Search Results (4,006)

Fritillaria unibracteata is a rare and endangered medicinal plant in the Liliaceae family, whose bulbs have been used in traditional Chinese traditional medicine for over 2000 years. The mevalonate (MVA) pathway is involved in the growth, development, response to environmental stress, and active ingredient production of plants; however, the functional characterization of MVA-pathway genes in the Liliaceae family remains poorly documented. In this study, an Acetyl-CoA C-acetyltransferase gene (FuAACT) was first cloned from F. unibracteata. It exhibited structural features of the thiolase family and showed the highest sequence identity with the Dioscorea cayenensis homolog. The K_m, V_max, and K_cat of the recombinant FuAACT were determined to be 3.035 ± 0.215 μM, 0.128 ± 0.0058 μmol/(min·mg), and 1.275 ± 0.0575 min⁻¹, respectively. The optimal catalytic conditions for FuAACT were ascertained to be 30 °C and pH 8.9. It was stable below 50 °C. His361 was confirmed to be a key amino acid residue to enzymatic catalysis by site-directed mutagenesis. Subsequent subcellular localization experiments demonstrated that FuAACT was localized in chloroplasts and cytoplasm. FuAACT-overexpressing transgenic Arabidopsis thaliana plants showed higher drought tolerance than wild-type plants. This phenotypic difference was corroborated by significant differences in seed germination rate, lateral root number, plant height, and leaf number (p < 0.05). Furthermore, the FuAACT transgenic plants resulted in the formation of a more developed fibrous root system. These results indicated that the FuAACT gene revealed substantial biological activity in vitro and in vivo, hopefully providing the basis for its further research and application in liliaceous ornamental and medicinal plants. Full article

In the context of increasing demand for sustainable floriculture, this study evaluated the effects of salicylic acid (SA) on phenotypic traits of poinsettia (Euphorbia pulcherrima Willd.). A factorial experiment was conducted in a commercial glasshouse using rooted poinsettia cuttings treated with three SA concentrations (10⁻³, 10⁻⁴, 10⁻⁵ M) applied via foliar or root application. Morphological parameters, colorimetric traits (CIELAB), canopy development, and biomass accumulation were assessed throughout the cultivation cycle. SA had no significant influence on the plant height, leaf number, or biomass of stems, leaves, and roots. However, notable phenotypic changes were observed. Foliar applications, particularly at 10⁻⁵ M, induced visible changes in leaf and bract color, including reduced brightness, saturation, and red pigmentation, especially in newly developed tissues. Conversely, root applications had milder effects and were generally associated with a more stable bract color. The 10⁻⁴ M root treatment promoted greater bract surface and color saturation. Canopy expansion and dry matter accumulation were also influenced by SA in a dose- and method-dependent manner, with high-dose foliar treatments (10⁻³ M) exerting suppressive effects. These findings suggest that the application mode and concentration of SA are critical in modulating ornamental quality traits, with low-to-moderate doses—particularly via root application—offering promising strategies to enhance plant performance in sustainable poinsettia cultivation. Full article

To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction. Full article

The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science. Full article

Oxybenzone (OBZ), an organic ultraviolet filter, is an emerging contaminant posing severe threats to ecosystem health. Using tobacco (Nicotiana tabacum) as a model plant, this study investigated the alleviation mechanisms of exogenous silicon (Na₂SiO₃, Si) and bamboo-based biochar (Bc) under OBZ stress. We systematically analyzed physiological and biochemical responses, including phenotypic parameters, reactive oxygen species metabolism, photosynthetic function, chlorophyll synthesis, and endogenous hormone levels. Results reveal that OBZ significantly inhibited tobacco growth and triggered a reactive oxygen species (ROS) burst. Additionally, OBZ disrupted antioxidant enzyme activities and hormonal balance. Exogenous Bc mitigated OBZ toxicity by adsorbing OBZ, directly scavenging ROS, and restoring the ascorbate-glutathione (AsA-GSH) cycle, thereby enhancing photosynthetic efficiency, while Si alleviated stress via cell wall silicification, preferential regulation of root development and hormonal signaling, and repair of chlorophyll biosynthesis precursor metabolism and PSII function. The mechanisms of the two stress mitigators were complementary, Bc primarily relied on physical adsorption and ROS scavenging, whereas Si emphasized metabolic regulation and structural reinforcement. These findings provide practical strategies for simultaneously mitigating organic UV filter pollution and enhancing plant resilience in contaminated soils. Full article

