valid Ka/Ks estimates had values below 1, suggesting pervasive purifying selection. PgUGT-containing co-expression modules were associated with bud, stem, leaf, and fruit developmental conditions, while promoter cis-acting element compositions exhibited member-specific variation. Transcriptional responses to fungal pathogens and abiotic, hormone, and chemical treatments were concentrated in particular members and local gene arrays rather than being coordinated across entire clades or subgenomes. Conclusions: The PgUGT family is characterized by extensive ancestral copy retention accompanied by local copy-number changes and copy-specific regulatory divergence. These findings provide a subgenome-resolved framework for understanding UGT family evolution in allotetraploid ginseng and prioritize candidate PgUGT genes for subsequent functional validation. Full article

1-deoxy-D-xylulose-5-phosphate synthase (DXS) is the first key enzyme in the methylerythritol phosphate (MEP) pathway of plant terpenoid biosynthesis, and it plays a vital role in Eucommia ulmoides terpenoid biosynthesis. In this study, bioinformatics methods were used to comprehensively identify and analyze the expression pattern of the EuDXS gene family, aiming to provide a basis for further functional study of EuDXS genes. A total of four EuDXS gene family members were identified and named EuDXS1 to EuDXS4. The encoded proteins contained 625 to 713 amino acid residues, with molecular weight ranging from 67.98 kDa to 76.61 kDa. The theoretical isoelectric points varied from 6.79 to 8.69, aliphatic index was between 85.29 and 91.29. In silico subcellular localization prediction revealed that all EuDXS proteins were localized in chloroplasts. EuDXS gene members were categorized into three subfamilies, which were unevenly distributed on three chromosomes. The promoters of EuDXS genes contained various cis-acting elements related to stress response, phytohormone signaling, light response and growth regulation. Expression pattern analysis showed that EuDXS genes exhibited tissue-specific expression: EuDXS1 was highly expressed in stem, leaf and fruit, EuDXS2 was predominantly expressed in fruit. EuDXS1 and EuDXS2 exhibited high expression levels at the early developmental stage of fruits and leaves. In addition, EuDXS genes responded to salt and drought stress in varying degrees. Transient expression in tobacco revealed that EuDXS1 and EuDXS2 could increase carotenoid and total chlorophyll content. This study will provide important genetic resources for further exploration of EuDXS gene function and germplasm innovation in E. ulmoides. Full article

Tomato brown rugose fruit virus (Tobamovirus fructirugosum, ToBRFV) is an emerging virus that affects tomatoes, capsicum, and chili. Since its first detection in Jordan in 2015, the virus has been reported in more than 40 countries across all the continents. In Morocco, the virus was reported for the first time in October 2021. However, its genetic diversity remains unexplored. In this work, we used a collection of 100 tomato fruits from local markets to investigate the virus’s variability. Thirty-eight sequences were recovered and used to study evolutionary pressures acting on the N-terminus of the RNA-dependent RNA polymerase, the movement protein (MP), and the coat protein (CP) genes. The genetic diversity among Moroccan sequences was low, with over 99% nucleotide identity, which is consistent with the global situation, with the CP exhibiting higher diversity followed by the MP. We identified two sites with non-synonymous substitutions in the CP, and one in the MP. We used haplotype network analyses to reveal the population structure within the Moroccan isolates and studied their relationships with sequences from the rest of the world. Sequences from Morocco showed a clear geographic structure, suggesting that geographic factors, potentially combined with agricultural practices, may contribute to shaping the population structure of ToBRFV in Morocco. Our analyses suggest few introduction events, probably from Israel, Jordan, and Italy. Full article

Accurate and reliable fruit detection in agricultural fields is crucial for applications such as yield estimation, crop tracking, and autonomous harvesting. However, the high density of small fruits in images, overlap, and complex vegetation can limit object detection performance. In this study, LemonDet, a parameter-efficient object detection model based on YOLO11n, is proposed to improve the detection of small lemons in particular. Considering the object scale distribution in the dataset, the standard P3–P5 detection structure is restructured as P2–P4 to ensure the preservation of high-resolution spatial features. In addition, the Lemon Color Prior Convolution (LCP-Conv) module, which transfers the dataset-specific RGB color prior obtained from labeled lemon regions in the training data to the early feature extraction process, has been developed. A label-guided local image enhancement approach, applied only to training images, has also been included in the model to strengthen the limited pixel representations of small objects. In the experimental results, LemonDet achieved 87.2% Precision, 81.6% Recall, 84.3% F1-score, 89.2% mAP50, and 54.8% mAP50-95. With 0.99 million parameters, the model provides a more parameter-efficient architecture than the baseline YOLO models while achieving higher detection performance. Ablation results show that color normalization and label-guided local enhancement contribute to performance. The findings indicate that considering object scale distribution and dataset-specific color information together in model design is an effective approach for detecting small lemons. Full article

