Plant Methods最新文献

{"title":"DAPR-AM-Net: an end-to-end smart farming system powered by dual-attention progressive refinement and adaptive MixUp for explainable tomato leaf disease classification and forecasting.","authors":"Ran Wang, Xiao Yu, Lina Lu, Cong Chen","doi":"10.1186/s13007-026-01556-z","DOIUrl":"10.1186/s13007-026-01556-z","url":null,"abstract":"<p><p>Vision-based crop disease diagnosis plays a pivotal role in smart agriculture, yet challenges such as complex field backgrounds, high intra-class similarity of lesion morphology, and severe data imbalance continue to impede model stability and interpretability. To address these issues, this study proposes DAPR-AM-Net, an intelligent diagnostic framework for tomato leaf diseases that integrates dual-attention progressive refinement with adaptive MixUp. The method introduces four key innovations: (1) a Dual Attention Fusion Mechanism (DAFM) that jointly leverages channel-wise and spatial attention to enhance lesion-related texture, color, and structural cues while suppressing background noise via the CBAM module, thereby directing the network's focus toward pathogenic regions; (2) an Adaptive MixUp with Attention-Aware Sampling (AMAAS) module that dynamically adjusts sample mixing ratios according to attention maps, effectively improving discrimination in complex boundary areas; (3) a Progressive Feature Refinement with Dual Attention (PFR-DA) module that incrementally optimizes deep feature representations through cross-hierarchical information flows; and (4) an Imbalance-Aware Multi-Objective Optimization (IAMOO) strategy that adaptively modulates loss weights based on category distribution to strengthen recognition of minority disease classes. On our self-constructed Tomato-DD dataset, DAPR-AM-Net achieves superior performance across all major metrics-including an accuracy of 99.73%, precision of 99.73%, recall of 99.74%, and an F1-score of 99.73%-outperforming current state-of-the-art approaches. On the full Plant-Village dataset, the model achieves 99.85% accuracy, 99.78% precision, 99.84% recall, and a 99.81% F1-score, while maintaining a compact model size of only 4.72 M parameters. Multi-level interpretability analyses corroborate the transparency and reliability of the model's inference process. Additionally, we developed an end-to-end smart agriculture platform powered by DAPR-AM-Net. Overall, DAPR-AM-Net provides a forward-looking yet practical solution for high-accuracy and strongly interpretable disease diagnosis in smart agriculture scenarios, demonstrating both methodological innovation and real-world applicability.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep soil layers show the most pronounced genetic variation in wheat root length.","authors":"Bjarne Nielsen, Marta Malinowska, Pernille Sarup, Winnie S Füchtbauer, Dario Fè, Arnesta Odone, Kristian Thorup-Kristensen, Just Jensen","doi":"10.1186/s13007-026-01554-1","DOIUrl":"10.1186/s13007-026-01554-1","url":null,"abstract":"<p><p>Wheat is one of the most important cereals worldwide, yet significant gaps remain in our understanding of genetic variability in root traits, especially those associated with deeper rooting that support resource acquisition in challenging environments. Root traits are typically controlled by many genes with small effects and often display low heritability. Our aim was to develop a statistical approach to analyse root variation across soil depth and to determine where genetic differences in root intensity are most detectable. An experiment was conducted at the RadiMax semi-field facility, which is designed to measure deep root systems. Five years of phenotypic data recorded each June produced observations from 1500 rows. Each row captured root intensity across the soil profile from 0.6 m to 2.6 m, enabling detailed analysis of vertical root distribution. Across the five years, 513 winter wheat cultivars were grown in the facility, and among those 409 were genotyped with SNP chips. Depth-resolved regression models with random coefficients were used to quantify genetic and non-genetic variation in root intensity across soil depths, while accounting for spatial variation between rows. Random variation within rows was found to be constant across depths. The models showed that genetic variance for cumulative root intensity increased substantially below 1.1 m, with the deepest layers exhibiting the largest differences between wheat lines. Narrow-sense heritability of point measurements peaked at approximately 1.5 m ([Formula: see text]).