Plant Methods最新文献

{"title":"MicroDeblurNet: high-fidelity restoration of spatially variant defocus in microscopic images for cucumber downy mildew.","authors":"Yiding Zhang, Bo Wang, Yuzhaobi Song, Zonghuan Han, Xiaoshuan Zhang, Lingxian Zhang","doi":"10.1186/s13007-025-01495-1","DOIUrl":"10.1186/s13007-025-01495-1","url":null,"abstract":"<p><p>Microscopic imaging provides essential visual evidence for pathogen monitoring, but its shallow depth of field and the three-dimensional height variation of spores lead to pronounced defocus blur and structural degradation. Restoring such images is therefore crucial for reliable spore identification and downstream analysis. However, microscopic defocus is a spatially varying process that severely suppresses high-frequency structures, causing natural-image deblurring models to generalize poorly. In addition, optical constraints of microscopy make realistic sharp-blur pairs difficult to obtain, further limiting learning-based restoration. To address these challenges, we propose MicroDeblurNet, a single-image deblurring network specifically designed for microscopic defocus restoration. The model incorporates a convolutional block attention module to enhance spatial selectivity toward key pathogen structures, and employs depthwise over-parameterized convolutions to capture locally varying blur patterns more effectively, enabling spatially consistent and structurally coherent restoration. Furthermore, a spatial-frequency consistency loss is proposed to strengthen high-frequency detail recovery while maintaining color fidelity and morphological integrity. To support high-fidelity supervision, we propose a paired-data construction strategy based on Laplacian-pyramid fusion and construct a clear-blur microscopic dataset for cucumber downy mildew. The restored outputs of MicroDeblurNet are further applied to sporangia detection and semantic segmentation to evaluate their impact on high-level visual tasks. Finally, we build an integrated microscopic analysis platform that delivers standardized high-quality data and automated pathogen-structure recognition and analysis to support disease assessment and management. Experimental results demonstrate that MicroDeblurNet achieves an optimal balance across pixel-level, structure-level, and perception-level metrics, reaching a PSNR of 42.48 dB and SSIM of 0.9839, outperforming advanced state-of-the-art methods. In downstream tasks, MicroDeblurNet delivers higher detection recall and segmentation accuracy in challenging scenarios involving sporangia adhesion and background impurities, demonstrating its ability to enhance target discernibility, preserve structural completeness, and improve robustness under complex microscopic conditions.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"16"},"PeriodicalIF":4.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12888288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145960017","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":"Crop phenotype prediction using SNP context and whole-genome feature embedding based on DNABERT-2.","authors":"Huan Li, Yunpeng Cui, Tan Sun, Ting Wang, Zhen Chen, Chao Wang, Wenbo Bian, Juan Liu, Mo Wang, Li Chen, Jinming Wu, Jie Huang","doi":"10.1186/s13007-025-01492-4","DOIUrl":"10.1186/s13007-025-01492-4","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Modern agriculture demands precise genomic prediction to accelerate elite crop breeding, yet traditional genomic prediction approaches, such as genomic best linear unbiased prediction (GBLUP) and Bayesian methods, focus primarily on the cumulative effect of individual SNPs, thus neglecting the concerted influence that the surrounding sequence context has on the phenotype.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;To overcome these limitations, we propose two novel feature embedding modes (SNP-context and whole-genome) based on DNABERT-2, a cross-species genomic foundation model that uses self-attention mechanisms and transfer learning to automatically identify conserved sequence features across diverse evolutionary lineages without prior biological assumptions. The whole-genome feature embedding aggregates genomic information at a global scale by pooling vectors from chunked sequences processed by DNABERT-2, whereas the context feature embedding captures local information by directly encoding variable-length (500-3000 bp) sequences centered on target SNPs. To reduce noise in the high-dimensional feature embeddings, we employed principal component analysis (PCA) and partial least squares (PLS) to project the features into a lower-dimensional space. We generated two kinds of feature embedding for three crop datasets (rice413, rice395, and maize301), investigated the impact of 500-3000 bp flanking SNP contexts on phenotypic prediction, and compared prediction accuracy variations across algorithms at 4-768 feature dimensions among the PCA, PLS, and no dimensionality reduction strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The results demonstrate that machine learning (ML) algorithms operating under the SNP-context embedding mode achieve greater accuracy and lower mean absolute errors (MAEs) than traditional SNP features, with performance peaking at optimal context lengths that proved to be trait-dependent (e.g., 1000 bp