To optimize the varietal structure of fruit crops in arid environments and screen apricot-plum varieties suitable for cultivation in the Aksu region, a systematic agronomic evaluation was conducted to comprehensively analyze the phenotypic diversity and fruit quality traits of seven apricot-plum cultivars in Aksu, Xinjiang. In 2025, seven-year-old trees cultivated under identical and standardized agronomic conditions were selected as experimental materials. Key agronomic parameters, including flowering and fruiting phenology, floral and foliar morphological traits, fruit appearance properties, and internal nutritional profiles (soluble solids, soluble sugars, titratable acids, vitamin C, flavonoids, and total phenolics) were systematically measured. Correlation analysis and principal component analysis (PCA) were applied to quantitatively integrate these multi-dimensional datasets, identify key factors driving agronomic variation, and evaluate varietal adaptability to the arid climate. The results revealed a high degree of phenotypic diversity and significant variations in agronomic performance among the seven cultivars. Specifically, ‘Weihou’ recorded the highest single-fruit weight (111.27 g), indicating superior physical development, whereas ‘Konglongdan’ achieved the maximum soluble solid content (21.43%) and vitamin C level (96.41 mg/100 g), exhibiting exceptional nutritional quality. Despite these outstanding individual traits in specific cultivars, evaluating overall adaptability requires a holistic approach. PCA effectively captured 93.84% of the total variance through five extracted principal components, successfully modeling the interrelationships among vegetative vigor, reproductive morphology, and fruit chemical profiles. Based on the comprehensive agronomic scores, the varieties ranked as follows: ‘Fengweihuanghou’ > ‘Weihou’ > ‘Hongtianerong’ > ‘Konglongdan’ > ‘Weidi’ > ‘WeiWang’ > ‘Fengweimeigui’. Our findings identify ‘Fengweihuanghou’ as the most superior cultivar, with optimal agronomic adaptability and fruit quality balance under arid conditions. This study suggests that ‘Fengweihuanghou’ is a highly promising cultivar for large-scale cultivation in the Aksu region, providing a valuable reference for optimizing the local crop varietal structure, although further multi-year and multi-location validations are warranted. Full article

Background: Calcineurin B-like (CBL) protein-mediated calcium signaling represents a core regulatory pathway underlying plant abiotic stress adaptation. Ziziphus jujuba var. spinosa (sour jujube) is a perennial woody species with remarkable saline–alkali and drought tolerance, yet the CBL gene family in this species has not been systematically characterized. Methods: We performed genome-wide identification and characterization of the CBL gene family in sour jujube through integrated bioinformatic analyses, transcriptomic profiling, qRT-PCR, and yeast two-hybrid (Y2H) assays, with the annotation of one gene manually corrected based on molecular cloning and transcriptome validation. Results: Ten non-redundant ZjCBL genes were identified and grouped into three phylogenetic clades. Synteny and selection pressure analyses identified two collinear gene pairs that have undergone strong purifying selection. All ZjCBL proteins contain canonical EF-hand motifs and are predicted to be mainly localized to the plasma membrane, with ZjCBL4 and ZjCBL9 harboring additional N-terminal transmembrane helices. The promoter regions of ZjCBL genes are enriched in hormone- and stress-responsive cis-acting elements. ZjCBL1, ZjCBL2, ZjCBL3, and ZjCBL5 were highly expressed across multiple tissues, and ZjCBL1 exhibited sustained upregulation under long-term saline–alkali stress, concurrent with the accumulation of osmoprotectants. Y2H assays confirmed that ZjCBL1 interacts with two ZjCIPK proteins, exhibiting a stronger interaction signal with ZjCIPK13. Conclusions: This study provides the first systematic characterization of the CBL gene family in sour jujube and revises the annotation of ZjCBL1, establishing ZjCBL1 as an important component of the ZjCBL–ZjCIPK signaling network and offering candidate gene resources for molecular breeding to enhance stress tolerance in fruit crops. Full article

