Frontiers in Plant Science最新文献

{"title":"Integrative molecular mechanisms of lodging resistance in rice (<i>Oryza sativa</i> L.) culms: from DELLA-OsGSK2-BZR1 signaling to cell wall engineering and QTL-based breeding.","authors":"Ahmed Sherif, Cuifeng Tang, Chao Dong, Xinxiang A, Yiding Sun, Yi Yang, Na Ge, Ruimin Nie, Amr A Hassan, Feifei Zhang, Yayun Yang","doi":"10.3389/fpls.2026.1922614","DOIUrl":"10.3389/fpls.2026.1922614","url":null,"abstract":"<p><p>Rice lodging, permanent culm displacement from the vertical position, reduces grain yield and quality, potentially causing up to ~80% yield loss. Lodging resistance is a multifactorial trait shaped by stem morphology, internal anatomy, and cell wall chemistry. A central unresolved question in the field concerns the relative contribution of lignin versus cellulose to culm mechanical strength, a debate that has significant implications for targeted breeding and cell wall engineering. This review offers a comprehensive integration of two key questions: (1) What are the current molecular regulatory mechanisms underlying lodging resistance-associated culm traits in rice? (2) How can these mechanisms be translated into effective breeding strategies?. How can these mechanisms be translated into effective breeding strategies? Unlike earlier reviews that focused on individual mechanisms, this synthesis integrates hormonal crosstalk, hierarchical NAC-MYB cascades, emerging evidence for epigenetic regulation, and QTL-based breeding into a unified conceptual framework centered on the DELLA-OsGSK2-BZR1 module. Our synthesis reveals that plant architecture is governed by extensive phytohormone crosstalk centered on the proposed DELLA-OsGSK2-BZR1 module, where gibberellin and brassinosteroid synergistically modulate elongation and thickening, whereas auxin, strigolactone, ethylene, and jasmonic acid fine-tune plant height and tillering. Major-effect loci, such as <i>SCM2/APO1</i> and <i>SCM3/OsTB1</i>, enable the concurrent improvement of strength and yield, with QTL pyramiding reported to increase culm diameter by up to ~39% in specific genetic backgrounds. Vascular bundle development and cell wall biosynthesis (lignin, cellulose, and hemicellulose) are coordinated by hierarchical NAC-MYB transcriptional cascades, with recent advances suggesting epigenetic regulation through DNA methylation, histone modification, and DELLA-mediated chromatin silencing in rice. We further discuss how these mechanistic insights may guide modern breeding through marker-assisted selection, genome selection, CRISPR/Cas9-mediated precision engineering, and ideotype design, providing a roadmap for developing next-generation lodging-resistant rice varieties.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1922614"},"PeriodicalIF":5.9,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149215971","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":"Environmental adaptation in wheat landraces: integrating physiological, molecular, and ecological mechanisms for climate-resilient agriculture.","authors":"Dali Vilma Francis, Malu Kishorkumar, Tarfa Abdulla, Abdul Jaleel, Habiba Alsafar, Ayesha Al Dhaheri, Shyam S Kurup","doi":"10.3389/fpls.2026.1942405","DOIUrl":"10.3389/fpls.2026.1942405","url":null,"abstract":"<p><p>Climate change poses an increasing threat to wheat production, particularly in arid and semi-arid regions where rising temperatures, water scarcity, soil degradation, and salinity interact to reduce crop productivity and stability. Wheat landraces constitute an invaluable reservoir of adaptive genetic diversity that has evolved through centuries of natural and farmer-mediated selection under resource-limited environments. This review provides a comprehensive synthesis of the mechanisms underlying environmental resilience in wheat landraces, integrating evidence across physiological, biochemical, hormonal, molecular, ecological, and agronomic scales within a single framework. It examines how coordinated adaptive strategies-including stress escape, avoidance, tolerance, and recovery-interact with root system architecture, rhizosphere processes, plant-microbe associations, and resource-use efficiency to enhance resilience under multiple abiotic stresses. Recent advances in genomics, transcriptomics, proteomics, metabolomics, pangenomics, and multi-omics integration have substantially improved our understanding of the genetic and molecular basis of environmental adaptation, creating new opportunities for the identification and deployment of resilience-associated traits. The review further discusses how these discoveries are being translated into climate-smart wheat improvement through genome-wide association