Frontiers in Plant Science最新文献

{"title":"Editorial: Unraveling pathogen-plant-microbiome interactions in horticultural crops through omics approaches.","authors":"Abhay Kumar Pandey, Sai Shiva Krishna Prasad Vurukonda, Davide Giovanardi","doi":"10.3389/fpls.2025.1696789","DOIUrl":"https://doi.org/10.3389/fpls.2025.1696789","url":null,"abstract":"","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1696789"},"PeriodicalIF":4.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145307723","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":"Editorial: Dynamics of greenhouse gases in forest systems.","authors":"Julio Javier Diez Casero, Bruno Britto Lisboa, Luciano Kayser Vargas","doi":"10.3389/fpls.2025.1698093","DOIUrl":"https://doi.org/10.3389/fpls.2025.1698093","url":null,"abstract":"","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1698093"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299715","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":"Countrywide <i>Corchorus olitorius</i> L. core collection shows an adaptive potential for future climate in Benin.","authors":"Dèdéou A Tchokponhoué, Sognigbé N'Danikou, Emmanuel Omondi, Spéro Coffi, Belchrist Eliel Sossou, Aristide Carlos Houdegbe, Charlotte A O Adje, Nicodeme V Fassinou Hotegni, M Eric Schranz, Maarten van Zonneveld, Enoch G Achigan-Dako","doi":"10.3389/fpls.2025.1634672","DOIUrl":"10.3389/fpls.2025.1634672","url":null,"abstract":"<p><strong>Introduction: </strong>Understanding the genome-wide variation pattern in crop germplasm is required in profiling breeding products and defining conservation units. Yet, such knowledge was missing for the large germplasm collection of <i>Corchorus olitorius</i> in Benin at CalaviGen (the University of Abomey-Calavi genebank), the world's largest holder of the crop germplasm with 1,566 accessions conserved.</p><p><strong>Methods: </strong>Using 1,114 high-quality SNPs, this study: i) investigated the spatial variation of the genetic structure of 305 accessions sampled along the South-North ecological gradient of Benin, ii) derived a core collection from the batch of accessions and iii) gauged the extent of (mal)adaptation of this core set.</p><p><strong>Results and discussion: </strong>Overall, we detected a moderate diversity with a total gene diversity of 0.28 and an expected heterozygosity estimate of 0.27. The spatial variation of the genomic diversity painted an increasing trend following the South-North ecological gradient, giving rise to four optimal genetic groups based on STRUCTURE analysis while the neighbour-joining analysis revealed three clusters. The ShinyCore algorithm application yielded a core set of 54 accessions that echoed a good geographical representativeness and encompassed a level of diversity comparable to that of the whole collection. Nearly 88% of this core set accessions were characterized by a low genomic offset score, which suggests a strong adaptation potential to future climate. This SNP-based core collection represents a unique and viable working asset for accelerated traits-discovery, in the species and should play a pivotal role in international collaborative initiatives dedicated to promoting <i>C. olitorius</i> use and conservation.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1634672"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299714","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":"Enhancing yield prediction from plot-level satellite imagery through genotype and environment feature disentanglement.","authors":"Anirudha A Powadi, Talukder Z Jubery, Michael Tross, Nikee Shrestha, Lisa Coffey, James C Schnable, Patrick S Schnable, Baskar Ganapathysubramanian","doi":"10.3389/fpls.2025.1617831","DOIUrl":"10.3389/fpls.2025.1617831","url":null,"abstract":"<p><p>Accurately predicting yield during the growing season enables improved crop management and better resource allocation for both breeders and growers. Existing yield prediction models for an entire field or individual plots are based on satellite-derived vegetation indices (VIs) and widely used machine learning-based feature extraction models, including principal component analysis (PCA) and autoencoders (AE). Here, we significantly enhance pre-harvest yield prediction at plot-scale using Compositional Autoencoders (CAE) - a deep-learning-based feature extraction approach designed to disentangle genotype (G) and environment (E) features - on high-resolution, plot-level satellite imagery. Our approach uses a dataset of approximately 4,000 satellite images collected from replicated plots of 84 hybrid maize varieties grown at five distinct locations across the U.S. Corn Belt. By deploying the CAE model, we improve the separation of genotype and environment effects, enabling more accurate incorporation of