Accurate prediction of the cut chrysanthemum growth cycle is essential for precise market scheduling and quality control. While existing models primarily target the flowering date, key developmental stages remain poorly quantified. Through controlled experiments with varying planting dates and light levels, this study systematically analyzed the stage-specific influence of photosynthetic photon flux (PPF). We found that reduced PPF significantly delays both vegetative growth and harvest timing but does not affect floral bud differentiation. To address these stage-dependent responses, we developed a multi-model ensemble (MME) framework that integrates the most accurate models for each critical phase: the accumulated photo-thermal product for initiating short-day treatment, the triangular-function-based relative thermal effect for bud emergence, and the chrysanthemum clock model for the optimal harvest date. Validation results demonstrate that this integrated approach achieves significantly higher predictive accuracy than any single model. This research not only provides a reliable tool for the year-round precision production management of cut chrysanthemum but also offers a physiologically-based MME methodology reference for modeling the growth of horticultural crops.

In many flowering plants, the transition from vegetative growth to reproductive development is regulated by seasonal changes in photoperiod. Under inductive photoperiods, leaves produce the florigen FT (FLOWERING LOCUS T), which is transported to the shoot apex to promote flowering. The photoperiod is known to have a major effect on the flowering of chrysanthemum. In the perennial short-day (SD) plant Chrysanthemum seticuspe, the expression of CsFTL3 (FT-like gene) does not increase immediately after shifting from long-day (LD) to SD conditions but gradually accumulates under continuous SD conditions, peaking during inflorescence development. However, the underlying mechanism remains elusive. We show that CsFDL1 (an ortholog of FD) is upregulated, while CsFTL3 is downregulated in leaves during the initial stage of SD inductions. Furthermore, the expression of CsFTL3 is upregulated in the leaves of CsFDL1-knockdown transgenic lines. CsFDL1 is expressed in leaves and forms a complex with CsFTL3 to recognize several TCGA- and ACGT-containing motifs in the CsFTL3 promoter. The CsFTL3-CsFDL1 complex downregulates CsFTL3 expression, thereby preventing its excessive induction by SD signals and inhibiting precocious floral transition. This study reveals that CsFDL1 acts as a key early repressor in the photoperiodic flowering pathway of chrysanthemum leaf, mediating negative feedback regulation by forming a complex with CsFTL3 to achieve precise temporal control of SD-dependent flowering responses.

Abstract Black spot disease (BSD), caused by Alternaria alternata, is a devastating threat to the chrysanthemum industry, yet its genetic basis remains largely elusive. The present study aimed to decipher the genetic architecture of chrysanthemum BSD resistance and to discover genetic loci and candidate genes using genome-wide association studies (GWAS) in a biparental F1 population (n = 164). Phenotypic evaluations of BSD resistance were conducted using both multi-stage detached-leaf assays and seedling-stage in vivo inoculations. The disease severity index (DSI) exhibited wide coefficient of variation (CV: 26.21%–54.84%) and high broad-sense heritability (0.71–0.95), with significant transgressive segregation observed in the F1 progeny. 375 865 high-quality SNPs-based GWAS identified 220 quantitative trait nucleotides (QTNs) and 36 QTN-by-environment interactions (QEIs), explaining up to 7.39% and 3.46% of the phenotypic variance, respectively. Among 26 stable QTNs, 17 favorable alleles displayed significant additive effects and a clear dosage-pyramiding effect (P < 0.001). By integrating functional annotation with transcriptome profiling, 34 candidate genes involved in immune defense were identified within the candidate intervals. Notably, three key candidate genes, CmABF1, CmSINAT3, and CmLTPG1, were validated as positive regulators of BSD resistance through transient overexpression and silencing assays. The research findings provide crucial genetic resources for the molecular improvement of resistance to BSD in chrysanthemums.