</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13563984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148332141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A three-dimensional reconstruction method for seedlings based on improved DIFIX3D.","authors":"Zibo Yin, Tingting Yang, Suyin Zhou, Xinmei Wu, Junhua Ye, Aijun Xu","doi":"10.1186/s13007-026-01541-6","DOIUrl":"10.1186/s13007-026-01541-6","url":null,"abstract":"<p><p>Three-dimensional (3D) reconstruction of seedlings commonly suffers from under-constrained issues in areas with severe occlusion and insufficient observation, leading to false geometry and rendering artifacts. The existing methods primarily rely on pixel-level consistency constraints, resulting in poor stability when reconstructing seedlings with slender branches and complex structures. To address these challenges, this paper proposes a 3D reconstruction method for seedlings which is based on improved DIFIX3D+. By integrating the Stable Diffusion-VAE module into the DIFIX3D+ framework, this approach enhances the reconstruction constraint mechanism at the representation level. Firstly, high-precision segmentation of seedlings was achieved by eliminating background interference using the Florence-2 semantic priors and the SAM2 (Segment Anything Model 2) instance segmentation model. Then, by jointly mapping DIFIX3D+ restored images and their corresponding rendered images into the latent space of Stable Diffusion-VAE, consistency supervision was established within the latent representation domain. It worked in tandem with traditional pixel-level constraints, thereby suppressing the propagation of pixel noise caused by lighting variations, specular reflections, and texture repetitions at the representation level. Experimental results demonstrated that on the self-built dataset, compared to the original 3DGS (3D Gaussian splatting) and DIFIX3D+, the proposed method achieved the peak signal-to-noise ratio (PSNR) improvement of 28.99% and 24.25%, respectively, and the structural similarity index measure (SSIM) improvement of 3.16% and 3.74%, while reducing the learned perceptual image patch similarity (LPIPS) by 9.24% and 41.94%. The average PSNR, SSIM, and LPIPS values reached 31.54 dB, 0.9759, and 0.054, respectively, demonstrating significantly superior reconstruction accuracy compared to mainstream models such as Nerfacto, Plenoxels, and Mip-Splatting. This method combines high visual fidelity with excellent geometric structure representation capabilities, providing a technical reference for high-quality, low-cost 3D reconstruction of seedlings in complex scenes.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13536574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148296112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting the tomato fruit epidermis using the nsLTP2 promoter: a molecular tool to study the composition, architecture and properties of the cuticle.","authors":"Marie Alonso, Ella Paulsen, Nathalie Geneix, Sandra Pedemay, Angelina D'Orlando, Didier Marion, Marc Lahaye, Benedicte Bakan, Christophe Rothan, Johann Petit","doi":"10.1186/s13007-026-01559-w","DOIUrl":"10.1186/s13007-026-01559-w","url":null,"abstract":"<p><strong>Background: </strong>The cuticle is a hydrophobic polymeric assembly covering the surface of the aerial parts of plants. In the tomato fruit (Solanum lycopersicum), the cuticle is involved in many agronomically important traits, such as fruit brightness, resistance to cracking or shelf life. The development of a molecular tool targeting cuticle-associated genes in tomato fruit epidermis could considerably advance our understanding of the relationship between the structure of the cuticle and its functional properties.</p><p><strong>Results: </strong>According to its expression profile, the non-specific Lipid Transfer Protein 2 promoter (pronsLTP) was selected and its application as a molecular tool for targeting modifications in the epidermis of tomato fruit was validated. Indeed, fluorescence analysis confirmed that pronsLTP activity is associated with the fruit epidermis and coincides with cuticle accumulation. In addition, edition using a CRISPR tissue-specific knockout approach (CRISPR-TSKO) of the carotenoid SlPSY1 gene via pronsLTP showed a high mutation frequency only in the fruit exocarp and mesocarp. Furthermore, overexpression of the flavonoid regulatory gene SlMYB75 under the control of pronsLTP resulted in targeted accumulation of anthocyanins in the exocarp of growing fruit, associated with a change in cuticle composition and permeability.