genotype-by-environment (GxE) interactions for downstream prediction tasks. Results show that the CAE-based features improve early-stage yield predictions by up to 10% compared to traditional autoencoder-based features and outperform vegetation indices (VIs) by 9% across various growth stages. The CAE model also excels in separating environmental factors, achieving a high silhouette score of 0.919, indicating effective clustering of environmental features. Moreover, the CAE consistently outperforms standard models in unseen environments and unseen genotypes yield predictions, demonstrating strong generalizability. This study demonstrates the value of disentangling G and E effects for providing more accurate and early yield predictions that support informed decision-making in precision agriculture and plant breeding.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1617831"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299727","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":"CNATNet: a convolution-attention hybrid network for safflower classification.","authors":"Pengwei Ma, Nan Lian, Leilei Dong, Yunchen Luo, Zheng Sun, Yuanjiao Zhu, Zefang Chen, Jie Zhou","doi":"10.3389/fpls.2025.1639269","DOIUrl":"10.3389/fpls.2025.1639269","url":null,"abstract":"<p><p>Safflower (Carthamus tinctorius L.) is an important medicinal and economic crop, where efficient and accurate filament grading is essential for quality control in agricultural and pharmaceutical applications. However, current methods rely on manual inspection, which is time-consuming and difficult to scale. A coarse-to-fine grading framework is established, consisting of cluster-level classification for rapid assessment and filament-level fine-grained classification. To implement this framework, a lightweight hybrid network, CNATNet, is designed by integrating convolutional operations and attention mechanisms. The classical C2f feature extraction module is optimized into two components: C2S2, a lightweight convolutional variant with cascaded split connections, and AnC2f, an n-order local attention mechanism. A depthwise separable convolution-based head (DWClassify) is further employed to accelerate inference while maintaining accuracy. Experiments on a high-resolution safflower filament dataset indicate that CNATNet achieves 98.6% accuracy at the cluster level and 95.6% at the filament level, with an average latency of 1.9 ms per image. Compared with representative baselines such as YOLOv11m and RT-DETRv2s, CNATNet consistently yields higher accuracy with reduced latency. Moreover, deployment on the Jetson Orin Nano demonstrates real-time performance at 63 FPS under 15 W, confirming its feasibility for embedded agricultural grading in resource-constrained environments. These results suggest that CNATNet provides a task-specific lightweight solution balancing accuracy and efficiency, with strong potential for practical safflower quality classification.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1639269"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299704","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":"Bibliometric analysis of global research trends and development prospects of <i>Smilax glabra</i> Roxb using CiteSpace.","authors":"Wenqing Shi, Xiao Li, Xin Zhao, Jingjing Jiang, Chenxi Wang, Guorong Fan, Yuefen Lou","doi":"10.3389/fpls.2025.1651650","DOIUrl":"10.3389/fpls.2025.1651650","url":null,"abstract":"<p><strong>Introduction: </strong><i>Smilax glabra</i> Roxb (SGR) is extensively utilized in the management of disorders, including hyperuricemia and gout, due to its notable pharmacological effects, and it is also the primary component in the functional food turtle jelly. Despite extensive research on SGR, no systematic statistical analysis of the literature has been conducted on it. This study provides a comprehensive bibliometric analysis of SGR, identifies the current research landscape, identifies hotspots, and performs trend analysis.</p><p><strong>Methods: </strong>All Chinese and English literatures pertaining to SGR was gathered from Web of Science Core Collection (WoSCC), China Knowledge Network (CNKI), Wanfang database, and VIP database, subsequently de-duplicated and organized, with CiteSpace software employed for visualization and analysis of the literature.</p><p><strong>Results: </strong>A total of 1723 articles were incorporated into the analysis, and the quantity of SGR-related publications in English persists in its upward trajectory. Sun Weifeng, Lisa Dong, and Zhang Qingfeng emerged as the principal contributors, while Beijing University of TCM and Zhejiang University of TCM established themselves as the foremost publishing organizations. Noteworthy, keywords indicative of contemporary research focal points encompassed \"Chinese medicine treatment,\" \"gout,\" \"anti-inflammatory,\" and \"network pharmacology.