Cliff habitats are characterized by limited and heterogeneous water availability, requiring plants to develop adaptive strategies to cope with drought stress. Opisthopappus longilobus, a cliff-endemic Asteraceae species restricted to the Taihang Mountains of northern China, has evolved under persistent water-limited conditions and represents a valuable model for investigating the molecular mechanisms underlying drought adaptation. However, the transcriptional regulatory networks involved in its drought response remain largely unexplored. In this study, we performed RNA sequencing of O. longilobus leaves under control and drought conditions to investigate drought-responsive regulatory networks. Six RNA-seq libraries were generated, and a total of 5260 differentially expressed genes (DEGs) were identified in response to drought stress. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these DEGs were mainly associated with phytohormone signal transduction, stress-responsive regulation, defense responses, metabolic reprogramming, and transcriptional regulation. Notably, multiple transcription factor families, including MYB, ERF, and ABF, were enriched among drought-responsive genes, suggesting their involvement in drought adaptation. Furthermore, quantitative RT-PCR was used to validate the RNA-seq results. Among the drought-responsive transcription factors, an R2R3-MYB transcription factor, OlMYB35, was identified as a candidate regulator and was further demonstrated to play a positive role in drought response through transient transformation assays. Taken together, this study provides new insights into drought-responsive regulatory mechanisms in O. longilobus and identifies OlMYB35 as a promising candidate gene for further functional validation and potential application in stress-resilient chrysanthemum breeding.

Accurate classification of crop cultivars is a crucial technical support for germplasm resource research and targeted breeding. Chrysanthemum has ornamental, beverage, and medicinal values, with over 30,000 cultivars worldwide. The large scale of germplasm resources and the subtle differences in key morphological traits such as flower shape and petal type make traditional manual identification difficult and inefficient. General deep learning models can achieve high recognition accuracy in chrysanthemum cultivar classification; however, their relatively large parameter size and computational overhead limit their efficient deployment on mobile or edge devices in field scenarios. To address these problems, we first constructed a large-scale fine-grained chrysanthemum dataset, Chry-FG-151, containing 43,325 RGB images of 151 cultivars across six color series (white, yellow, orange, pink, purple, red), captured under natural light using various types of mobile devices. To reduce the influence of complex field backgrounds during training, we developed a Background Substitution Module (BSM) that combines explicit foreground extraction, morphological refinement, and neutral solid-color background reconstruction to generate foreground-focused augmented samples. To strengthen spatial responses to flower-related regions, we integrated a Spatial Semantic Calibration Module (SSCM), implemented as a lightweight pointwise-convolution spatial gate without spatial downsampling, to recalibrate feature maps for fine-grained cultivar discrimination. To meet the computational constraints of mobile deployment, we built a lightweight classification network, CCNet, consisting of a stem, six inverted residual bottleneck layers, a last expansion layer, two DropBlock2D layers placed before and after SSCM, and a lightweight classifier. CCNet achieved a test accuracy of 99.31 ± 0.24% on Chry-FG-151 under the full training pipeline. Under the BSM-only public-dataset protocol, it achieved test accuracies of 92.82 ± 0.84% and 86.18 ± 1.76% on Public Dataset I and Oxford 102 Flowers, respectively. The model contains 0.16 M parameters and requires 0.10 G FLOPs. Grad-CAM visualization and foreground–background attention analysis showed that CCNet allocated a slightly larger proportion of activation responses to flower foreground regions than MobileNetV3-small. Across seven backbone models and two public datasets, the BSM-only setting yielded higher mean test accuracy than the no-augmentation baseline. The compiled model size was 312.13 KB, and the single-image inference latency on the tested mobile CPU was 143 ms. These results support the feasibility of on-device inference under the evaluated hardware setting. This study presents a lightweight, deployment-oriented approach for intelligent chrysanthemum cultivar recognition.

Assessing plant nutritional status through phenotypic traits is essential for optimizing fertilization in cut chrysanthemum (Chrysanthemum morifolium Ramat.) production; however, practical and rapid methods for nitrogen level identification remain limited. In this study, cultivation experiments were conducted on the major cultivar ‘Jingcheng’ under three nitrogen supply levels within typical production ranges. A PSPNet (Pyramid Scene Parsing Network) model was used to segment entire leaves and top lobes, and a YOLO v11n-pose model was employed to detect ten key points per leaf, enabling extraction of ten measured and nine calculated shape parameters. The PSPNet achieved a mean accuracy of 0.9926 with 59.21 GFLOPs, while YOLO v11n-pose obtained a mAP0.5 of 0.982 with only 2.73 M parameters, ensuring efficient and accurate feature extraction. The extracted leaf shape parameters were measured with mean relative errors (MRE) below 2.2%, confirming the accuracy of the proposed morphological parameter extraction framework. Calculated parameters showed higher stability (CV ≈ 10%) than directly measured ones (CV > 20%), particularly in mid- to lower-position leaves. One-way ANOVA indicated that eight calculated parameters of lower and middle leaves differed significantly among nitrogen treatments (P < 0.05). Among tested classifiers, the XGBoost model performed best, with an accuracy of 0.8617 and a Kappa coefficient of 0.7926. Feature importance analysis identified the Ratio of terminal lobe length to leaf length and Top lobe aspect ratio as the key discriminative indicators. The results establish an efficient and interpretable morphological framework for rapid identification of nitrogen supply levels in cut chrysanthemum, providing support for further precision nutrient management and intelligent cultivation.