studies, marker-assisted and genomic selection, speed breeding, gene editing, and artificial intelligence-assisted phenotyping. In addition, sustainable agronomic management, conservation strategies, farmer participation, and policy frameworks are considered as complementary components supporting the effective utilization of wheat landraces. By integrating adaptive biology, modern breeding technologies, sustainable agronomy, and socio-economic perspectives, this review presents a comprehensive framework for conserving and utilizing wheat landrace diversity to accelerate the development of climate-resilient wheat cultivars and strengthen global food and nutritional security under increasingly unpredictable climatic conditions.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1942405"},"PeriodicalIF":5.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13671108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149215968","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":"The adaptive landscape of the miR482 gene family in tomato is rugged.","authors":"Jessica Nicolai, Laura E Rose","doi":"10.3389/fpls.2026.1884466","DOIUrl":"10.3389/fpls.2026.1884466","url":null,"abstract":"<p><p>MicroRNA genes are important regulators of development and disease resistance in plants. Successful miR:target interactions rely on high sequence complementarity, usually permitting few to no mismatches. Exploring the concept of an adaptive landscape, we investigated how individual nucleotide changes in the miR482 gene family may alter the targeted repertoire of disease resistance gene (<i>R</i>-genes) in tomato. We predicted the target scores for all mutants accessible through individual point mutations <i>in silico</i> and compared them to the target scores of the original miR482 genes. Most mutations lead to a loss of targeting or lower likelihood of targeting. Only a few mutants showed improved target scores or gained new <i>R</i>-gene targets. Our results indicate that the miR482 genes may be optimized for targeting of their current <i>R</i>-gene repertoire. This suggests that the miR482 genes experience natural selection to maintain their current target repertoire and that the adaptive landscape is relatively peaked or rugged. This study has implications for understanding the adaptive process governing gene regulation in plants and the design of new miR:target pairs for generating novel, beneficial plant phenotypes.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1884466"},"PeriodicalIF":5.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216143","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":"Adaptive RGB-VNIR spectral fusion for non-destructive quality assessment of Canadian wild rice kernels.","authors":"Zhenhua Qiao, Jinting Zou, Dongxu Wang, Chunyun Wang","doi":"10.3389/fpls.2026.1973124","DOIUrl":"10.3389/fpls.2026.1973124","url":null,"abstract":"<p><strong>Introduction: </strong>Canadian wild rice kernels exhibit diverse postharvest variations, including breakage, discoloration, insect damage, and incomplete dehulling, making accurate and non-destructive classification important for screening and grading. However, RGB imaging may be insufficient for distinguishing kernels with subtle or overlapping visual characteristics, while visible-near-infrared (VNIR) spectra provide complementary information but contain substantial redundancies. Moreover, conventional multimodal fusion strategies based on direct concatenation or fixed weighting cannot readily adapt to sample-specific differences in modality contribution.</p><p><strong>Methods: </strong>To address these limitations, this study proposed a lightweight RGB-VNIR dual-modal fusion framework for fine-grained quality-category classification of Canadian wild rice kernels. A Coordinate Attention-enhanced MobileNetV3-Small was employed to extract spatial phenotypic features, while a 1D-GhostResNet-SE was used to model continuous VNIR spectral responses. A Cross-Modal Contribution-Aware Recalibration Fusion Head (CMCR-FH) was further introduced to adaptively regulate the contributions of the two modalities. The framework was evaluated on 2,048 paired RGB-VNIR samples from eight categories using stratified five-fold cross-validation.</p><p><strong>Results: </strong>The proposed framework achieved a mean accuracy of 98.68% ± 0.22% and a Macro-F1 score of 98.69% ± 0.22%, representing numerical improvements of 4.93 and 4.97 percentage points, respectively, over the baseline model, while requiring only 1.236 M parameters and 63.537 M multiply-accumulate operations. Ablation experiments demonstrated complementary contributions from visual feature enhancement, lightweight spectral representation, and sample-adaptive multimodal fusion, with CMCR-FH producing the largest numerical performance gain among the evaluated components. Grad-CAM++ and SHAP analyses further indicated that the model jointly utilized visible phenotypic abnormalities and class-dependent spectral response patterns for discrimination.