Plant Methods最新文献

{"title":"Integrating scanning X-ray scattering and fluorescence for multi-scale analysis of seed structure supported by machine learning tools.","authors":"Lena Merten, Gudrun Lotze, Marianne Ahmad, Felix Roosen-Runge","doi":"10.1186/s13007-026-01585-8","DOIUrl":"10.1186/s13007-026-01585-8","url":null,"abstract":"<p><strong>Background: </strong>Understanding the structure of plant seeds cultivated for human consumption and food manufacturing is vital to provide sustainable products as well as to investigate early growth stages. This includes structural variation between different plant species, varieties and cultivars depending on genetic setup, as well as structural modifications upon germination, aging and storing or seed treatment during processing. For plant seeds as multi-component biological materials, structural characterization must extend across multiple length scales, from molecular organization to cellular architecture.</p><p><strong>Results: </strong>We apply scanning Small- and Wide-Angle X-ray Scattering (SWAXS) and X-ray Fluorescence (XRF) on yellow pea seeds to combine local structural information on the molecular scale with imaging of cellular structures on the micrometer scale, enabling a comprehensive analysis of hierarchical organization. To identify and characterize heterogeneous regions within the pea seeds, we implement a fitting-free, data-driven segmentation and analysis workflow based on machine learning tools. This approach allows for classification of structurally distinct domains and enables quantitative comparison across samples without relying on predefined models. Furthermore, we incorporate multi-modal analysis by combining structural imaging with complementary elemental information obtained from XRF. The integration of compositional and structural data provides deeper insight into structure-composition relationships.</p><p><strong>Conclusions: </strong>This multi-scale, multi-modal approach opens new possibilities for investigating hierarchical structures and their development under diverse conditions and enables systematic comparison between different species or seeds at different developmental stages or exposed to different processing steps. The approach is broadly applicable to various kinds of samples and other hierarchically organized biological materials, which makes it a valuable technique for plant science as well as plant-based food science.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13536852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148881543","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":"Assessing the suitability of a developed photogrammetric and multispectral method for detecting biostimulant effects on plants.","authors":"Jiří Mach, Karel Fous, Zdeněk Svatý, Martin Dušek, Drahomír Schmidt, Tomáš Brányik, Martin Halecký","doi":"10.1186/s13007-026-01578-7","DOIUrl":"https://doi.org/10.1186/s13007-026-01578-7","url":null,"abstract":"<p><p>The present study validates a custom multi-sensor system for high-resolution, non-phenotyping. The photogrammetric workflow was optimised by evaluating image density, algorithms, and camera calibration. Beyond validation, a case study demonstrated the system's capacity to monitor early plant development following biostimulant treatment. Technical assessment revealed that prior internal camera calibration was unnecessary for the optics used. A reduced dataset of 120 images yielded reconstruction accuracies statistically comparable to full 360-image sets (p > 0.05), confirming potential for maximised throughput efficiency by reducing processing time by approximately 66% without compromising data integrity. Analysis demonstrated that algorithmic settings determined reconstruction accuracy. Active parameter tweaks were essential for maximising surface area precision across all plant species (R² ≥ 0.98). Conversely, volumetric accuracy required deactivation of these tweaks to maintain mesh consistency. While surface area estimation remained robust, volumetric precision showed species-specific variability driven by morphological complexity. Baseline parameters for accurate plant health scaling were established by defining species-specific vegetation index ranges (e.g., 0.38-0.82 for C. sativus). The platform's robustness was validated through a longitudinal study evaluating four treatments: yeast autolysate (A), a fungal biostimulant (F), their combination (AF), and a control (C). The system captured distinct morpho-physiological responses, demonstrating that autolysate-based treatments (A and AF) significantly enhanced biomass growth. The developed multi-sensor system recorded surface area expansions of 122% and 102% relative to the control (p < 0.001), alongside a 110% increase in biological height. Fidelity of these 3D reconstructions was substantiated by a strong correlation (R² = 0.96) between the 3D-derived leaf area index and ground-truth measurements. A key innovation of the pipeline is the integration of vertical distribution metrics as descriptive statistical tools, enabling high-resolution characterisation of canopy architecture. The plant health status metric evidenced enhanced physiological resilience in variants A and AF. Gravimetric analysis corroborated the non-destructive findings, confirming significant increases (p < 0.001) in dried shoot weight of 77% (A) and 79% (AF). The validated system decoupled structural biomass from physiological health, offering broad utility across diverse phenotyping tasks. Such functionality streamlines the valorisation of industrial by-products into biopreparations, driving progress in sustainable agriculture.