Under greenhouse production conditions, variability in fertilization management, substrate properties, and microenvironmental factors can disrupt balanced nutrient uptake, often resulting in localized or transient multi-element nutrient imbalances. Hyperspectral sensing provides continuous and high-resolution spectral information for plant nutrient assessment. However, most existing studies focus on single-element deficiencies or simplified scenarios, which limits their applicability to complex nutritional environments encountered in practice. To address this limitation, we designed a series of single- and dual-element deficiency treatments in four cultivars of chrysanthemum (Chrysanthemum morifolium Ramat.), an important cut-flower crop whose ornamental quality is highly influenced by nutrient supply. Sampling was conducted at five key growth stages across three independent experiments, yielding a total of 615 data points. Each treatment included replicates and was confirmed based on characteristic deficiency symptoms. A hyperspectral-based qualitative classification framework was developed to assess nutrient imbalances under controlled greenhouse conditions. Results indicate that although some nutrient deficiencies exhibit similar visual or phenotypic symptoms, their hyperspectral responses are distinguishable, suggesting that hyperspectral data can capture subtle differences associated with distinct nutrient imbalance conditions. To mitigate class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, and multiple classification models were evaluated using cross-validation. The Gradient Boosting Decision Tree (GBDT) classifier combined with SMOTE showed the most consistent performance across nutrient-recognition tasks, achieving cross-validation accuracies from 0.9191 ± 0.0401 to 0.8556 ± 0.0516, balanced accuracies from 0.9595 to 0.8447, F1 from 0.9591 to 0.8496 and testing accuracies from 0.9200 to 0.8269, balanced accuracies from 0.9167 to 0.8269, F1 from 0.9140 to 0.8244. Overall, this study presents a non-destructive hyperspectral framework for classifying multi-element nutrient imbalances and demonstrates its effectiveness under greenhouse conditions, supporting hyperspectral-based nutritional assessment in ornamental crops. Further validation across diverse genotypes, seasons, and environmental conditions is needed to confirm broader applicability and model generalizability.

Cut chrysanthemum is a globally important high-value cut flower crop. Excessive nitrogen (N) application is prevalent in its production, leading not only to reduced flower quality and resource waste but also to environmental risks. However, the insufficient understanding of the dynamic N accumulation patterns throughout the entire growth cycle of chrysanthemum constrains the effective implementation of precision fertilization and scientific N reduction strategies. In this study, a two-year soilless cultivation experiment with a gradient of N application rates was conducted using the cut chrysanthemum cultivar ‘Nannong Xiaojinxing’. The study used principal component analysis (PCA) and regression modeling to systematically quantify the N requirements at key growth stages and to establish a precision fertilization strategy based on plant N accumulation. Results indicated that growth and quality indicators of chrysanthemum initially increased and then decreased with increasing N application, but the peak intervals varied among different indicators. Growth indicators were integrated via PCA into two principal components—phenotype and biomass—to generate a comprehensive score. Both this score and the cut-flower quality indicators were well-described by a quadratic regression model with N accumulation, thereby precisely determining the optimal N accumulation at each growth stage. Continued fertilization beyond the optimal N requirement induced luxury N absorption, consequently reducing N use efficiency. A conversion model between N accumulation and application rate was established. By applying the identified optimal N accumulation values to this model, the optimal N application rates were determined for the slow growth, rapid growth, flower bud differentiation, flower bud swelling, and flower color appearance stages as 89, 155, 35, 47, and 12 mg·plant⁻¹, respectively. Implementation of this optimized protocol resulted in a N agronomic efficiency of 140–160 g·g⁻¹ and an apparent N recovery rate of 60%–70%. This study pivots the N fertilization strategy for cut chrysanthemum from a fixed-amount regime to a dynamic management system centered on the plant’s optimal N status, thereby providing a robust pathway to on-demand fertilization, scientific N reduction, and stable quality production.