</p><p><strong>Conclusions: </strong>Altogether, our results provide a new molecular tool essential for delineating the functional role of cuticle components and opens new perspectives for the targeted engineering of cuticle-related traits to improve fruit quality and agronomical properties.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13536634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148295722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated stomatal traits measurement in melon (Cucumis melo L.) based on vision transformers with dynamically composable multi-head attention.","authors":"Yao Huang, Lelong Yan, Jiaxi Yang, Chunyan Cheng, Yuhui Wang, Ji Li, Qunfeng Lou, Yongbing Zhang, Chuntao Qian","doi":"10.1186/s13007-026-01558-x","DOIUrl":"10.1186/s13007-026-01558-x","url":null,"abstract":"<p><p>Stomatal trait analysis is essential for optimizing crop photosynthesis and transpiration, yet deep learning studies have focused mainly on monocotyledons, leaving dicotyledonous crops such as melon (Cucumis melo L.) understudied. To bridge this gap, we established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images. On this basis, we developed an improved Mask R-CNN framework using Vision Transformer (ViT) as the backbone. Specifically, standard Multi-Head Attention (MHA) was replaced with Dynamically Composable Multi-Head Attention (DCMHA), which enhances information exchange across attention heads and alleviates the low-rank limitation of conventional attention. In addition, a modified effective Squeeze-and-Excitation (eSE) module was incorporated into the Feature Pyramid Network (FPN) to strengthen channel dependency modeling and multi-scale feature representation. On the melon dataset, the proposed model achieved a mean average precision (mAP) of 72.40 ± 0.09%, with AP50 and AP75 of 91.93 ± 0.14% and 84.59 ± 0.19%, respectively. Repeated-run statistical analyses showed that eSE significantly and consistently improved the main detection metrics across backbones, whereas DCMHA provided a more moderate gain within the ViT-based setting, with clearer support for AP50 than for mAP or AP75 under the baseline FPN setting. Overall, the combined configuration remained among the top-performing models for stomatal instance segmentation. Ellipse fitting further enabled automated quantification of stomatal length, width, count, area, and circumference, showing strong agreement with manual measurements (Pearson r = 0.978). The model also showed preliminary transferability to cucumber, watermelon, pumpkin, and loofah, with an average species-specific R² of 0.86, although each species was evaluated on a limited sample set.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13555868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148295459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying wheat spike morphology by high resolution 3D surface scanning.","authors":"Latifa Greche, Nicolas Virlet, Malcolm J Hawkesford","doi":"10.1186/s13007-026-01550-5","DOIUrl":"10.1186/s13007-026-01550-5","url":null,"abstract":"<p><p>An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and cross-sectional area profile. New shape descriptors based on cross-sectional area profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148259218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phytohormone dynamics in tomato leaves due to in-situ continuous wearing of sensors.","authors":"Mingji Wei, Shuai Lu, Fei Lyu, Kai Zou, Yongxian Song, Guoxiao Li, Tianbao Wang, Ning Yang","doi":"10.1186/s13007-026-01552-3","DOIUrl":"10.1186/s13007-026-01552-3","url":null,"abstract":"<p><p>Tomato has become an increasingly important functional food crop due to its high nutritional value and short growth cycle. However, its production is highly susceptible to compound environmental stresses, leading to substantial yield losses. Currently, in-situ continuous monitoring using wearable sensors offers a novel approach for early warning of stress in tomatoes. Yet, the physical properties of these sensors may trigger leaf stress responses during wear, resulting in distorted hormone monitoring data. To address above problems, this study systematically investigated the effects of key sensor properties including surface hardness, breathability, light transmittance, invasion depth and weight on the dynamic changes of six hormones in tomato leaves: abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), ethylene (ET), auxin (IAA), and gibberellin (GA). By designing polydimethylsiloxane (PDMS) sensor simulators with varying parameters and employing enzyme-linked immunosorbent assay (ELISA) for multi-time-point hormone detection, the results demonstrate that sensor physical properties significantly disrupt hormonal balance. Results show that higher hardness induces sharp accumulation of JA and SA, inadequate breathability promotes ethylene and ABA synthesis, and light shading significantly suppresses GA while activating ABA-mediated senescence pathways. This study provides a theoretical and experimental basis for optimizing sensor biocompatibility and improving the accuracy of in-situ monitoring.