Members of the heat shock protein 20 (HSP20) family of proteins play an important role in responding to various forms of stress. Here, the expression of ClaHSP17.4 was induced by heat stress in watermelon. Then, a floral dipping approach was used to introduce the pCAMBIA1391b-GFP overexpression vector encoding the heat tolerance-related gene ClaHSP17.4 from watermelon into Arabidopsis thaliana, and we obtained ClaHSP17.4-overexpressing Arabidopsis plants. Under normal conditions, the phenotypes of transgenic and wild-type (WT) Arabidopsis plants were largely similar. Following exposure to heat stress, however, the germination rates (96%) of transgenic Arabidopsis plants at the germination stages were significantly higher than those of wild-type idopsis (17%). Specifically, the malondialdehyde (MDA) content of transgenic Arabidopsis was half that of the control group, while the activities of peroxidase (POD) and superoxide dismutase (SOD) were 1.25 times those of the control group after exposure to high temperatures for 12 h at the seedling stages. The proline content in ClaHSP17.4-overexpressing transgenic Arabidopsis increased by 17% compared to WT plants (* p < 0.05), while the soluble sugar content rose by 37% (* p < 0.05). These results suggest that ClaHSP17.4 overexpression indirectly improves the antioxidant capacity and osmotic regulatory capacity of Arabidopsis seedlings, leading to improved survival and greater heat tolerance. Meanwhile, the results of this study provide a reference for further research on the function of the ClHSP17.4 gene and lay a foundation for breeding heat-tolerant watermelon varieties and advancing our understanding of plant adaptation to environmental stress. Full article

This study measured eight key phenotypic traits across 259 fresh maize inbred lines, including plant height and spike length. A total of 82 single nucleotide polymorphisms (SNPs) significantly associated with these phenotypes were identified by applying a mixed linear model to calculate the best linear unbiased prediction (BLUP) values and integrating genome-wide genotypic data through genome-wide association analysis (GWAS). A further analysis of significant SNPs contributed to the identification of 63 candidate genes with functional annotations. Notably, 11 major candidate genes were identified from multi-trait association loci, all of which exhibited highly significant P-values (<0.0001) and explained between 7.21% and 12.78% of phenotypic variation. These 11 genes, located on chromosomes 1, 3, 4, 5, 6, and 9, were functionally involved in signaling, metabolic regulation, structural maintenance, and stress response, and are likely to play crucial roles in the growth and physiological processes of fresh maize inbred lines. The functional genes identified in this study have significant implications for the development of molecular markers, the optimization of breeding strategies, and the enhancement of quality in fresh maize. Full article

To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based on the Structure-from-Motion Multi-View Stereo (SFM-MVS) algorithms, a high-precision three-dimensional point cloud model was reconstructed from multi-view RGB image sequences, and 12 phenotypic parameters, such as plant height, crown width, were accurately extracted. Through regression analyses of plant height, crown width, and crown height, and the R² values were 0.98, 0.99, and 0.99, respectively, the RMSE values were 2.26 mm, 1.74 mm, and 1.69 mm, respectively. On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an R² of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. This research achieves three-dimensional reconstruction and accurate biomass prediction of facility lettuce, and provides a portable and lightweight solution for facility crop growth detection. Full article

The red plant phenotype of R1 cotton is a genetic marker produced by light-induced anthocyanin accumulation. GhPAP1D controls this trait. There are two 228 bp tandem repeats upstream of GhPAP1D in R1 cotton. In this study, GUS staining assays in transgenic Arabidopsis thaliana (L.) Heynh. demonstrated that tandem repeats in the GhPAP1D promoter-enhanced transcriptional activity. GhPAP1D is a homolog of A. thaliana AtPAP1. AtPAP1’s expression is regulated by photomorphogenesis-related transcription factors such as AtHY5 and AtBBXs. We identified the homologs of A. thaliana AtHY5, AtBBX21, and AtBBX24 in R1 cotton, designated as GhHY5, GhBBX21, and GhBBX24, respectively. Y1H assays confirmed that GhHY5, GhBBX21, and GhBBX24 each bound to the GhPAP1D promoter. Dual-luciferase reporter assays revealed that GhHY5 weakly activated the promoter activity of GhPAP1D. Heterologous expression assays in A. thaliana indicated that GhBBX21 promoted anthocyanin accumulation, whereas GhBBX24 had the opposite effect. Dual-luciferase assays showed GhBBX21 activated GhPAP1D transcription, while GhBBX24 repressed it. Further study indicated that GhHY5 did not enhance GhBBX21-mediated transcriptional activation of GhPAP1D but alleviates GhBBX24-induced repression. Together, our results demonstrate that GhBBX21 and GhBBX24 antagonistically regulate anthocyanin accumulation in R1 cotton under GhHY5 mediation, providing insights into light-responsive anthocyanin biosynthesis in cotton. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