Chrysanthemum contains numerous active compounds, including flavonoids and phenolic acids, with its dried capitula widely used for tea and medicinal applications. The content of functional compounds is readily influenced by environmental factors, and the use of varieties with high-level and stable bioactive compounds is essential for sustainable cultivation. However, a key challenge is identifying genotypes that consistently perform well for functional-component traits in contemporary breeding activities. This study aimed to evaluate the performance and stability of functional components in tea chrysanthemums across multiple years. Total flavonoids, chlorogenic acid, luteoloside, and isochlorogenic acid A were investigated in 24 tea chrysanthemum accessions across three growing years of 2018, 2021, and 2022. The additive main effects and multiplicative interaction (AMMI) model analysis revealed significant genotype (G), environment (E), and genotype-by-environment interaction (GEI) effects for all functional traits across three growing years. The GEI accounted for 63.58% to 80.82% of the variation across the four components in the AMMI model. Based on the AMMI stability value (ASV) parameter, the tea chrysanthemums showing the most stable concentrations of total flavonoids, chlorogenic acid, luteoloside, and isochlorogenic acid A were identified. Based on phenotypic values and stability results, Suju-6, Hongxinju, Wangongju, and Baixiaoxiangju performed relatively well across the functional components investigated, making them promising candidates for future breeding and promotion programs. These findings provide valuable insights into the genetic basis of functional elements in tea chrysanthemum and will contribute to further genetic improvement.

This study examined the intergeneric chemodiversity and bioactivities of essential oils (EOs) from 50 accessions of four Anthemideae genera (Chrysanthemum, Opisthopappus, Ajania, and Crossostephium). Hydrodistilled EOs from 25 representative accessions were predominantly terpenoids. Multivariate analyses—principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA)—clearly discriminated the four genera and revealed a correspondence between chemical profiles and original geographic provenance. This finding suggests that essential oil chemotypes in Anthemideae largely reflect genetically fixed chemotypic differentiation rather than plastic responses to local environments. Characteristic marker compounds were identified for each genus, including caryophyllene oxide in Opisthopappus and 1,8-cineole with camphene in Ajania and Crossostephium. Bioactivity assays revealed significant intergeneric variation in antioxidant and antibacterial activities. Chemometric analysis linked these activities to specific compounds: antioxidant capacity was associated with myrcene, eugenol, and γ-elemene, while antibacterial effects correlated with guaiol, spathulenol, and bisabolol. Scanning electron microscopy (SEM) confirmed that the antibacterial mechanism involves concentration-dependent bacterial membrane disruption. Collectively, this work provides a chemotaxonomic framework for Anthemideae, elucidates key structure-activity relationships, and offers valuable insights for germplasm screening and natural product development.

Flowering represents a critical transition from vegetative to reproductive growth in plants. For the ornamental species chrysanthemum (Chrysanthemum morifolium), the precise regulation of its flowering time is of particular importance. GIGANTEA (GI) is a conserved plant-specific protein whose expression is photoperiod-regulated and which plays a key role in flowering time control. However, its function in short-day plants such as chrysanthemum remains poorly understood. This study demonstrates that CmGI functions as a flowering repressor in chrysanthemum and exhibits rhythmic expression in the shoot apex. CmGI binds directly to the promoter of the floral integrator SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (CmSOC1) and represses its expression. We further found that CmGI can interact with BAF60, a subunit of the SWI/SNF chromatin remodeling complex. Overexpression of CmBAF60 resulted in early flowering, whereas knockdown lines showed delayed flowering. Genetic evidence demonstrated that CmGI antagonizes CmBAF60 to regulate chrysanthemum flowering through CmSOC1. Additionally, CmBAF60 increased H3K4me3 deposition and chromatin accessibility at the CmSOC1 promoter, whereas CmGI reduced them. Therefore, our work reveals that CmGI fine-tunes the flowering process in chrysanthemum by antagonizing CmBAF60 to co-regulate H3K4me3 deposition and chromatin accessibility at the CmSOC1 promoter.