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148252912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DiffPlantCT: a training-free, annotation-free approach to cross-species plant CT image segmentation.","authors":"Weizhen Liu, Yihao Fan, Haowei Zhao, Chang Chi, Yihong Wu, Hao Lu, Weijuan Hu, Xiaohui Yuan","doi":"10.1186/s13007-026-01551-4","DOIUrl":"10.1186/s13007-026-01551-4","url":null,"abstract":"<p><p>Traditional deep learning-based plant computed tomography (CT) image segmentation methods require a large amount of high-quality manually labeled data for model training specific to each species, leading to substantial labor costs and poor adaptability to new species. These limitations hinder the application of CT imaging in large-scale cross-species plant phenotyping analysis. Therefore, developing annotation-free and training-free plant CT image segmentation methods is of significant research and application value in reducing research costs and promoting the efficiency of cross-species analysis. To achieve this, we introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT. It is a 2D-to-3D framework that first segments all 2D slices and then assembles them in their original order to generate a 3D CT segmentation. For each slice, this framework directly constructs discriminative clustering features by combining the general semantic priors provided by the self-attention layers in a pre-trained stable diffusion model with the intrinsic grayscale distribution of original image, thereby completely avoiding the need for manual annotations. The method ultimately outputs segmentation results solely through unsupervised clustering, achieving zero-shot generalization without any model training or fine-tuning. To evaluate the feasibility of DiffPlantCT in cross-species segmentation, we benchmark the segmentation performance on two public datasets (walnut fruit and barley spike) and two self-collected datasets (wheat spike and rice panicle). The results show that DiffPlantCT achieved the best performance, with a 41.6% improvement in overall mIoU compared to the state-of-the-art unsupervised method. For the first time, we demonstrate annotation-free, training-free segmentation of cross-species plant CT images successfully.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13491703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148227431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ontology-driven dual-channel relational graph convolutional network (DCR-GCN) for lettuce leaf phenotype classification.","authors":"Ting Li, Sheng Wu, Guangjie Qiu, Ying Zhang, Minggang Zhang, Congmin Wang, Decong Zheng, Xinyu Guo","doi":"10.1186/s13007-026-01543-4","DOIUrl":"10.1186/s13007-026-01543-4","url":null,"abstract":"<p><p>Lettuce (Lactuca sativa L.) is an important leafy vegetable with substantial diversity in leaf topology, geometry, color, and texture, which poses challenges for germplasm identification and new variety protection. However, current approaches to complex phenotypic analysis are often limited in their ability to explicitly represent and exploit semantic relationships among phenotypic traits. To address this limitation, a knowledge graph-enhanced graph learning framework for lettuce phenotypic traits was developed. Phenotypic traits were first extracted from leaf images of five lettuce types. Based on the trait description standards of the International Union for the Protection of New Varieties of Plants (UPOV), a Lettuce Leaf Phenotypic Trait Knowledge Graph (LLPT-KG) was constructed to represent semantic associations among traits. On this basis, a Dual-Channel Relational Graph Convolutional Network (DCR-GCN) was developed to jointly integrate node attribute features and graph structural information for lettuce type classification. To improve interpretability, node- and edge-level importance analyses were further performed to identify the