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149271814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient static sampling method for in situ measurement of rhizosphere volatile organic compounds in plant-soil systems.","authors":"Anne-Sophie Wachter, Monica Barman, Nicole M van Dam, Mika Tarkka, Doris Vetterlein","doi":"10.1186/s13007-026-01573-y","DOIUrl":"10.1186/s13007-026-01573-y","url":null,"abstract":"<p><strong>Background: </strong>Volatile organic compounds (VOCs) play a crucial role in mediating the interactions of plants with their environment. Research has predominantly addressed aboveground VOCs, whereas belowground emissions-especially in undisturbed plant-soil systems-remain largely unexplored. Our study aimed to develop a method for the in situ trapping of rhizosphere VOCs using a static sampling approach. Apple (Malus domestica Borkh.) plantlets were grown in soil-filled, perforated rhizoboxes. The holes were integrated to insert silicone-based polymeric phases (Sorb-Stars^®) for the collection of VOCs. Sampling was repeated four times at one-week intervals. As VOCs found in the rhizosphere may derive from multiple origins, additional trapping setups were employed to obtain emissions specifically from roots (hydroponic-grown plants), shoots (aboveground VOC collection), and soil (soil-only rhizoboxes).</p><p><strong>Results: </strong>Using thermal desorption followed by gas chromatography/ mass spectrometry (GC/MS), a total of twelve VOCs were detected in the rhizosphere samples. Seven of these were successfully identified: 2-furanmethanol, longifolene, 2-(2-hydroxypropoxy)-1-propanol, α-dihydroterpineol, α-terpineol, 3(2H)-benzofuranone, and butylated hydroxytoluene. All of these have been documented in previous studies as VOCs emitted in biological systems. The other five VOCs could not be annotated. Six of the detected VOCs could be classified as the \"core volatilome\", being present in all trapping setups, while the others showed more setup-specific occurrence. Notably, 3(2H)-benzofuranone was the only VOC detected exclusively in the rhizosphere samples, highlighting the necessity of plant-soil interactions for its production. The setup and sampling time at which the highest relative abundance was observed varied considerably between compounds, emphasizing the dynamic nature of belowground VOC emissions. Therefore, an optimal sampling time could not be identified.</p><p><strong>Conclusions: </strong>We demonstrated that the proposed method offers a suitable approach to non-invasively capture VOCs in the rhizosphere of plant-soil systems. It was further shown that additional setups, focusing on isolated below- and aboveground components, may help determine the VOC emitters.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13412312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148606427","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":"Covered smut screening in barley: power analysis and effect on agronomic traits.","authors":"Gopika Gopinathan, Fluturë Novakazi, Ines Berro, Julie Dawson, Therése Bengtsson, Lucía Gutiérrez","doi":"10.1186/s13007-026-01577-8","DOIUrl":"10.1186/s13007-026-01577-8","url":null,"abstract":"<p><strong>Background: </strong>Covered smut in barley caused by Ustilago hordei leads to yield reduction and quality loss of stored grains and is especially challenging in organic production. However, screening for resistance remains challenging. The goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes. We considered favorable pathogen growth conditions, a sufficient sample size to detect differences among genotypes through a power analysis, sources of disease escape or avoidance, and the infection effect on agronomic traits.</p><p><strong>Results: </strong>In the first experiment, twenty genotypes treated with various inoculum concentrations were screened for disease incidence under a speed breeding system. Generally, low infection levels were found, likely due to disease escape or avoidance. Based on a power analysis, we modified the protocol to include more plants and improved pathogen growth conditions under a normal greenhouse system. With the modified protocol, the incidence of covered smut was significantly different among genotypes. The protocol also reduced the number of plants required to detect at least one infected plant. Artificial inoculation significantly decreased germination rates while head emergence, days to heading, and plant height were affected by disease infection in the most susceptible genotypes. We also found that covered smut incidence varied with tiller emergence order. The genotypes 'DH160779' (RES check), PI 270630', 'CIho15270', and 'MTV-color-158' presented potential resistance to covered smut.