The number of petals in an inflorescence is an important phenotypic indicator for quality evaluation and cultivar identification of cut chrysanthemums (Chrysanthemum morifolium Ramat.). Current manual measurement methods are time-consuming, error-prone, and poorly suited to the complex geometry of chrysanthemum flowers, which limits their utility for large-scale phenotyping and breeding programs. Although image-based phenotyping has advanced rapidly, automated and reliable methods for petal counting in densely packed or partially obscured inflorescences remain underdeveloped. Here, we developed a deep learning-based framework for automatic extraction of petal number in cut chrysanthemums. Images from multiple varieties were collected to construct a representative dataset, and petal density maps were generated through manual annotation with Gaussian kernel function. We employed a Congested Scene Recognition Network (CSRNet) enhanced with a Squeeze-and-Excitation (SE) channel attention mechanism (SE-CSRNet) for petal density estimation. Spearman correlation analysis revealed strong agreement between visible and actual petal counts (Spearman's r = 0.953, p < 0.0001). Compared with the original CSRNet, SE-CSRNet reduced mean absolute error (MAE) and root mean squared error (RMSE) by 5.2% and 7.4%, respectively. Further optimization using regression fitting revealed that random forest achieved the best performance (MAE = 4.24, RMSE = 5.06, R2 = 0.967), indicating reliable stability and satisfactory generalization under the conditions evaluated in this work. Application of the optimized model to two cut chrysanthemum varieties confirmed its practicality by successfully detecting reductions in petal number under high-temperature stress. Our results demonstrate that integrating dataset construction, deep learning-based density estimation, and machine learning optimization enables efficient and accurate prediction of petal number in cut chrysanthemums.

Polyploidization is a major driver of plant evolution and stress adaptation, yet its role in modulating biotic stress resistance through epigenetic mechanisms remains poorly understood. This study demonstrates that autotetraploidization in Chrysanthemum lavandulifolium significantly enhances resistance to Alternaria alternata, the cause of black spot disease. Whole-genome methylome and transcriptome analyses reveal that polyploidization induces locus-specific CHH hypomethylation in the promoters of a subset of WRKY transcription factors, leading to their transcriptional activation upon fungal infection. Functional characterization of CIWRKY103, a key hypomethylated WRKY gene, confirms its critical role in conferring disease resistance. Chemical inhibition of DNA methylation (5-azacytidine treatment) in diploid plants mimics the tetraploid phenotype by activating WRKY103 expression and enhancing resistance. This epigenetic regulatory mechanism is conserved across diverse chrysanthemum species, highlighting the potential of targeting DNA methylation to modulate fungal disease resistance in polyploid crops. Our findings unveil a novel link between polyploidy, epigenetic reprogramming, and pathogen defense, offering strategic insights for sustainable crop protection.

Drought is one of the most serious abiotic stresses limiting plant productivity and becomes increasingly extreme worldwide due to the ongoing deterioration of the global climate. Chrysanthemum ( Chrysanthemum morifolium Ramat.), one of the four most popular cut flowers in the world, is sensitive to water-limited environment. However, the genetic basis and causal genes underlying drought tolerance (DT) remain largely unknown. In this research, multi-locus GWAS was employed to detect the genetic loci and candidate genes for DT in a diverse panel of 200 cut chrysanthemum accessions that were genotyped with 330,710 high-quality SNPs. As a result, 43 stable QTNs in single-environment analysis, 18 stable QTNs and 115 QEIs in multiple-environments analysis were identified via the 3VmrMLM method. Among the genes around stable QTNs and QEIs, eleven were homologous to known DT regulatory genes in other plants such as WRKY57 , MYB121 , GH3.6 . In addition, seven candidate genes were predicted to be associated with DT related traits by combing the functional annotation, transcriptomics data and quantitative real-time PCR. More importantly, four drought-tolerant cultivars harboring favorable alleles were identified as pre-bred material to improve tolerance of cultivated chrysanthemum. These findings provide robust insights into the genetic architecture of DT and offer valuable prospects for the molecular breeding of chrysanthemum.

Flowering is a crucial process in the growth and development of plants, playing an essential role in their life cycles. Thus, research into the regulation of flowering time holds significant importance. While CmBBX7 has been identified as a flowering activator regulated by photoperiod, the molecular mechanisms underlying its regulation at the protein level remain unclear. In this study, we utilized yeast two-hybrid screening to identify two proteins that interact with CmBBX7: the phosphatase CmPP6 and the E3 ubiquitin ligase CmMIEL1. Ubiquitination assays confirmed that CmMIEL1 promotes the degradation of CmBBX7, thereby affecting its protein stability and influencing flowering. Genetic evidence indicated that both CmPP6 and CmMIEL1 delay flowering. By interacting with CmBBX7, they indirectly repress the expression of CmFTL1, which is a key flowering gene in chrysanthemum. Our findings provide preliminary evidence that ubiquitin modification affects the stability of the CmBBX7 protein and thereby regulates flowering in chrysanthemum.