Melon (Cucumis melo L.), a widely cultivated species of the Cucurbitaceae family, is one of the most economically important fruit crops globally. TRM proteins have been implicated in the regulation of plant development, including processes related to cell division, cell expansion, and organ morphology. Although the functions of TRM family members have been extensively characterized in Arabidopsis, tomato, and cucumber, their roles in melon fruit development remain poorly understood. In this study, a total of 22 CmTRM (Cucumis melo) genes containing the conserved DUF3741 and DUF4378 domains were identified from the melon genome data via bioinformatics approaches. Subsequently, we systematically analyzed the physicochemical properties of CmTRM-encoded proteins as well as the chromosomal distribution, gene organization, conserved motif patterns, and promoter regulatory elements of all 22 CmTRM members. Notably, the high-accuracy three-dimensional structure prediction of the CmTRM proteins were predicted using the AlphaFold3, intraspecific collinearity analyses of the CmTRMs were performed, seven pairs of collinear genes were identified. Additionally, phylogenetic analysis revealed that the CmTRM proteins could be classified into eight distinct clades, with CmTRM05 and CmTRM21 assigned to Group I. We performed quantitative real-time PCR (qRT-PCR) analysis on all 22 CmTRMs. The results showed that CmTRM05, CmTRM19 and CmTRM21 exhibited significantly distinct stage-specific expression patterns across fruit developmental gradients between the two melon accessions with contrasting fruit phenotypes. These three genes were therefore prioritized as candidate genes for subsequent genetic functional validation. Furthermore, subcellular localization assays revealed that CmTRM05 primarily resides in the cytoplasm. In conclusion, this study advances our understanding of the TRM gene family in melon and offers a theoretical and practical basis for investigating the influence of TRM on melon fruit shape, which is of great significance for developing new melon varieties with desirable fruit shapes. Full article

Tropical viticulture has reached a considerable scale worldwide. Driven by innovative production systems such as two-harvests-a-year cultivation and ripening regulation, tropical grape-growing regions have become dynamic and economically significant viticultural areas. Most grape varieties currently cultivated in the tropics were originally bred in traditional temperate zones. When introduced to tropical regions, they often suffer from poor dormancy release, high disease and pest pressure, and compromised fruit quality. Therefore, it is imperative to conduct localized breeding directly under tropical stress conditions (e.g., high temperature, high humidity) to develop varieties with key target traits, including low chilling requirement, heat tolerance, disease resistance, suitability for two-harvest cultivation, good flavor, and good storage and transport tolerance. This paper systematically reviews the current status and breeding progress of the grape industry in major tropical viticultural regions, such as India, Brazil, and China. It summarizes the advances made by breeding teams in these countries in germplasm collection, crossbreeding, bud mutation selection, and the breeding and release of new varieties. The review concludes that developing adapted varieties through targeted breeding in tropics is essential for the sustainable and high-quality development of the grape industry in tropical regions. Full article

Macleania rupestris is an underutilized wild fruit-bearing shrub native to the Ecuadorian Andes, with considerable ecological importance and potential for sustainable local development. However, information regarding its genetic, morphological, and phytochemical diversity remains scarce. This study aimed to characterize the genetic and morphological diversity and total phenolic content (TPC) of M. rupestris populations. A multilocus analysis was performed using three previously amplified concatenated sequences. Fruit characterization was conducted using quantitative morphological and physicochemical traits, while TPC was expressed as gallic acid equivalents and determined in fruits collected from the same individuals. Relationships among genetic, fruit, phytochemical, and environmental variables were also explored. The multilocus phylogenetic structure showed no clear geographic pattern, while most fruit traits exhibited limited differentiation among provinces. However, soluble solids content (°Brix), seed number, and pedicel length differed significantly among provinces. Fruit moisture content was moderately negatively correlated with soluble solids content (r = −0.45) and moderately positively correlated with seed number (r = 0.46). These correlations represent observational associations and do not demonstrate causal relationships. No significant correlation was detected between fruit traits and TPC. These findings provide important scientific basis for the conservation, selection of promising genotypes, and future domestication of this underutilized Andean fruit species. Full article