</p><p><strong>Conclusion: </strong>The protocol has a high power to differentiate moderately resistant barley genotypes and we confirmed that specific agronomic traits were affected by disease incidence in susceptible genotypes.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13411101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148606506","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":"Visualization and quantitative analysis of endosperm cavities in maize kernels via X-ray micro-computed tomography.","authors":"Xuebo Yin, Yu Jiang, Shengxi Hou, Jiayu Sun, Mei Huang, Yingxin Zhong, Jian Cai, Xiao Wang, Qin Zhou, Tingbo Dai, Dong Jiang","doi":"10.1186/s13007-026-01560-3","DOIUrl":"https://doi.org/10.1186/s13007-026-01560-3","url":null,"abstract":"<p><strong>Background: </strong>Endosperm cavities within maize kernels influence quality traits such as kernel plumpness and hardness, serving as a key phenotypic indicator for assessing maize yield and quality. Research on endosperm cavities remains relatively scarce due to the small size of maize kernels and limitations in technical approaches. This study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels.</p><p><strong>Results: </strong>Endosperm cavities exhibit spatial heterogeneity within the kernels: embryo-adjacent cavities (EACs) are distributed in a conical pattern around the embryo, whereas internal endosperm cavities (IECs) are located in the floury endosperm at the tip region of the kernel and exhibit a boat-shaped morphology. The volume ratio of EACs to IECs is approximately 5:1. A coordinate system was established with the kernel length axis perpendicular to the horizontal plane, revealing the spatial positions of IECs (x = 3.5 mm, y = 2.1 mm, z = 1.1 mm) and EACs (x = 2.5 mm, y = 2.3 mm, z = 7.1 mm). Significant differences in endosperm cavity characteristics were observed among the different varieties. The average volume of the endosperm cavities was 4.1 mm³, with kernel porosities ranging from 0.4% to 3.3%. These parameters exhibited highly significant positive correlations with kernel volume, kernel thickness, cavity surface density, etc. Although manual sectioning methods cannot capture the 3D features of endosperm cavities, their operational simplicity and rapid data extraction allow them to reflect, to some extent, the characteristics of endosperm cavities across different maize varieties, as confirmed by this study.</p><p><strong>Conclusion: </strong>This study elucidates the morphology and spatial distribution of endosperm cavities, revealing significant varietal differences in cavity characteristics that correlate with grain morphological traits. These findings lay the groundwork for research into maize grain digital characterisation and the relationship between grain structure and function.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149245502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The plant Golden Gate toolkit: compatibility, interoperability, and harmonized assembly in plant synthetic biology.","authors":"Jérôme Gélinas Bélanger, Shona Hanaishi, Benjamin Péret","doi":"10.1186/s13007-026-01563-0","DOIUrl":"10.1186/s13007-026-01563-0","url":null,"abstract":"<p><p>In plant synthetic biology, the emergence of Golden Gate (GG) cloning has led to the rapid development of numerous cloning kits that enable the standardized assembly of genetic modules and transcriptional units through a single, one-pot reaction. However, this rapid and somewhat uncoordinated expansion has introduced challenges, particularly regarding compatibility between cloning kits, which can hinder adoption and routine use in new laboratories. To address these recurring issues, this review provides a comprehensive overview of more than 25 well-established plant-specific GG cloning kits, along with an in-depth examination of the compatibility of their available modules, with the goal of improving interoperability across systems. To support a more integrated and forward-looking development of future GG-based efforts, we present key approaches for increasing system harmonization, including several domestication strategies, methylase-assisted hierarchical DNA assembly, and the inclusion of multiple cloning sites. As a whole, this review aims to streamline cloning workflows and reduce technical barriers for new plant synthetic biologists in pursuit of increasing the throughput of their experiments.