phenotypic traits and semantic relations most relevant to type discrimination. The proposed framework achieved an accuracy of 0.94 and a Macro-F1 score of 0.94. Compared with the best-performing single-channel graph baseline, R-GCN (Relational Graph Convolutional Network), DCR-GCN improved accuracy by approximately 9% points and Macro-F1 by 10% points. These results demonstrate that combining knowledge graphs with graph neural networks can effectively capture complex phenotypic relationships in lettuce and improve classification performance. The proposed framework provides methodological support for precise lettuce germplasm identification, digital phenotypic evaluation for new variety protection, and digital-assisted pre-screening prior to field-based DUS (Distinctness, Uniformity, and Stability) testing.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13480040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148219244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"X-ray-mediated isolated microspore mutagenesis: a system for salt-tolerant line development in wheat (Triticum aestivum L.).","authors":"Wenlu Tan, Hui Li, Huijun Guo, Yongdun Xie, Huiyuan Li, Jiayu Gu, Hongchun Xiong, Yuping Ding, Le Xu, Linshu Zhao, Luxiang Liu","doi":"10.1186/s13007-026-01549-y","DOIUrl":"10.1186/s13007-026-01549-y","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Establishing a technical system for in vitro mutagenesis and salt tolerance screening of wheat microspores is crucial for accelerating the homozygous stabilization of mutant progeny, creating novel salt‑tolerant wheat germplasm, and enabling the effective utilization of saline‑alkali land. Due to technical limitations of in vitro microspore culture in wheat, studies combining wheat microspore culture with in vitro mutagenesis and salt stress screening have not been reported. Based on a previously established genotype‑dependent wheat microspore culture system, our laboratory developed a technical system integrating microspore‑based X‑ray mutagenesis and salt stress screening, thereby providing a new technical approach for accelerating the development of salt‑tolerant wheat germplasm.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;To determine the optimal X-ray irradiation dose, microspores from the cultivars Shi 4185 and HeNong 6425 were exposed to 0, 3, 5, 10, 15, and 20 Gy of X-ray radiation. The LD₅₀ values for Shi 4185 and HeNong 6425 were 3 Gy and 4 Gy, respectively. To determine the NaCl concentration for in vitro salt tolerance screening of microspores, callus induction and differentiation were carried out on media supplemented with a range of NaCl concentrations. Adding 51 mM NaCl to the induction medium reduced regenerated plants per anther by 62.95% (Shi 4185) and 42.76% (HeNong 6425) relative to controls, while adding 85 mM NaCl to the differentiation medium decreased the regenerated plants by 46.65% (Shi 4185) and 44.02% (HeNong 6425), respectively. Microspores of Shi 4185 and HeNong 6425 were irradiated with X‑rays (3 Gy) and then subjected to salt screening at the induction (51 mM NaCl) and differentiation (85 mM NaCl) stages. Under this dual salt-stress treatment, the regenerated plants per anther of Shi 4185 and HeNong 6425 was reduced by 84.82% and 81.60%, respectively, compared with untreated controls. Overall, 68 and 33 doubled haploid (DH) lines were obtained from Shi 4185 and HeNong 6425, respectively. Through salt tolerance evaluation at the germination, seedling, and 5 dS/m saline‑alkali field stages, 15 salt‑tolerant lines were obtained from Shi 4185 and two from HeNong 6425, with per‑plant yield increases of 56.54%-105.81% and 54.01%-56.07%, respectively, compared with the wild type. Exome sequencing of five salt‑tolerant Shi 4185 DH lines revealed higher numbers of SNP and InDel mutation sites than those in regenerated lines without mutagenesis and salt stress treatment; Under 342 mM NaCl stress, the expression levels of salt tolerance genes TaNHX, TaPDI, and TaMYB in these five lines were significantly higher than those in the wild type Shi 4185.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The integrated technical system combining microspore culture, X‑ray irradiation, and in vitro salt stress screening established in this study provides technical support for the development of novel salt‑tolerant wheat germplas","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13471552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148194865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}