With the objective of identifying superior Vernicia montana trees grounded in phenotypic and agronomic traits, this study sought to develop and implement a comprehensive evaluation method which would provide a practical foundation for future clonal breeding initiatives. Using the Vernicia montana propagated from seedling forests grown in the Suxian District of Chenzhou City in southern Hunan Province, we conducted pre-selection, primary selection, and re-selection of Vernicia montana forest stands and took the nine trait indices of single-plant fruiting quantity, single-plant fruit yield, disease and pest resistance, fruit ripening consistency, fruit aggregation, fresh fruit single-fruit weight, fresh fruit seed rate, dry seed kernel rate, and seed kernel oil content rate as the optimal evaluation indexes and carried out cluster analysis and a comprehensive evaluation in order to establish a comprehensive evaluation system for superior Vernicia montana trees. The results demonstrated that a three-stage selection process—consisting of pre-selection, primary selection, and re-selection—was conducted using a comprehensive analytical approach. The pre-selection phase relied primarily on sensory evaluation criteria, including fruit count per plant, tree size, tree morphology, and fruit clustering characteristics. Through this rigorous screening process, 60 elite plants were selected. The primary selection was based on phenotypic traits, including single-plant fruit yield, pest and disease resistance, and uniformity of fruit ripening. From this stage, 36 plants were selected. Twenty plants were then selected for re-selection based on key performance indicators, such as fresh fruit weight, fresh fruit seed yield, dry seed kernel yield, and oil content of the seed kernel. Then the re-selected optimal trees were clustered and analyzed into three classes, with 10 plants in class I, 7 plants in class II, and 3 plants in class III. In class I, the top three superior plants exhibited outstanding performance across key traits: their fresh fruit weight per fruit, fresh fruit seed yield, dry seed yield, and seed kernel oil content reached 41.61 g, 42.80%, 62.42%, and 57.72%, respectively. Compared with other groups, these figures showed significant advantages: 1.17, 1.09, 1.12, and 1.02 times the average values of the 20 reselected superior trees; 1.22, 1.19, 1.20, and 1.08 times those of the 36 primary-selected superior trees; and 1.24, 1.25, 1.26, and 1.19 times those of the 60 pre-selected trees. Fruits counts per plant and the number of fruits produced per plant of the best three plants in class I were 885 and 23.38 kg, respectively, which were 1.13 and 1.18 times higher than the average of 20 re-selected superior trees, 1.25 and 1.30 times higher than the average of 36 first-selected superior trees, and 1.51 and 1.58 times higher than the average of 60 pre-selected superior trees. Class I superior trees, especially the top three genotypes, are suitable for use as mother trees for scion collection in grafting. The findings of this study provide a crucial foundation for developing superior clonal varieties of Vernicia montana through selective breeding. Full article

Sorghum is an important cereal crop. The maintenance of leaf color significantly influences sorghum growth and development. Although the mechanisms of leaf color mutation have been well studied in many plants, those in sorghum remain largely unclear. Here, we identified a sorghum gradient-pale-green leaf mutant (sbgpgl1) from the ethyl methanesulfonate (EMS) mutagenesis mutant library. Phenotypic, photosynthesis-related parameter, ion content, transcriptome, and metabolome analyses were performed on wild-type BTx623 and the sbgpgl1 mutant at the heading stage, revealing changes in several agronomic traits and physiological indicators. Compared with BTx623, sbgpgl1 showed less height, with a smaller length and width of leaf and panicle. The overall Chl a and Chl b contents in sbgpgl1 were lower than those in BTx623. The net photosynthetic rate, stomatal conductance, and transpiration rate were significantly reduced in sbgpgl1 compared to BTx623. The content of copper (Cu), zinc (Zn), and manganese (Mn) was considerably lower in sbgpgl1 leaves than in BTx623. A total of 4469 differentially expressed genes (DEGs) and 775 differentially accumulated metabolites (DAMs) were identified by RNA-seq and UPLC-MS/MS. The results showed that sbgpgl1 primarily influenced sorghum metabolism by regulating metabolic pathways and the biosynthesis of secondary metabolites, especially flavonoids and phenolic acids, resulting in the gradient-pale-green leaf phenotype. These findings reveal key genes and metabolites involved on a molecular basis in physiological variations of the sorghum leaf color mutant. Full article

