Frontiers in Plant Science最新文献

Pine wilt disease, often referred to as the "cancer of pine trees," is characterized by its rapid spread and extremely high mortality rate, posing a severe threat to forest ecosystems. Currently, most automatic identification methods for pine wilt disease based on UAV remote sensing imagery rely on a single architecture of Convolutional Neural Networks (CNNs) or Transformer, which suffer from limitations such as restricted receptive fields, insufficient global context modeling, and loss of local details. Existing fusion strategies typically adopt simple stacking or parallel designs without an effective hierarchical feature interaction mechanism, resulting in inadequate integration of semantic and detailed information, as well as high computational overhead, which hinders their deployment in edge computing environments. To address these issues, this study proposes PGCNet, a semantic segmentation model that efficiently fuses CNN and Transformer representations. The model employs CSWin Transformer as the backbone network to capture comprehensive global contextual information. A Progressive Guidance Fusion Module (PGFM) is designed to achieve effective cross-layer fusion of semantic and detailed features through a spatial-channel collaborative attention mechanism. Furthermore, a lightweight Context-Aware Residual Atrous Spatial Pyramid Pooling module (CAR-ASPP) is introduced to enhance multi-scale feature representation while significantly reducing the number of parameters and computational complexity. Experimental results demonstrate that PGCNet outperforms mainstream semantic segmentation models across multiple evaluation metrics, showing especially strong performance in scenarios with complex background interference and small-scale disease target identification. The proposed model achieves high accuracy with excellent computational efficiency, offering a practical solution for real-time monitoring and edge deployment of forestry disease detection, and exhibiting strong potential for extension to agricultural remote sensing disease identification tasks.

Wild rice species serve as a valuable genetic reservoir for rice improvement, and the DNA segments containing useful genes/alleles can be transferred to elite rice varieties through crosses. However, DNA markers, which are essential tools for genetic analysis and molecular breeding, are not yet well-established for harnessing wild rice species. To enable an efficient utilization of the CC-genome wild rice species in the genus Oryza, we developed a genome-wide polymorphic InDel marker (≥20 bp) set comprising 182 markers through positional sequence alignments between O. officinalis (CC-genome) and rice cultivars (AA-genome). These markers were evenly distributed across the 12 chromosomes, with ~2Mb marker intervals. For validation of marker polymorphism, all the markers were tested by using PCR-agarose gel analysis with 12 accessions of CC-genome species (four accessions each from O. eichingeri, O. officinalis, and O. rhizomatis), two accessions of BBCC-genome species (O. minuta), and two cultivars, Nipponbare (japonica) and IR24 (indica). Out of 182 markers, 172 markers (94.5%) successfully amplified and exhibited polymorphism between rice and the CC-genome accessions, corresponding to an average marker interval of ~2.17Mb across the rice genome. Moreover, the marker set also showed high polymorphism (84.1-92.9%) when applied to BBCC-genome species. Based on the marker validation data, five markers were selected for species identification within the three CC-genome species. In addition, the polymorphic markers successfully detected wild rice introgressions from the wide hybridization progenies. The newly developed marker set will function as valuable genomic tools for harnessing CC-genome and CC-genome containing germplasm for rice improvement.

Calcium-dependent protein kinase (CPK) gene family, which can be activated directly by Ca²⁺, plays an important role in Ca²⁺ signal transduction and stress response and is widely present in green plants. So far, the role of CPK gene family evolution for species in karst area is far from understanding. Hemiboea subcapitata (Gesneriaceae) was used to explore this issue, for its mainly distributing in the karst area of south China. Our results indicated that 32 highly conserved CPK genes identified were distributed across 14 chromosomes in H. subcapitata. Additionally, 10 gene pairs were generated by fragment replication. Subcellular localization analysis revealed that HsCPKs were mainly localized in chloroplasts and cytoplasm. This gene family experienced intron loss events, but its motif structure was highly similar. Phylogenetic analysis showed that the HsCPKs were divided into four subfamilies. Subfamilies I and II were under neutral selection, while subfamilies III and IV were under strongly positive selection. The HsCPKs showed different expressions in three vegetative organs of H. subcapitata. Meanwhile, the expression levels under calcium stress revealed an overall increasing trend for all HsCPKs examined. Cis-acting elements analysis revealed that HsCPKs contained hormone-responsive elements related to stress. The expansion and evolution of CPK gene family in H. subcapitata may be related to its adaptation to calcium-rich and stressed habitats. This study provides a valuable understanding for the roles of the CPK gene family within karst species.

Introduction: Plant disease segmentation in real-world agricultural environments poses significant technical challenges, including complex backgrounds, diverse lesion morphologies, and extreme class imbalance.

Methods: In this paper, we propose an integrated solution, STAR-Net, which combines a novel network architecture with a dynamic training strategy. The architecture features an innovative Heterogeneous Branch Attention Aggregation (HBAA) module to robustly represent multi-scale and multi-morphology features. The training strategy employs a Dynamic Phase-Weighted Loss (DPW-Loss) to navigate the complexities of imbalanced data.