to 3000 bp), particularly for traits with low-to-moderate heritability (H&lt;sup&gt;2&lt;/sup&gt; ∈ (0.2, 0.7]). In contrast, using whole-genome embeddings as input for ML can further improve the prediction accuracy for highly heritable traits (H&lt;sup&gt;2&lt;/sup&gt; ∈ (0.7, 1.0]), even outperforming state-of-the-art deep learning models (such as DNNGP and ResGS) that rely on SNP markers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The proposed feature embedding methods, which leverage DNABERT-2 to capture the contextual features of SNPs, effectively overcome the limitations of traditional prediction models. This study demonstrates that the SNP-context mode is superior for traits with low-to-moderate heritability, while the whole-genome embedding mode excels for highly heritable ones. Our work provides plant breeders with a flexible and powerful analytical framework, enabling them to select the most suitable phenotypic prediction method based on the complexity of the target trait, thereby accelerating genetic gain in the breeding of elite crop ","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"15"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12888605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145960071","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":"Assisting species differentiation and taxonomic classification by hyperspectral imaging: an example from the parasitic plant realm.","authors":"Vasili A Balios, Samuel Ortega, Karsten Heia, Anna Avetisyan, Kirsten Krause","doi":"10.1186/s13007-025-01498-y","DOIUrl":"10.1186/s13007-025-01498-y","url":null,"abstract":"<p><p>BACKGROUND: Cuscuta, a genus of parasitic plants, poses a threat to global agriculture by infesting a wide variety of economically important crops and facilitating the transmission of plant viruses. Accurate species identification is crucial for management but is traditionally based on morphological traits that require expert knowledge, limiting accessibility and early detection. Hyperspectral imaging, a technique that captures detailed reflectance information across hundreds of narrow and contiguous wavelength bands, offers the potential to non-invasively monitor plant health with high precision. This study aimed to explore whether hyperspectral imaging, combined with machine learning algorithms, can accurately differentiate between host plant tissue and parasitic Cuscuta species and further distinguish among different species within the genus. RESULTS: Hyperspectral images were collected in both the visible-near infrared and short-wave infrared ranges, followed by preprocessing and segmentation of plant material from the background. The Normalized Difference Vegetation Index method yielded the most consistent segmentation performance. Random Forest and Neural Network models trained on segmented pixels achieved high classification accuracy and balanced F1 scores of approximately 0.97 in both binary (host versus parasite) and multiclass (species-level) classification. Feature selection using a genetic algorithm and an iterative elbow method successfully reduced the number of spectral bands needed for accurate predictions, identifying key wavelengths associated with chlorophyll content and other biochemical markers. CONCLUSIONS: This study demonstrates the effectiveness of hyperspectral imaging combined with machine learning for identifying and classifying parasitic Cuscuta species. The findings highlight the potential of this approach for rapid, non-destructive field diagnostics and precision agriculture applications. As imaging hardware continues to improve and become more affordable, such integrated systems could be deployed in real-world crop monitoring and management to mitigate the impact of parasitic plants on global food production. </p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"14"},"PeriodicalIF":5.2,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12882463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952883","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":"Machine learning-based classification of roses using 18 SNP markers for optimized genebank management.","authors":"Laurine Patzer, Marcus Linde, Thomas Debener","doi":"10.1186/s13007-025-01496-0","DOIUrl":"10.1186/s13007-025-01496-0","url":null,"abstract":"<p><strong>Background: </strong>Reliable classification of rose cultivars is complicated by their long and complex breeding history, frequent hybridization, and the coexistence of traditional horticultural categories with genetically heterogeneous groups. While molecular marker sets such as SSRs have been applied to assess genetic relationships, studies across cultivated roses and species are rare. Using 18 SNP markers on 1,345 accessions in combination with machine learning now offers an opportunity to systematically evaluate how well horticultural classes align with underlying genomic structure and to provide robust tools for the management of large germplasm collections.