\"</p><p><strong>Discussion: </strong>The investigation into SGR concentrates on pharmaceuticals and their active components, therapeutic interventions, and pharmacological mechanisms. Recently, anti-inflammatory and cyberpharmacology have emerged as prominent trends, indicating that the integration of animal studies with molecular bioinformatics to investigate the pharmacological mechanism of SGR is the main research direction in the future, while cardiovascular protection and neuroprotective effects have become significant areas of recent inquiry. Consequently, SGR is anticipated to be a functional plant for the treatment of various diseases.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1651650"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12519948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299703","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 deep convolutional neural network development for soybean leaf disease recognition.","authors":"Yakun Zhang, Ruofei Bao, Mengxin Guan, Zixuan Wang, Libo Wang, Xiahua Cui, Xiaoli Niu, Yan Wang, Shaukat Ali, Yafei Wang","doi":"10.3389/fpls.2025.1655564","DOIUrl":"10.3389/fpls.2025.1655564","url":null,"abstract":"<p><p>Soybean is one of the world's major oil-bearing crops and occupies an important role in the daily diet of human beings. However, the frequent occurrence of soybean leaf diseases caused serious threats to its yield and quality during soybean cultivation. Rapid identification of soybean leaf diseases could provide a better solution for efficient control and subsequent precision application. In this study, a lightweight deep convolutional neural network (CNN) based on multiscale feature extraction fusion (MFEF) and combined with a dense connectivity (DC) network (MFEF-DCNet) was proposed for soybean leaf disease identification. In MFEF-DCNet, a multiscale feature extraction fusion (MFEF) module for soybean leaves was constructed by utilizing a convolutional attention module and depth-separable convolution to improve the model feature extraction capability. Multiscale features are fused by using dense connections (DC) in the backbone network to improve the model generalization capability. Experiments were implemented on eight distinct disease and deficiency classes of soybean images (including bacterial blight, cercospora leaf blight, downy mildew, frogeye leaf spot, healthy, potassium deficiency, soybean rust, and target spot) using the proposed network. The results showed that the MFEF-DCNet had an accuracy of 0.9470, an average precision of 0.9510, an average recall of 0.9480, and an F1-score of 0.9490 for soybean leaf disease identification. And MFEF-DCNet had certain performance advantages in terms of classification accuracy, convergence speed and other effects compared with VGG16, ResNet50, DenseNet201, EfficientNetB0, Xception and MobileNetV3_small models. In addition, the accuracy of the MFEF-DCNet model in recognizing soybean diseases in local data was 0.9024, which indicated that the MFEF-DCNet model had favorable application in practical applications. The proposed model and experience in this study could provide useful inspiration for automated disease identification in soybean and other crops.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1655564"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12519086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299710","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":"Analysis of coat texture characteristics of bread wheat grains obtained from digital images.","authors":"Dmitry R Avzalov, Mian Abdur Rehman Arif, Evgenii G Komyshev, Vasily S Koval, Andreas Börner, Dmitry A Afonnikov","doi":"10.3389/fpls.2025.1659548","DOIUrl":"10.3389/fpls.2025.1659548","url":null,"abstract":"<p><strong>Introduction: </strong>The coat texture characteristics of grains in an image are informative parameters often used to classify plants into species or varieties. Intraspecific and interspecies diversity of texture parameters indicates a significant contribution of the genetic component to the formation of these traits. However, the structural and molecular properties of the grain shell, which can determine the texture in the image, have been poorly studied.</p><p><strong>Methods: </strong>Here, a comprehensive analysis of the texture characteristics of bread wheat grains from the International Triticeae Mapping Initiative (ITMI) population was performed based on their digital images.