Sequential drought and heat stress pose a growing threat to forest ecosystems in the context of climate change, yet systematic evaluation methods for woody plants remain limited. This study aimed to develop a comprehensive screening platform for identifying woody plant species tolerant to sequential drought and heat stress among 27 native species growing in Korea. A sequential stress protocol was applied: drought stress for 2 weeks, followed by high-temperature exposure at 45 °C. Physiological indicators, including relative water content (RWC) and electrolyte leakage index (ELI), were used for preliminary screening, supported by phenotypic observations, Evans blue staining for cell death, and DAB staining to assess oxidative stress and recovery ability. The results revealed clear differences among species. Chamaecyparis obtusa, Quercus glauca, and Q. myrsinaefolia exhibited strong tolerance, maintaining high RWC and low ELI values, while Albizia julibrissin was highly susceptible, showing severe membrane damage and low survival. DAB staining successfully distinguished tolerance levels based on oxidative recovery. Additional species such as Camellia sinensis, Q. acuta, Q. phillyraeoides, Q. salicina, and Ternstroemia japonica showed varied responses: Q. phillyraeoides demonstrated high tolerance, T. japonica showed moderate tolerance, and Q. salicina was relatively sensitive. The integrated screening system effectively differentiated tolerant species through multiscale analysis—physiological, cellular, and morphological—demonstrating its robustness and applicability. This study provides a practical and reproducible framework for evaluating sequential drought and heat stress in trees and offers valuable resources for urban forestry, reforestation, and climate-resilient species selection. Full article

Accurate organ-level segmentation is essential for achieving high-throughput, non-destructive, and automated plant phenotyping. To address the challenge of intelligent acquisition of phenotypic parameters in tomato plants, we propose TSINet, an end-to-end dual-task segmentation network designed for effective and precise semantic labeling and instance recognition of tomato point clouds, based on the Pheno4D dataset. TSINet adopts an encoder–decoder architecture, where a shared encoder incorporates four Geometry-Aware Adaptive Feature Extraction Blocks (GAFEBs) to effectively capture local structures and geometric relationships in raw point clouds. Two parallel decoder branches are employed to independently decode shared high-level features for the respective segmentation tasks. Additionally, a Dual Attention-Based Feature Enhancement Module (DAFEM) is introduced to further enrich feature representations. The experimental results demonstrate that TSINet achieves superior performance in both semantic and instance segmentation, particularly excelling in challenging categories such as stems and large-scale instances. Specifically, TSINet achieves 97.00% mean precision, 96.17% recall, 96.57% F1-score, and 93.43% IoU in semantic segmentation and 81.54% mPrec, 81.69% mRec, 81.60% mCov, and 86.40% mWCov in instance segmentation. Compared with state-of-the-art methods, TSINet achieves balanced improvements across all metrics, significantly reducing false positives and false negatives while enhancing spatial completeness and segmentation accuracy. Furthermore, we conducted ablation studies and generalization tests to systematically validate the effectiveness of each TSINet component and the overall robustness of the model. This study provides an effective technological approach for high-throughput automated phenotyping of tomato plants, contributing to the advancement of intelligent agricultural management. Full article

Quinoa (Chenopodium quinoa Willd.) has emerged as a climate-resilient, nutrient-dense crop with increasing global popularity because of its adaptability under current environmental variations. To address the limited understanding of quinoa’s genotypic performance under local agro-environmental conditions, this study hypothesized that elite genotypes would exhibit significant variation in agronomic, physiological, and biochemical traits. This study aimed to elucidate genotypic variability among 23 elite quinoa lines under field conditions in Faisalabad, Pakistan, using a multidimensional framework that integrated phenological, physiological, biochemical, root developmental, and yield-related attributes. The results revealed that significant variation was observed across all measured parameters, highlighting the diverse adaptive strategies and functional capacities among the tested genotypes. More specifically, genotypes Q4, Q11, Q15, and Q126 demonstrated superior agronomic potential and canopy-level physiological efficiencies, including high biomass accumulation, low infrared canopy temperatures and sustained NDVI values. Moreover, Q9 and Q52 showed enhanced accumulation of antioxidant compounds such as phenolics and anthocyanins, suggesting potential for functional food applications and breeding program for improving these traits in high-yielding varieties. Furthermore, root trait analysis revealed Q15, Q24, and Q82 with well-developed root systems, suggesting efficient resource acquisition and sufficient support for above-ground plant parts. Moreover, principal component analysis further clarified genotype clustering based on trait synergistic effects. These findings support the use of multidimensional phenotyping to identify ideotypes with high yield potential, physiological efficiency and nutritional value. The study provides a foundational basis for quinoa improvement programs targeting climate adaptability and quality enhancement. Full article