</p><p><strong>Results: </strong>Using a panel of SNP markers across 1,345 rose accessions from the Europa Rosarium Sangerhausen, multiple unsupervised (hierarchical, spectral, k-means, DBSCAN, HDBSCAN) and supervised (SVM, decision tree, naive Bayes, XGBoost) machine learning approaches were applied to identify genetic clusters and predict horticultural classifications. Across clustering methods, certain groups consistently emerged as genetically distinct, such as the alba and damask roses, which clustered together with low internal diversity, reflecting their shared historic origin. In contrast, tea, bengal, lutea, and remontant hybrids were repeatedly grouped together and predicted with high classification accuracies (up to 100%) but displayed high within-group diversity, which is consistent with complex breeding backgrounds. Miniature, kordesii, and rubiginosa hybrids also tended to cluster together, despite their differing horticultural labels. Overall, the labels obtained from unsupervised clustering were consistently confirmed by supervised models, which achieved balanced accuracies of up to 100%, highlighting the robustness of the observed groupings.</p><p><strong>Conclusions: </strong>Our results demonstrate that machine learning applied to SNP marker data can robustly resolve genetic relationships among rose cultivars and provide novel insights into the alignment of horticultural classifications with genomic structure. The high predictive accuracies obtained suggest that marker-based classification can serve as a reliable complementary tool for genebank management, cultivar identification, and reassessment of traditional rose categories.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"8"},"PeriodicalIF":4.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12849561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145912722","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":"Correction: PVP-40 mediated enhancement of mesophyll protoplast yield and viability for transient gene expression in black huckleberry.","authors":"Sweety Majumder, Abir U Igamberdiev, Samir C Debnath","doi":"10.1186/s13007-025-01488-0","DOIUrl":"10.1186/s13007-025-01488-0","url":null,"abstract":"","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":"1"},"PeriodicalIF":4.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12772042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906318","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":"Protoplast isolation and transient gene expression in Suaeda aralocaspica, a halophyte with single-cell C₄ anatomy.","authors":"Jing Cao, Xingxin Liao, Rui Yu, Bolaqiake Asihatibieke, YanXia Liu, Haiyan Lan","doi":"10.1186/s13007-025-01497-z","DOIUrl":"10.1186/s13007-025-01497-z","url":null,"abstract":"<p><p>BACKGROUND: Suaeda aralocaspica, an annual halophyte belonging to the Amaranthaceae family, is widely distributed in saline-alkali and arid habitats, exhibiting remarkable stress tolerance. This species has evolved a unique single-cell C4 (SCC4) photosynthetic pathway, where both C4 and C3 cycles are compartmentalized within a single cell, and demonstrates superior photosynthetic efficiency. This distinctive physiological adaptation makes S. aralocaspica an intriguing model for investigating the photosynthetic mechanism and its environmental adaptation in plants. However, efficient and rapid transformation systems are still lacking for this species. This gap has significantly hindered the dissection of the molecular regulatory networks underlying the SCC4 photosynthetic structure formation and the stress resistance mechanisms. RESULTS: Through systematic optimization of enzymatic digestion parameters and leaf pretreatment methods, we developed an efficient protoplast isolation and transient transformation system for S. aralocaspica. The highest protoplast yield (1.1 × 105/g FW) and viability (64%) were obtained from young leaves under optimized condition: enzyme solution containing 0.75% cellulase R-10 and 0.25% macerozyme R-10, 1.3 M glucose as osmotic regulator, with transverse/longitudinal leaf sectioning combined with epidermal peeling. Protoplast quality was verified by organelle fluorescence staining using different organelle markers. The highest transfection efficiency (75%) was achieved using protoplasts isolated under optimal conditions and transfected with 2400 ng/µL plasmid (5 µg total). Subcellular localization analysis revealed localization of key photosynthetic enzyme SaPEPC1 in the cytoplasm and chloroplasts, and nuclear localization of transcription factor SabHLH169. CONCLUSIONS: In summary, we established a simple and efficient system for isolating and transiently transforming S. aralocaspica chlorenchyma protoplasts. This system is shown to express exogenous proteins with the proper subcellular localization, and it provides a platform for further research on S. aralocaspica, including potential applications in somatic cell fusion, genome editing, and signal transduction and transcriptional regulation studies. The limitations of the current system, such as relatively low protoplast yield and challenges with high-throughput or complex molecular biology experiments, are also discussed. This work can serve as a starting point for developing a similar system in other SCC4 plants or Suaeda halophytes. </p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"13"},"PeriodicalIF":5.