</p><p><strong>Discussion: </strong>These results demonstrate that integrating complementary RGB and VNIR information with sample-adaptive fusion can improve fine-grained quality-category classification while maintaining low computational complexity. The proposed framework provides an effective balance among classification accuracy, computational efficiency, and interpretability, offering a promising approach for multimodal postharvest quality-category classification and grading of Canadian wild rice kernels.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1973124"},"PeriodicalIF":5.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149215964","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":"Quantitative trait loci and genome-wide association study identifies novel loci for plant height in pepper (<i>Capsicum</i> spp.).","authors":"Geon Woo Kim, Abhinandan S Patil, Kyeong-Seok Lee, Min-Young Kang, Hea-Young Lee, Solomon Abate Mekonnen, Muhammad Irfan Siddique, Koeun Han, Jelli Venkatesh, Jin-Kyung Kwon, Byoung-Cheorl Kang","doi":"10.3389/fpls.2026.1934965","DOIUrl":"10.3389/fpls.2026.1934965","url":null,"abstract":"<p><p>Plant height (PH) is a key trait that influences photosynthetic efficiency, early maturity, lodging resistance, and crop yield. The development of plant factories and mechanical harvesting has increased the demand for peppers (<i>Capsicum</i> spp.) with suitable plant architecture. In this study, we performed quantitative trait locus (QTL) analysis and genome-wide association studies (GWAS) to determine the genetic basis of PH in pepper. For QTL analyses, 117 F₂ individuals derived by crossing <i>C. annuum</i> 'Jeju' and 'Micropep' were used for PH analysis. Another population of 154 F₂ plants derived from crossing <i>C. annuum</i> 'Takanotsume' and 'Micropep' was used to analyze main stem length (MSL), number of main stem nodes (NMSN), and average node length of the main stem (ANLMS). Of these, 153 individuals were used for genetic map construction and QTL mapping. After genotyping-by-sequencing and filtering, 1,066 SNPs from the JM population and 1,446 from the TM population were retained for bin-map construction. QTL mapping was conducted with 451 bin markers for JM and 518 for TM. The GWAS used 25,126 SNPs on chromosomes 1-12 in the core collection. Composite interval mapping revealed QTLs for PH on chromosomes 1 and 11 in the JM population. QTL analysis in the TM population identified a significant NMSN QTL on chromosome 3 and a significant MSL QTL on chromosome 1. GWAS additionally identified significant SNPs for PH, NMSN, ANLMS, and MSL in the core collection. Genes near these SNPs were predicted, based on sequence similarity to characterized genes in other species, to include transcription factors, cell-wall-remodeling proteins, metabolic enzymes, and developmental regulators. Neither of the significant TM QTLs overlapped an LD block containing significant GWAS SNPs for the corresponding trait. These findings provide linkage regions, association signals, and predicted candidate genes for plant height and main stem traits in pepper, which will require validation in independent populations before they can be used in breeding.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1934965"},"PeriodicalIF":5.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149215976","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":"Edge-deployable greenhouse tomato cluster harvesting robot integrating YOLOv8n-BiFPN-WIoU and ROS-based autonomous control.","authors":"Fanzhao Meng, Zhen Wang, Huan Wang, Xiaohui Li, Xizhen Xu, Ningrong Zeng, Guoqing Wang, Dongpo Wei, Xuan Li, Yunqi Shi","doi":"10.3389/fpls.2026.1926482","DOIUrl":"10.3389/fpls.2026.1926482","url":null,"abstract":"<p><strong>Objective: </strong>To develop an edge-deployable greenhouse tomato cluster harvesting robot capable of accurate fruit detection, three-dimensional localization, autonomous navigation, and robotic harvesting under complex greenhouse conditions. The objective was to improve detection performance under fruit occlusion and variable illumination while maintaining real-time inference and stable robotic operation on embedded computing hardware.