Chrysanthemum & times;morifolium Ramat., a polyploid ornamental plant of the genus Chrysanthemum in the family Asteraceae, possesses ornamental, medicinal and economic values. As segmental allopolyploid hexaploids, cultivated Chrysanthemum s possess an enormous and highly heterozygous genome, which has long been a major obstacle to genetic dissection and targeted varietal improvement. Benefiting from the rapid evolution of next-generation sequencing and long-read sequencing technologies, multi-omics research on the genus Chrysanthemum has witnessed tremendous progress over recent years. The rapid breakthroughs in genomics technologies have propelled Chrysanthemum research into the era of molecular precision, with remarkable achievements made in genome sequencing, functional gene mining, gene editing and molecular marker development. To date, high-quality chromosome-scale genome assemblies have been completed for a wide range of Chrysanthemum species, including diploid wild relatives, aromatic varieties, medicinal germplasms and commercial hexaploid cultivars. These genomic resources reveal the evolutionary characteristics of polyploidization, genome expansion and structural variation within the genus, and lay a fundamental framework for functional gene mining and molecular mechanism analysis. Integrated multi-omics strategies combining transcriptomics, metabolomics, proteomics, epigenomics and single-cell transcriptomics have been widely adopted to dissect complex agronomic traits. A large number of core genes and regulatory modules related to flower color, flower shape, flowering time, plant architecture, biotic and abiotic stress tolerance, postharvest senescence and biosynthesis of active ingredients have been identified and functionally verified. These findings clarify the molecular regulatory networks of key traits and provide abundant candidate genes for precise genetic improvement. Currently, a comprehensive molecular breeding system has been established for Chrysanthemum. Transgenic technology enables directional modification of important traits such as ornamental and stress-related traits by regulating critical metabolic and signaling pathways. Optimized CRISPR/Cas and epigenetic editing systems support accurate manipulation of endogenous genes, overcoming the limitations of conventional breeding methods. Meanwhile, a full range of molecular markers, from traditional PCR-based markers to high-throughput SNP, InDel and SV markers, have been widely applied in germplasm identification, genetic map construction, QTL mapping and genome-wide association studies. Genomic selection has also shown prominent application value in predicting complex quantitative traits such as plant height and flowering time, displaying broad application potential in practical breeding programs in Chrysanthemum. Despite substantial progress, multiple bottlenecks still restrict the large-scale application of Chrysanthemum molecular breeding. The complex polyploid genetic background leads to widespread gene redundancy and functional differentiation of homologous genes. Additionally, genotype-dependent transformation systems, ambiguous allelic dosage effects and high costs of high-throughput genotyping remain major obstacles. To address these issues, future research will prioritize constructing high-quality pan-genomes and haplotype maps, developing efficient multi-gene editing tools and novel genotype-independent delivery systems. It is also essential to build low-cost high-throughput genotyping platforms and intelligent multi-omics databases for data sharing and deep mining. Collectively, these technological advancements will fully promote the transformation of Chrysanthemum breeding from conventional breeding to modern precise and intelligent molecular design breeding in the future.

Global climate change and human activities are posing substantial threats to biodiversity. The genus Opisthopappus (O. taihangensis and O. longilobus), endemic to Taihang Mountains in china, possesses great ornamental and medicinal value. However, it is confronted with the compounding pressures of habitat fragmentation and escalating climate change. Here, we present the first haplotype-resolved, chromosome-scale genome assembly of O. longilobus (~2.95 Gb, ~58,000 protein-coding genes per haplotype) and re-sequence 115 individuals across its range. Comparative analyses show Opisthopappus is sister to Artemisia-Chrysanthemum, with Opisthopappus and Chrysanthemum diverging at 5.15-5.18 million years ago. A profound genetic divergence is evident between O. taihangensis and O. longilobus, resulting in two distinct lineages within each species, driven by geography and climate. Our analyses indicate restricted gene flow, low diversity, and recurrent demographic bottlenecks collectively contribute to their endangerment. By integrating population genomics and environmental variables, we identified 4,620 core adaptive loci and 4,437 core adaptive genes linked to water deprivation, hormone regulation, and metabolism. Genomic offset predicts higher maladaptation risk in populations under drastic climate change. Furthermore, metabolomic and experimental data demonstrate that diverged promoters of two O-methyltransferase genes, OMT250 and OMT310, account for the differential acacetin/linarin accumulation between C. morifolium and O. longilobus. These findings advance understanding of evolution and climate vulnerability of Opisthopappus, offering a model for genomics-guided biodiversity conservation. ### Competing Interest Statement The authors have declared no competing interest. National Key Research and Development Program of China, 2021YFD1200200