Image-based cultivar identification remains challenging in perennial medicinal crops because cultivar-specific datasets are often small and morphological differences can be subtle. This study investigated whether a Siamese network-based hybrid learning framework could support cultivar classification and image-derived phenomic similarity analysis in Panax ginseng under low-data conditions. A total of 347 images representing 22 cultivars across four above-ground image acquisition categories were evaluated using stratified five-fold cross-validation. The framework jointly optimized class-weighted cross-entropy and contrastive losses, and five ImageNet-pretrained backbone architectures were assessed across contrastive margins. ConvNeXt-Tiny with a margin of 0.50 achieved the highest five-fold mean performance, with an accuracy of 64.31 ± 7.23% and a macro-F1 score of 61.66 ± 6.63%. UMAP visualization indicated qualitative reorganization of the embedding space after fine-tuning, while Grad-CAM++ localized model responses mainly to plant structures, including leaf, stem, and fruit regions, rather than broad background areas. Hierarchical clustering of cultivar embeddings further suggested structured phenomic relationships, with cosine distance and average linkage yielding a cophenetic correlation of 0.81 ± 0.08 and Kendall’s τ-b of 0.61 ± 0.06. These findings support the potential of hybrid classification and metric learning as a complementary tool for extracting interpretable phenomic representations from limited ginseng image datasets. The framework may provide supporting image-based evidence alongside conventional morphological cultivar assessment. However, the observed cultivar relationships should be considered exploratory and require validation across environments, developmental stages, independent datasets, and genetic information before broader biological interpretation. Full article

Fruit thinning concentrates nutrients and serves as an essential procedure for cultivating high-quality strawberries with exceptional palatability. It is established that controlling the number of fruits per peduncle to three to five significantly increases the proportion of large fruits and enhances sweetness while effectively reducing the risks of disease infection. Consequently, pesticide application is minimized, thereby ensuring food safety and improving overall product quality. Although precise execution baselines are a prerequisite for automated thinning, current research is largely restricted to isolated fruit recognition, leaving the direct detection of peduncle–fruit topological associations under complex occlusions unaddressed. To address this gap, a lightweight, three-stage (“coarse-to-fine”) detection method tailored for automated thinning is proposed. The pipeline comprises global localization, local cropping with background suppression, and fine secondary inference, which effectively mitigates environmental interference. To support this investigation, a dedicated dataset comprising 1131 high-quality images was constructed. For efficient edge deployment, YOLOv11n was adopted as the baseline architecture, integrated with structural Re-parameterized Convolution (RepConv), Coordinate Attention (CoordAtt), and a dynamic re-weighting loss. This configuration ensures robust feature extraction of slender peduncles with an extremely low parameter overhead. The experimental results demonstrate that with only 2.77 M parameters, the proposed model achieves an mAP@0.5 of 75.73% and an F1-score of 73.85%. Notably, the average false positive (FP) detections per image in complex scenarios were significantly reduced from 1.53 to 0.23. Following deployment on a Jetson Orin Nano Super edge device utilizing TensorRT and FP16 quantization, the end-to-end system inference speed stabilized at 15.58 frames per second (FPS) with near-lossless precision. Ultimately, this method provides a reliable technical foundation for automated thinning decisions, facilitates sustainable greenhouse management, and secures the supply of high-quality food from the source. Full article

In precision agriculture, fruit segmentation serves as a fundamental visual prerequisite for orchard robotic operations, including precision management, automated harvesting and yield estimation. For unripe passion fruits, complex scene interference, high fruit–leaf similarity and foliage occlusion in practical orchard scenes easily cause missed detection and over-segmentation in existing models. To address these challenges, we build a multi-scene unripe-passion-fruit dataset named ZKMPF. Based on the UNet architecture, we propose Environment-Adaptive and Prompt-Fusion UNet (EAPF-UNet). First, EAPF-UNet embeds an Environmental Adapter into the encoder, which adjusts feature parameters to mitigate complex scenes interference. Then, it incorporates a Localization Multi-Scale Fusion Module (LMSM) and Refinement Multi-Scale Fusion Module (LMFM) to achieve accurate localization and refinement of unripe passion fruits and address scale variations. Finally, it designs the Multi-Dimensional Prompt Fusion module that integrates color, geometry and texture priors to improve the feature discriminability between unripe passion fruits and background. We conduct experiments on our self-built dataset, comparing EAPF-UNet with eight other segmentation models. Evaluated against eight segmentation models, EAPF-UNet obtains mDice of 85.51% and mIoU of 77.16% across six metrics and achieves competitive segmentation results within this dataset’s test scenes. Full article