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12870416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906341","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":"Profiling DNA-protein interactions in Meloidogyne incognita using dCas9-based affinity purification.","authors":"Caroline Bournaud, Alwéna Tollec, Etienne G J Danchin, Yohann Couté, Sebastian Eves-van den Akker","doi":"10.1186/s13007-025-01475-5","DOIUrl":"10.1186/s13007-025-01475-5","url":null,"abstract":"<p><strong>Background: </strong>The root-knot nematode Meloidogyne incognita, is a highly destructive parasite that manipulates host plant processes through effector proteins, affecting agriculture globally. Despite advances in genomic and transcriptomic studies, the regulatory mechanisms controlling effector gene expression, especially at the chromatin level, are still poorly understood. Gene regulation studies in plant-parasitic nematodes (PPN) face several challenges, including the absence of transformation systems and technical barriers in chromatin preparation, particularly for transcription factors (TFs) expressed in secretory gland cells. Conventional methods like Chromatin Immunoprecipitation (ChIP) are limited in PPN due to low chromatin yields, the impermeability of nematode cuticles, and difficulties in producing antibodies for low-abundance TFs. These issues call for alternative approaches, such as dCas9-based CAPTURE (CRISPR Affinity Purification in siTU of Regulatory Elements) that allows studying chromatin interactions by using a catalytically inactive dCas9 protein to target specific genomic loci without relying on antibodies.</p><p><strong>Results: </strong>This study presents an optimized in vitro dCas9-based CAPTURE for second stage juvenile (J2) M. incognita that addresses key challenges in chromatin extraction and stability. The protocol focuses on the promoter region of the 6F06 effector gene, a critical gene for parasitism. Several optimizations were made, including improvements in nematode disruption, chromatin extraction, and protein-DNA complex stability. This method successfully isolated chromatin-protein complexes and identified four putative chromatin-associated proteins, including BANF1, linked to chromatin remodelling complexes like SWI/SN.</p><p><strong>Conclusion: </strong>The optimized in vitro dCas9-based CAPTURE protocol offers a new tool for investigating chromatin dynamics and regulatory proteins in non-transformable nematodes. This method expands the scope of effector gene regulation research and provides new insights into M. incognita parasitism. Future research will aim to validate these regulatory proteins and extend the method to other effector loci, potentially guiding the development of novel nematode control strategies.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"20"},"PeriodicalIF":4.4,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12895963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145900870","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":"Integrated experimental and computational workflows for single-cell transcriptomics in plants.","authors":"Jing Wang, Shanqiao Zheng, Bojie Lu, Yuan Jiang, Yabing Zhu, Qun Liu, Song Gao, Peng Liu, Peng Yu, Sanjie Jiang, Liang Zong","doi":"10.1186/s13007-025-01490-6","DOIUrl":"10.1186/s13007-025-01490-6","url":null,"abstract":"<p><strong>Background: </strong>Single-cell transcriptomics is a powerful approach to resolve cellular heterogeneity, yet its application in plants is constrained by challenges in tissue preparation, nuclei isolation, and transcriptome quality. Optimized experimental and computational workflows are essential to achieve robust results in plant systems.</p><p><strong>Results: </strong>We systematically benchmarked bulk and single-cell transcriptomic workflows in maize and established an integrated, optimized framework. First, we developed an improved bulk RNA-seq protocol, providing higher consistency and serving as a reference for single-cell datasets. Second, we compared three input types, protoplasts, fresh nuclei, and frozen nuclei, across tissues, demonstrating overall comparability of their transcriptomic profiles and offering guidance for studies with limited material. Third, by leveraging bulk RNA-seq as a reference, these complementary data provide additional biological context that helps to interpret and validate findings derived from single-cell transcriptomic analyses. A combination of these strategies resulted in high transcriptome integrity and clear clustering resolution in the final dataset, supporting robust identification of plant cell types. While all experimental data are derived from maize, the principles and strategies described here provide practical guidance and inspiration for single-cell studies in other plant species.</p><p><strong>Conclusions: </strong>Our study establishes optimized experimental and computational workflows for plant single-cell transcriptomics. By validating input comparability and addressing the limitations of nuclear data, we provide methodological guidance that extends beyond maize and supports future single-cell investigations across diverse plant species.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"12"},"PeriodicalIF":4.