Results: Our method achieves a state-of-the-art average mIoU of 93.36% on the NLB dataset. This result demonstrates its superior ability to precisely segment diseases with specific elongated morphologies. Furthermore, the model obtains a competitive average mIoU of 41.13% on the highly challenging PlantSeg dataset. This result validates its robustness in complex 'in-the-wild' scenarios.

Discussion: Our work presents a powerful, well validated, and synergistic solution for plant disease segmentation. It also paves the way for practical applications in precision agriculture.

As land salinization intensifies across the United States, there is growing interest in cultivating salt-tolerant crops such as olives (Olea europaea). With extra virgin olive oil becoming an increasingly popular and valuable commodity in the U.S. market, growing olive cultivars that can thrive under challenging conditions is critical. This study evaluated the physiological, antioxidative and biochemical responses of two relatively newly introduced olive cultivars, 'Oliana' and 'Lecciana', to salinity stress under controlled greenhouse conditions. Eight-month-old plants were subjected to three salinity treatments (0, 50, and 100 mM NaCl) in a completely randomized design (n = 9). Plant gas exchange parameters were measured at 0, 15, 30, and 45 days after treatment, while chlorophyll content, fluorescence (Fv/Fm), biomass, nutrient accumulation (in leaves, stems, and roots), antioxidant enzyme activity, osmolyte levels, and reactive oxygen species (ROS) concentrations were assessed at 15, 30, and 45 days. Salinity stress significantly reduced gas exchange parameters in 'Lecciana' cultivar as compared to 'Oliana', resulting in decreased chlorophylls, Fv/Fm, and nutrient content. In response, antioxidant enzymes and osmolyte accumulation (proline and glycine betaine) increased with 'Lecciana' showing the strongest response. Conversely, lipid peroxidation and hydrogen peroxide (H₂O₂) levels were highest in 'Oliana' under 100 mM NaCl, indicating greater oxidative stress. These findings suggest that 'Lecciana's high salinity tolerance is associated with enhanced antioxidant defense and compatible solutes. In addition, 'Lecciana' and 'Oliana' could be a promising cultivar for saline and drought-prone regions of the U.S., supporting the expanding production of high-quality extra virgin olive oil.

Far-red light (FR, 700-800 nm) can enhance photosynthesis by stimulating photosystem I (PSI). However, during chlorophyll fluorescence (CF) measurements using pulse-amplitude modulation (PAM) fluorometry, unusually high quantum yields of photosystem II (Φ _PSII) have been observed under high FR light intensities, raising concerns about measurement artifacts. To test this, we constructed light response curves for sweet basil (Ocimum basilicum L.) grown under light-emitting diode (LED) light (R:G:B = 44%:18%:38%) with varying photosynthetic photon flux densities (PPFD, 0-1,000 μmol m^-2 s^-1) and FR fractions (0, 0.26, 0.45, and 0.63). FR treatments consistently increased Φ _PSII, but when total photon flux density (TPFD, 400-800 nm) exceeded 1,000 μmol m^-2 s^-1, Φ _PSII rose abruptly. Nonfluorescent reference tests using white and black paper confirmed that FR induced spurious fluorescence signals, likely due to spectral overlap between FR photons and the PAM detection range (680-760 nm). Tilting the LED panel to reduce reflected FR eliminated the abrupt Φ _PSII peak but introduced unexpectedly increased Φ _PSII across treatments, likely due to probe-induced shading. These findings demonstrate that high-intensity FR can confound PAM-based CF measurements by producing spurious signals unrelated to plant physiology. Accurate and reliable assessment of photosynthetic performance under extended spectral lighting conditions requires careful management of lighting geometry and FR intensity.

Bowman-Birk protease inhibitors (BBIs) are multifunctional proteins with a double-headed structure, featuring two distinct inhibitory loops that target trypsin and chymotrypsin proteases. BBI regulates protease activity in plants and provides defense against pests and pathogens, but little is still known about their expression levels and their ability to interact with natural targets. Our results showed that BBI1 and BBI2 genes are the most highly expressed in Vigna seeds. Consequently, we produced two multiple sequence alignments including homologs from 42 Vigna taxa to explore variability and functionality. Phylogenetic relationships, signals of positive selection, and interaction energy levels with their natural targets were inferred. Overall, BBI2 exhibited the highest affinity for the assessed targets compared to BBI1. Amino acid substitutions have led to distinct protein variants across species, each displaying different interaction capacities with their respective targets. Additionally, the residue conferring inhibitory specificity for trypsin, located in the first domain, was found to be under positive selection in both genes. This suggests an ongoing evolutionary process aimed at optimizing affinity with proteases through continuous adaptation. Finally we emphasize that findings obtained can be used to drive the activity of plant breeders and more efficient cultivars can be selected. Given the growing availability of genomic information of wild and domesticated accessions, docking simulations offer a convenient and effective method to preliminarily assess new protein variants.

Introduction: Complex traits arise from polygenic and interactive genomic architectures that are difficult to resolve using traditional genome-wide association study (GWAS) approaches. Machine learning (ML) provides complementary methods capable of capturing non-linear effects, improving signal detection, and enhancing predictive accuracy of marker trait associations (MTAs).