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148550165","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":"LongPolyASE: an end-to-end framework for allele-specific gene and isoform analysis in polyploids using long-read RNA-seq.","authors":"Nadja Nolte, Kristina Gruden, Marko Petek","doi":"10.1186/s13007-026-01569-8","DOIUrl":"10.1186/s13007-026-01569-8","url":null,"abstract":"<p><strong>Background: </strong>Allele-specific expression analysis can reveal cis-regulatory differences (e.g., promoter variants, epigenetic changes) that cause imbalanced gene expression between haplotypes. Haplotype-resolved reference genomes and long-read RNA sequencing enable allele-specific expression analysis at gene and isoform-levels. However, existing tools are largely restricted to short-read RNA sequencing data and diploid organisms.</p><p><strong>Results: </strong>We developed LongPolyASE, an end-to-end computational framework for allele-specific gene and isoform expression analysis in diploid and polyploid organisms using long-read RNA sequencing, consisting of three components: Syntelogfinder, for identifying syntenic gene relationships and annotation inconsistencies; longrnaseq, for novel isoform discovery and haplotype-level quantification; and PolyASE, for statistical testing and visualization of allelic imbalance and isoform usage. We applied LongPolyASE to diploid rice, autotetraploid potato, allotetraploid rapeseed, and allooctoploid strawberry using Oxford Nanopore and PacBio long-read RNA-seq. The framework enabled identification of cis-regulatory variation, tissue-specific trans-regulatory effects, differential isoform usage, and haplotype-specific splicing differences. In addition, it facilitated the discovery of novel transcripts and genes with potential functional relevance in plant development.</p><p><strong>Conclusions: </strong>LongPolyASE addresses a key methodological gap by enabling allele-specific expression analysis in polyploid organisms using long-read RNA sequencing. By combining haplotype-aware quantification with isoform-level resolution in a reproducible workflow, the framework provides a practical tool for plant researchers working with complex genomes. Its application to crop species highlights its potential to support the identification of regulatory variation and candidate targets for plant breeding.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148550136","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":"Pixel-registered multimodal synchrotron XRF and FTIR microscopies reveal salinity stress response mechanisms in pistachio.","authors":"Jocelyn A Richardson, Georgia Drakakaki, Hannah McCurry, Shuxiao Zhang, Hoi-Ying N Holman, Samuel M Webb","doi":"10.1186/s13007-026-01572-z","DOIUrl":"10.1186/s13007-026-01572-z","url":null,"abstract":"<p><strong>Background: </strong>Salinity is a major abiotic stress that negatively affects nearly all plant species at all stages of growth. Drought and poor-quality irrigation cause high soil salinity and salt accumulation via evaporation, reducing crop productivity. Despite its critical importance, the spatial localization of salt ions and associated biochemical changes within plants experiencing high salinity remains largely unknown. In this study, we developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1 (Pistacia atlantica x Pistacia integerrima). We directly link biochemical fingerprints in stem tissue architecture with salt ion localization to provide insights into the strategies pistachio uses to tolerate salinity.</p><p><strong>Results: </strong>We observed that Pistacia spp. exposed to high salt conditions accumulated Ca, Si, Cl, Al and Mg as hotspots within the pith, compared to the control (of which only Ca and Al co-locate). In contrast, there was a decrease in K between the control and salinity treatment. Hotspots of amide I and II were present in the cortex and pith of the salinity treated sample. Additionally, the salinity treatment resulted in an increased abundance of pectin and carbohydrates within the pith compared to the control, and the abundance of esters/carboxylic acid was greater in the salinity treatment.</p><p><strong>Conclusions: </strong>We determined that Cl and K, S and P, and biochemical components polysaccharide and pectin, esters and carboxylic acid, amide I and cellulose are the strongest drivers of salinity-treatment induced variability. In the cortex and phloem/xylem, a negative K-Ca correlation decreases in the salinity treatment. Several hotspots of elements and amide I (proteins) appear under salinity treatment, particularly in the cortex, suggesting an increase in the production of stress-related proteins (in response to high Cl) and/or structural proteins (i.e. Ca). Together, these results indicate that pistachio responds to salinity through ion compartmentalization coupled with a targeted biochemical adjustment, rather than a broadscale tissue-wide response. Overall, these novel, spatially resolved pixel-registered multimodal imaging data provide an enabling platform to understand the mechanisms of salinity tolerance in Pistacia spp and can be broadly applied to studying stress-related phenotype response in various plant tissues.