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12866480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145896653","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 lightweight hybrid transformer approach for hyperspectral imaging-based drought tolerance evaluation in tea plants.","authors":"Yuchen Li, Yi Zhang, Yu Wang, Hao Chen, Xiao Han, Yilin Mao, Litao Sun, Jiazhi Shen, Zhaotang Ding","doi":"10.1186/s13007-025-01487-1","DOIUrl":"10.1186/s13007-025-01487-1","url":null,"abstract":"<p><strong>Background: </strong>In Shandong Province of China, where annual precipitation is below 800 mm, tea plants face persistent drought stress exacerbated by global warming. Breeding drought-tolerant tea cultivars is one of the effective ways to cope with this challenge. However, traditional breeding approaches are still limited by prolonged cycles, low efficiency, and subjective evaluation. To overcome these limitations, the development of rapid and objective germplasm evaluation methods has become critical‌.</p><p><strong>Results: </strong>In this study, hyperspectral images of leaves from 12 widely cultivated 'Lucha series' tea cultivars in Shandong Province during different drought periods were collected, and the drought-related physiological indicators were measured simultaneously. Then, a tea drought tolerance index (TDTI) with enhanced accuracy was established by integrating the rate of change of indicators with temporal weights and indicator weights. Subsequently, we developed a novel lightweight Transformer-based hybrid integrated architecture to establish prediction models for the physiological indicators and TDTI. The Transformer-based models synergistically combined a Transformer encoder with XGBoost and LightGBM within a lightweight framework that leverages ensemble learning, data augmentation, and regularization to ensure robustness on limited datasets. Finally, we compared the performance of Transformer-based models against traditional machine learning models.​ The optimal models for MRP, MDA, Pro, SS, ChlT and TDTI were identified as 1D-CARS-TF, 2D-UVE-SVM, 2D-UVE-BRR, 2D-CARS-SVM, 1D-UVE-TF-CNN, and 2D-UVE-TF, respectively, achieving determination coefficient (R²) of 0.8992, 0.8307, 0.8929, 0.8373, 0.7894, and 0.7614, on an independent test set.​ The results demonstrated that the lightweight Transformer-based models equipped with multi-head self-attention mechanism exhibited outstanding capabilities in processing indicators requiring multi-band correlation mining. ​​Simultaneously, feature selection algorithms and overfitting-mitigation optimization strategies played a critical role in enhancing both the accuracy and stability of the Transformer-based models.​.</p><p><strong>Conclusions: </strong>This study established a robust technical foundation for rapid, accurate, and non-destructive comprehensive evaluation of drought tolerance for tea plant germplasm resources. However, it should be noted that they were based on a specific set of greenhouse-cultivated samples, and further validation under field conditions with expanded germplasm resources would strengthen generalizability. Anyway, the demonstrated potential of the Transformer-based model in our study advances phenomics of tea plants toward greater intelligence and efficiency.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"11"},"PeriodicalIF":4.4,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12866587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878833","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":"Integrating microbiome and machine learning for precision diagnosis of rice bakanae disease.","authors":"Sishi Chen, Fahui Yuan, Hongda Fang, Mostafa Gouda, Wenyuan Wu, Haixiang Zhang, Zhonghua Ma, Lei Feng, Mengcen Wang, Yufei Liu","doi":"10.1186/s13007-025-01486-2","DOIUrl":"10.1186/s13007-025-01486-2","url":null,"abstract":"<p><p>Bakanae is a fungal rice disease that is threatening global rice production, causing severe yield losses. The plant microbiome plays a significant role in plant stress resistance, but its high-dimensional characteristics have not been fully exploited. Therefore, we integrated the microbiome and machine learning (ML) to diagnose bakanae disease in this study. We found significant correlations between Gammaproteobacteria and Bacteroidia and the severity of bakanae disease. We constructed different diagnosis models based on random forests (RF), support vector machines (SVM), and convolutional neural networks (CNN) on 88 biological replicates with an independent test set. We found that the RF model demonstrated strong performance across four taxonomic levels, with an accuracy of 88.9% and an F1 score of 94.1%. Notably, a Bray-Curtis dissimilarity-based extraction method was proposed to rapidly screen practical information from the original microbial community, which can enhance the model performance to a certain extent. According to phenotypic data, the disease severity of infected samples was classified into two levels (high and low infected levels) using the K-means clustering method. In the diagnosis of infection severity based on the family level, the model's prediction accuracy reached 77.8%. Collectively, these findings highlight that the combination of microbiome with ML can advance diagnostic strategies for bakanae disease, providing new avenues for precision agriculture.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"10"},"PeriodicalIF":4.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12859924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145857507","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}