</p><p><strong>Methods: </strong>A lightweight YOLOv8n-based tomato detection model, termed YOLOv8n-BiFPN-WIoU, was developed by incorporating a bidirectional feature pyramid network and Wise-IoU loss to improve multiscale feature fusion and localization accuracy. An RGB-D vision system based on the Intel RealSense D435 was used for three-dimensional fruit localization. The robotic system integrated a rail-guided mobile platform, a six-axis manipulator, a dual-claw harvesting end-effector, and a ROS-based distributed control architecture. The vision model was optimized and deployed on an NVIDIA Jetson Nano using ONNX and TensorRT. Robot kinematics, hand-eye transformation, trajectory planning, and autonomous harvesting procedures were integrated to enable station-to-station operation and fruit harvesting.</p><p><strong>Results: </strong>The proposed YOLOv8n-BiFPN-WIoU model achieved a precision of 88.534%, recall of 89.377%, F1-score of 88.954%, mAP@0.5 of 92.338%, and mAP@0.5:0.95 of 71.368%. Compared with the baseline YOLOv8n model, these metrics improved by 2.143, 2.779, 2.460, 4.587, and 5.675 percentage points, respectively. The proposed model increased the parameter count from 3.2 million to 3.6 million and computational complexity from 19.6 to 22.4 GFLOPs. After ONNX and TensorRT optimization, the model achieved an inference speed of 21.1 frames per second at an input resolution of 640 × 640 with batch size 1 on the Jetson Nano. The robotic system achieved a mean positioning error of 3.70 ± 0.32 mm across five representative targets, demonstrating the feasibility of accurate visual localization and robotic positioning for greenhouse tomato harvesting.</p><p><strong>Conclusion: </strong>The proposed edge-deployable greenhouse tomato cluster harvesting robot integrates lightweight deep learning, RGB-D three-dimensional localization, ROS-based autonomous control, rail-guided mobile operation, and robotic harvesting into a unified system. The YOLOv8n-BiFPN-WIoU model improved tomato detection performance while maintaining real-time inference on embedded hardware, and the integrated vision and manipulation system demonstrated accurate positioning suitable for autonomous harvesting. The results indicate that the proposed architecture provides a practical foundation for intelligent greenhouse tomato harvesting under complex visual and operational conditions.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1926482"},"PeriodicalIF":5.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149215984","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":"Water-fertilizer coupling balances yield and the input-related carbon footprint in double-cropping greenhouse Hami melon.","authors":"Zhenliang Song, Ming Hong, Quanjiu Wang, Han Guo, Fanfeng Dang, Guangning Wang, Zhaoxin Liu, Liang Ma","doi":"10.3389/fpls.2026.1917283","DOIUrl":"10.3389/fpls.2026.1917283","url":null,"abstract":"<p><p>Water-fertilizer management is central to balancing productivity and environmental performance in greenhouse agriculture.</p><p><strong>Methods: </strong>In a spring-autumn greenhouse Hami melon system in Xinjiang, five irrigation (W1-W5) and three fertilization (F1-F3) levels were combined. Life cycle assessment quantified the input-related carbon footprint, plant carbon storage was measured, and structural equation modeling (SEM) evaluated associations among inputs, biomass, yield and carbon-footprint intensity.</p><p><strong>Results: </strong>Production inputs generated 12.77 t CO₂-eq ha^-1, with fertilizers (54.52%) and plastic film (23.16%) as the main sources. W4F3 achieved the highest yields (75.74 and 52.79 t ha^-1 in spring and autumn, respectively) and plant carbon storage (up to 35.78 g C plant^-1). In the fitted SEM, yield was strongly negatively associated with product carbon-footprint intensity (β = -1.253, P < 0.001), whereas area-based emissions were positively associated with it (β = 0.729). Under low irrigation, increasing fertilization did not consistently increase yield, while adequate water enabled fertilizer inputs to be converted more effectively into biomass and yield.</p><p><strong>Conclusion: </strong>Under the tested sandy-soil, cultivar and one-year greenhouse conditions, W4F3 (approximately 3420 m³ ha^-1 irrigation and 863 kg ha^-1 fertilizer) provided the most favorable balance between yield and product carbon-footprint intensity.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1917283"},"PeriodicalIF":5.