</p><p><strong>Results: </strong>The assessment of their diversity revealed two characteristic types of variability: smoothness/roughness and wrinkling along and across the grain axis. It was shown that both genotype and storage duration in the genbank contribute significantly to the formation of all grain texture characteristics investigated. Storage duration was found to be associated with an increase in grain surface roughness. A significant relationship between texture and grain germination was found for only one characteristic, GLCM (gray-level co-occurrence matrix) correlation. QTL analysis identified thirty-six additive and eight pairs of epistatic loci associated with texture traits. These loci were located on eight wheat chromosomes. Prioritization of genes in the identified loci and their functional analysis allowed us to hypothesize a possible link between grain shell texture and cell wall properties.</p><p><strong>Conclusion: </strong>The results demonstrate the genetic and environmental determinants of grain texture traits.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1659548"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299711","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":"Plant stress and proteomics in medicinal plants.","authors":"Bita Kazemi Oskuei, Antonio Masi, Arkadiusz Kosmala, Nasser Mahna","doi":"10.3389/fpls.2025.1656247","DOIUrl":"10.3389/fpls.2025.1656247","url":null,"abstract":"<p><p>Medicinal plants serve as abundant reservoirs of natural compounds, including pigments, spices, insect repellents, and therapeutic compounds, which are utilized extensively in traditional systems. However, their phytochemicals, potential health benefits, and even response to extreme environments are not fully explored. A range of omics technologies has been extensively utilized in the study of medicinal plants to explore gene functions, unravel biosynthetic pathways of bioactive compounds, and understand the regulatory mechanisms behind gene expression. Due to the complex genetic regulatory network in medicinal plants, new technologies such as proteome assays make it easier to explain biological phenomena. Proteomics could offer a paradigm shift in our understanding of medicinal plants' cellular metabolism. Until now, few classifications regarding recent and upcoming trends in proteomic studies in medicinal plants have been presented. This review highlights the most recent advances in medicinal plants' proteomics and how proteomics gains insight into the dynamic changes in medicinal plants' cellular metabolism.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1656247"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299730","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":"Blending sludge alkaline hydrolysate and urea affects grape yield and quality by regulating soil bacterial communities.","authors":"Donghe Xue, Yan Yang, Huofeng Zhang, Yijie Quan, Zejin Li, Zixu Li, Wei Wang, Huijuan Bo, Dongsheng Jin, Minggang Xu, Qiang Zhang, Zhiping Yang","doi":"10.3389/fpls.2025.1665661","DOIUrl":"10.3389/fpls.2025.1665661","url":null,"abstract":"<p><strong>Introduction: </strong>Fertilization is vital for improving grape (<i>Vitis vinifera L.</i>) yield and quality. Unlike traditional nitrogen fertilizers, the mechanisms by which sludge alkaline hydrolysate (SAH), a novel fertilizer, influences grape quality and yield are still poorly understood.</p><p><strong>Methods: </strong>In this study, six treatments were established: 20% SAH + 80% urea (M1), 40% SAH + 60% urea (M2), 60% SAH + 40% urea (M3), 80% SAH + 20% urea (M4), pure SAH (M5), and pure urea (M6). The effects of applying SAH and urea mixtures to grapes were evaluated, with focus on performance parameters, soil nutrients, and microbial communities.</p><p><strong>Results and discussion: </strong>The results show that 60-80% SAH application significantly enhanced grape stem thickness, chlorophyll content, photosynthetic efficiency, fruit quality, and increased yield. Concurrently, it elevated soil nutrient contents, improved microbial community structure, and altered nitrogen cycle gene copy numbers. Molecular ecological network analyses indicated that Firmicutes, Acidobacteriota, Gemmatimonadota, and Ascomycota were key taxa. Bacterial-fungal cooperation was the dominant interaction, accounting for 65.98-94.61% of all observed microbial interactions, compared to antagonistic interactions. Mantel analysis showed that bacterial community and nitrogen cycle genes (ammonia-oxidizing bacteria (<i>AOB</i>), nitrogen fixation hydrogenase (<i>nifH</i>)) were important for grape yield and quality. These findings offer guidance for the effective use of SAH in grape production. Future studies should elucidate how SAH regulates fruit quality-related gene expression to uncover its mechanisms and enable its full-scale use in viticulture.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1665661"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299712","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}