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148536620","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":"BeanGPT: a domain-specific retrieval-augmented generation system for Phaseolus vulgaris research.","authors":"Kiarash Mirkamandari, Mohsen Yoosefzadeh-Najafabadi","doi":"10.1186/s13007-026-01575-w","DOIUrl":"10.1186/s13007-026-01575-w","url":null,"abstract":"<p><p>BeanGPT is a domain-specific retrieval augmented generation system designed to support research and breeding decisions in common bean (Phaseolus vulgaris L.) by transforming natural language questions into citation-backed, verifiable answers. The platform integrates a large, curated corpus of legume-focused peer-reviewed literature with structured multi-year agronomic trial records collected across diverse environments, climate projections extending to 2090 under multiple emission scenarios, and standardized cultivar nomenclature to resolve naming inconsistencies across datasets and publications. BeanGPT combines semantic retrieval from a vector database with intent-based query routing and structured parameter extraction to direct questions to genetics, field performance analytics, or climate modules. To reduce errors that commonly occur in general-purpose language models, BeanGPT incorporates a genomic index that enables constant time membership lookup of gene and protein identifiers against authoritative resources, ensuring that molecular entities are either validated or clearly flagged as literature-derived. The system is implemented with a streaming web interface and an asynchronous backend that supports concurrent users and can generate interactive visualizations through automated Plotly code generation. Beta testing demonstrated strong retrieval relevance, low response latency, reliable gene verification, and high citation precision, indicating that domain-grounded RAG can improve accuracy and usability for Phaseolus vulgaris research.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13663222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148497937","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":"Temporal alignment of UAV hyperspectral and phenotype data via autoencoders for predicting nitrogen and nicotine content in cured tobacco leaves.","authors":"Mingzheng Zhang, Yan Kuai, Dong Chen, Weimin Guo, Long Zhao, Shuai Yuan, Ruomei Zhao, Qiang Xu, Xiaohe Gu, Tian'en Chen","doi":"10.1186/s13007-026-01570-1","DOIUrl":"10.1186/s13007-026-01570-1","url":null,"abstract":"<p><p>Post-curing nitrogen content (cNg) and nicotine content (cNt) constitute fundamental chemical attributes that govern the quality of flue-cured tobacco leaves. In this study, we investigate the use of unmanned aerial vehicle (UAV)-borne hyperspectral imagery in conjunction with advanced sequence-learning models to obtain canopy-level spectral information and to quantitatively predict cNg and cNt at the field scale. Nevertheless, because field experiments conducted in different growing seasons follow heterogeneous observation schedules, the resulting time series differ in both length and temporal sampling density, which poses a substantial obstacle to the direct deployment of recurrent neural-network models. To mitigate this limitation, we devised three autoencoder-based temporal-alignment schemes to reconcile the temporal dimension of multi-year spectral-phenotypic sequences, namely a fully connected autoencoder (AE_F), a one-dimensional convolutional autoencoder (AE_C), and a long short-term memory (LSTM) autoencoder (AE_L). Within this framework, the autoencoders project the original multi-temporal observations into compact latent representations and subsequently reconstruct them on a standardized temporal grid, thereby preserving salient spectral-phenotypic information while enforcing dimensional consistency across years. For comparative analysis, we additionally considered a straightforward time-step removal (TSR) procedure, which discards non-overlapping observation dates among different years and thus serves as a baseline temporal-harmonization strategy. On the temporally aligned sequences, two representative recurrent-neural architectures-long short-term memory (LSTM) networks and gated recurrent unit (GRU) networks-were trained to establish predictive models for cNg and cNt. Overall, models trained on inputs aligned by the autoencoder-based schemes exhibited markedly higher predictive skill than their TSR-based counterparts, with the convolutional and LSTM autoencoders consistently yielding greater improvements than the fully connected variant. Among all evaluated configurations, the combinations of AE_L with LSTM and with GRU delivered the best performance, attaining coefficients of determination of 0.73 for cNg and 0.56 for cNt on the independent test set. Taken together, these results indicate that the proposed autoencoder-based temporal-alignment framework can effectively distil informative features from multi-year UAV hyperspectral and phenotypic observations and constitutes a promising tool for the quantitative prediction of post-curing quality indicators in flue-cured tobacco production.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13663056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148520756","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}