Methods: Using a publicly available winter wheat dataset (CIMMYT), we evaluated several widely used traditional GWAS tools, including GAPIT, GCTA, GEMMA, sommer, and TASSEL, with respect to computational efficiency, model performance, and the consistency of detected associations. In parallel, ML approaches, such as Elastic Net, Extreme Gradient Boosting (XGBoost), Random Forest, and the hybrid TSLRF model, were assessed based on feature importance metrics and functional annotation of selected markers.

Results: Despite a shared reliance on mixed linear models, the traditional GWAS tools exhibited differences in runtime and showed modest but meaningful variability in the number and overlap of MTAs. ML models recovered several associations detected by traditional methods and additionally identified novel markers, potentially reflecting non-linear or epistatic effects.

Discussion: Our findings demonstrate that ML can effectively complement traditional GWAS approaches for marker-trait identification in wheat. By extending beyond additive effects, ML broadens the scope of detectable genetic signals, providing a practical way to analyze complex traits and support informed marker-assisted breeding strategies.

Ferruginous outcrops are ecologically formations that host high biodiversity and edaphic endemism. While canga outcrops in Carajás have been the focus of more extensive research, ferruginous outcrops in the Araguaia remain poorly studied, especially with respect to their functional ecology and conservation value. We evaluated the soils, floristic and functional compositions of plant communities on ferruginous outcrops in Carajás and the Araguaia, with the goals of comparing edaphic conditions, floristic compositions, and functional strategies between these disjunct regions and identifying patterns relevant for biodiversity conservation. A total of 129 plots were sampled spanning grassland (GS), shrubland (SB), and woodland (WD) formations. In all plots, soil samples were collected, and plant traits related to resource acquisition (SLA, leaf N, N:P), reproductive strategies (fruit dimensions), and interaction modes (dispersal and pollination syndromes) were measured. Herbaceous and woody communities (trees and treelets with dbh ≥= 3 cm) were analyzed separately. Functional similarity was assessed via community-weighted means and multivariate trait space analyses. Despite exhibiting moderate floristic similarity between regions and edaphic differences, both regions share acidic soils with low phosphorus (P) availability, a condition that imposes similar constraints on resource acquisition. Open formations (GS, SB) in both regions showed functional convergence, indicating similar environmental filters. In contrast, woody communities, especially those in WD, presented pronounced differences in trait composition, reflecting differences in local conditions and ecological history. This study highlights the complementary conservation value of ferruginous outcrops in Carajás and the Araguaia. The functional similarities in open formations suggest that these environments may exhibit ecological strategies associated with similar environmental conditions. Recognizing and protecting these unique environments is essential to ensure their long-term ecological resilience.

Introduction: Achillea fragrantissima is valued for its broad spectrum of secondary metabo-lites with notable therapeutic potential. This study examined the influence of an ethanolic extract of Commiphora myrrha gum-resin on in vitro shoot growth, callus formation, and the effect of compound composition in A. fragrantissima.

Material and methods: Shoot-tip and root-segment explants were cultured on Murashi-ge and Skoog (MS) medium supplemented with filter-sterilized C. myrrha ethanolic extract at 0.5, 1.0, and 2.0 g·L^-1, alongside control treatment. Plant responses, including growth parameters, callus induction percentage and biomass, photosynthetic pigments, and me-tabolite composition, were assessed after one month of in vitro culture. GC-MS analysis was performed on the C. myrrha extract and on methanolic extracts of A. fragrantissima plantlets and callus tissues obtained from different treatments.

Results: GC-MS analysis of the ethanolic extract of C. myrrha identified 66 phytochemi-cal compounds dominated by sesquiterpenes-particularly 2,5,8-trimethyl-1-nonen-3-yn-5-ol, curzerene, myrcenol, germa-cra-1(10),4,11(13)-trien-12-oic acid, and several oxygenated sesquiterpenes-indicating a rich pool of bioactive, growth-promoting constituents. Shoot-tip and root-segment ex-plants showed the strongest responses to C. myrrha extract at specific concentrations. The highest concentration tested (2.0 g·L^-1) produced the most shoots and the greatest accu-mulation of photosynthetic pigments, including total chlorophyll and carotenoids. In contrast, the lowest concentration (0.5 g·L^-1) promoted the longest shoots, the highest fresh weight, and the greatest number of leaves. Callus formation from shoot tips was also highest at 0.5 g•L^-1, while root-derived callus reached full induction at both 0.5 and 2.0 g·L^-1. Both shoot-tip plantlets and root-derived callus showed notable modifications in their bioactive constituents. Several bioactive constituents were notably elevated (e.g., de-sulphosinigrin, thymidine, 4H-pyran-4-one derivatives, and fatty acid derivatives).

Discussion: Distinct tissue-specific patterns emerged, shoot tips accumulated a wider range of aromatic and therapeutic compounds, whereas callus tissue was enriched in fat-ty acids. These findings suggest that C. myrrha extract functions as a natural biostimulant and elicitor, providing a sustainable approach for producing metabolite-rich A. fragrantis-sima material with potential pharmacological applications.