Background/Objectives: Sweet orange (Citrus sinensis (L.) Osbeck) is an economically important fruit crop that contributes substantially to food security and smallholder income in Mozambique. Despite this, the genetic diversity of the locally grown germplasm has not been characterized at the molecular level, limiting its improvement and conservation programs. This study assessed the genetic diversity and population structure of germplasm from 94 sweet orange trees sampled across four districts of Inhambane Province using DArTSeq single-nucleotide polymorphism (SNP) markers. Methods: After filtering 8111 SNPs for call rate (≥0.80) and minor allele frequency (≥0.01), 1263 markers were retained, of which 1144 were anchored to the nine chromosomes of the reference genome. Results: Sparse non-negative matrix factorization identified K = 1, indicating a single undifferentiated gene pool, supported by a smooth PCA scree with one weak axis. DAPC assigned individuals to their district only 38.3% of the time (random expectation = 25%; maximum a-score = 0.10), and the first two PCoA axes explained 10.33% of variation with complete district overlap, indicating no detectable geographic structure. Diversity was low, with observed heterozygosity (Ho = 0.247) exceeding expected heterozygosity (He = 0.138) and a negative inbreeding coefficient (Fis = −0.222). The pattern indicated a heterozygote excess consistent with the fixation of the heterozygous interspecific-hybrid genome under clonal propagation. A hierarchical analysis of molecular variance showed that differentiation among districts was negligible (0.04%), whereas 4.16% of variation was partitioned among orchards (farms) within districts, indicating that the little of the existing structure resides at the orchard level, confounded with propagation method and cultivar, rather than among districts. Most variation was partitioned within individuals (76.0%), and pairwise FST values (0.0005–0.0035) were uniformly low. Conclusions: These results indicate that the sweet orange orchards stem from a single, highly heterozygous gene pool redistributed through the exchange of seed and vegetative planting material. This underscores the need to introduce diverse external germplasm to broaden the genetic base for sustainable improvement in Mozambique. Full article

Continuous monocropping of tomato causes soil degradation, secondary salinization, and yield decline. Mushroom–vegetable rotation coupled with spent mushroom substrate (SMS) incorporation offers a recycling strategy to mitigate these obstacles. Here, a five-stage progressive field experiment (2023–2025, Yantai, Shandong, China) was conducted to screen the optimal local configuration of Stropharia rugosoannulata–tomato rotation and reveal its soil improvement mechanisms. The combination of variety Nieyang and an apple woodchip-based substrate achieved the highest mushroom yield, with the substrate formula (F = 11.64, p = 0.0006) dominating productivity. Deep incorporation of SMS into the 0–20 cm plow layer (M treatment) avoided the seedling stress and mortality caused by surface mulching and increased the marketable yield of the large-fruited tomato R35 by 28.0% (79,560 kg·ha⁻¹, p < 0.05) without altering fruit soluble solids (p = 0.611). Two consecutive rotation years increased soil organic carbon by 17.6% (from 3.29 to 3.87 g·kg⁻¹) and total nitrogen by 243% (from 0.79 to 2.71 g·kg⁻¹) and raised soil desalination efficiency from 33.5% to 56.2%, while soil EC and pH remained within the optimal range for tomato growth. Cross-regional verification showed universal regulation of soil pH and EC but background-dependent nutrient accumulation. The system improves soil fertility through mycelium-mediated biological desalination, progressive SMS-derived carbon pool accumulation, and complementary acid–base homeostasis, generating a net annual economic benefit of approximately 255,000 CNY·ha⁻¹ (≈38,060 USD·ha⁻¹). This recyclable rotation pattern is suitable for popularization in Jiaodong facility-grown tomato production. Full article

Accurate perception of eggplant fruits and stems remains challenging for greenhouse harvesting robots because illumination changes, foliage occlusion, fruit overlap, and background branches can degrade target visibility, particularly for small and curved stems. To improve joint fruit-and-stem detection under these conditions, this study develops an enhanced YOLOv8n model using a greenhouse dataset collected across different illumination levels, viewpoints, occlusion degrees, and fruit-overlap situations. The baseline network was modified in three aspects. Selected conventional convolutions in the backbone and neck were replaced by Omni-Dimensional Dynamic Convolution (ODConv) to improve feature adaptation to targets with different scales and shapes. Efficient Multi-Scale Attention (EMA) was placed after the SPPF module to emphasize informative responses from fruit and stem regions while reducing background interference. In addition, C2f_MSBlock was incorporated into the neck to strengthen multi-scale feature representation and fusion. The resulting model achieved 96.4% precision, 97.2% recall, 99.0% mAP@0.5, and 86.0% mAP@0.5:0.95, with 3.74 M parameters, 6.5 GFLOPs, and a model size of 7.9 MB. Relative to the original YOLOv8n, these four detection metrics increased by 2.2, 0.3, 0.5, and 2.9 percentage points, respectively, while GFLOPs decreased by 16.7%. These results indicate that the modified model improves detection robustness in complex greenhouse scenes while maintaining moderate computational requirements, providing a feasible visual perception approach for eggplant fruit recognition and stem localization in robotic harvesting. Full article