Garden chrysanthemums (Chrysanthemum x morifolium Ramat.) are widely appreciated for their rich diversity in color, form, and blooming period. However, traditional evaluations of ornamental traits rely on subjective and labor-intensive methods, limiting efficiency and consistency. To address this, we developed an UAV-based phenotyping framework to objectively assess ornamental traits in chrysanthemums. Using the UAV-based RGB imaging platform, we evaluated forty cultivars and extracted eleven ornamental traits, including plant height, canopy orthophoto area, canopy roundness, clump cohesion, ornamental duration, flowering rarity grade, flowering efficiency index, visual uniformity, flower color coverage, color saturation, and color value. Principal component analysis (PCA) reduced trait dimensionality, with four principal components explaining 77.94 % of total variance. K-means clustering grouped cultivars into three performance categories (excellent, moderate, poor), supported by hierarchical clustering. Color group analysis showed that pink-purple cultivars exhibited the greatest color diversity, while red cultivars were more uniform. Traits associated with color and texture (e.g., color coverage, saturation, visual uniformity) and floral duration (e.g., efficiency index, ornamental duration) were most influential in cultivar differentiation. Twelve cultivars, such as 'Jinling Guili', were identified as excellent based on their visual appeal and landscape suitability. Our results demonstrate that UAV-based phenotyping provides an efficient, objective, and scalable method for cultivar evaluation. The proposed framework identifies color-texture and floral duration as core drivers and offers valuable tools for chrysanthemum breeding and landscape application. More broadly, this study demonstrates the potential of integrating remote sensing and multivariate analysis to modernize ornamental plant evaluation across diverse germplasm resources.

Chrysanthemum, a globally renowned economic crop, primarily relies on vegetative propagation methods such as cutting for commercial cultivation. However, certain varieties with exceptional ornamental qualities often encounter difficulties in widespread adoption due to poor rooting ability and suboptimal root quality. The genetic underpinnings of rooting ability in chrysanthemum cuttings have remained largely unexplored. This study marks a significant advancement in this field. By evaluating 11 rooting traits across a diverse panel of 188 chrysanthemum genotypes, we found that spray cut chrysanthemums exhibit superior rooting ability compared to other cultivated types and wild species. Selective sweep analysis identified 534 selected genomic regions potentially linked to rooting traits during the domestication and improvement of chrysanthemums. Genome-wide association studies (GWAS) conducted on four key rooting traits - total root length, root surface area, average root diameter, and number of roots, using multiple models discovered 71 significant SNPs and 98 candidate genes, including 21 differentially expressed genes identified via transcriptomic sequencing. A weighted gene co-expression network analysis further revealed two key modules (yellow and lightyellow) related to rooting traits. By integrating GWAS, transcriptomic data, and functional verification, we pinpointed the candidate gene CmNRAMP3 as a negative regulator of rooting ability. These findings substantially enrich our understanding of the genetic mechanisms underlying rooting ability in chrysanthemum cuttings and provide a promising gene pool for improving rooting traits in future breeding programs.

Monitoring the flowering period is essential for evaluating garden chrysanthemum cultivars and their landscaping use. However, traditional field observation methods are labor-intensive. This study proposes a classification method based on color information from canopy digital images. In this study, an unmanned aerial vehicle (UAV) with a red-green-blue (RGB) sensor was utilized to capture orthophotos of garden chrysanthemums. A mask region-convolutional neural network (Mask R-CNN) was employed to remove field backgrounds and categorize growth stages into vegetative, bud, and flowering periods. Images were then converted to the hue-saturation-value (HSV) color space to calculate eight color indices: R_ratio, Y_ratio, G_ratio, Pink_ratio, Purple_ratio, W_ratio, D_ratio, and Fsum_ratio, representing various color proportions. A color ratio decision tree and random forest model were developed to further subdivide the flowering period into initial, peak, and late periods. The results showed that the random forest model performed better with F1-scores of 0.9040 and 0.8697 on two validation datasets, requiring less manual involvement. This method provides a rapid and detailed assessment of flowering periods, aiding in the evaluation of new chrysanthemum cultivars.