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216140","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":"Selection-aware metrics provide complementary decision-level information in plant genomic prediction.","authors":"Xueyang Wang, Jun Yan","doi":"10.3389/fpls.2026.1951182","DOIUrl":"10.3389/fpls.2026.1951182","url":null,"abstract":"<p><p>Breeding programs increasingly need prediction systems that support reliable candidate selection across heterogeneous environments, which makes cross-environment genomic prediction an important component of multienvironment crop improvement. However, model evaluation still relies heavily on Pearson's correlation coefficient (<i>r</i>), a global summary that may not fully capture which top-ranked candidates would be advanced. Using maize and switchgrass datasets as focused case studies, we compared global predictive ability with phenotype-referenced candidate diagnostics across within-environment and directed cross-environment evaluations of rrBLUP/ridge, Light Gradient Boosting Machine (LightGBM), a multilayer perceptron, and mixed-effect deep neural network (MeNet). Rather than proposing a new selection metric, we used a conditional comparison framework that distinguishes nominal metric reversal from strict decision-level discordance among models with similar Pearson's <i>r</i>. The <i>r</i>-best and candidate-metric-best models disagreed in 25%-55% of evaluation contexts, but large differences in candidate metrics among near-<i>r</i> models (|Δ<i>r</i>| ≤ 0.02) were uncommon. Nevertheless, models with nearly identical <i>r</i> did not always prioritize the same candidates: LightGBM and rrBLUP/ridge shared, on average, 72.9% of the top 5% candidates in maize and 68.8% in switchgrass. These results indicate that Pearson's <i>r</i> remains informative for broad model comparison but does not fully describe decision-level behavior. Phenotype-referenced candidate diagnostics are therefore best used as targeted complements when models have similar predictive ability, selection intensity is high, or candidate identity is consequential across environments.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1951182"},"PeriodicalIF":5.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216057","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":"Evaluating urea-based nitrogen fertilization practices on greenhouse tomato growth and nitrate leaching in cold regions.","authors":"Changqing Li, Ziyi Wu, Haixu Wang, Shuting Li, Xiaojing Wang, Jiqing Wang, Baoyue Zhang","doi":"10.3389/fpls.2026.1925562","DOIUrl":"10.3389/fpls.2026.1925562","url":null,"abstract":"<p><strong>Introduction: </strong>There are serious problems in agricultural production, such as low nitrogen utilization rate and nitrate leaching. This study examined how different fertilization practices affect the yield, quality, and soil nitrate leaching of greenhouse tomatoes in the cold regions. The goal was to provide technical support for simple, high-quality, and efficient greenhouse vegetable production in these areas.</p><p><strong>Methods: </strong>Under integrated water-fertilization conditions in the cold regions, six fertilization treatments were applied: No nitrogen fertilizer, Conventional farmer practice (360 kg ha^-1 N), Optimized nitrogen application (240 kg ha^-1 N), Slow-release urea A mixed with conventional urea at 7:3 (240 kg ha^-1 N), Slow-release urea B mixed with conventional urea at 7:3 (240 kg ha^-1 N), Conventional urea mixed with modified slow-release polymer material (240 kg ha^-1 N).</p><p><strong>Results: </strong>The results showed that optimized nitrogen treatments improved plant height and SPAD values, which helped increase yield. Among them, conventional urea mixed with modified slow-release polymer material had the highest yield. Optimized fertilization treatments also improved quality. Agronomic efficiency and partial factor productivity under optimized nitrogen treatments were 59.08%-231.95% higher than under conventional farmer practice. These treatments also reduced nitrate accumulation in the 60-100 cm soil layer. Using a multi-objective evaluation model based on fuzzy mathematics membership function, conventional urea mixed with modified slow-release polymer material was identified as the best option.</p><p><strong>Discussion: </strong>In summary, under integrated water-fertilization conditions, applying conventional urea mixed with modified slow-release polymer material can maintain tomato yield and quality while reducing soil nitrate leaching. This method meets the demands of modern agriculture and environmental protection.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1925562"},"PeriodicalIF":5.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216013","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":"Deciphering plant stress resilience in different millet crops: from multi-omics discoveries to predictive breeding for climate-resilient crop enhancement.","authors":"Mahalakshmi Mani, Poonguzhali Subramanian, Sourav Paramanik, Madhusmita Dishri","doi":"10.3389/fpls.2026.1971866","DOIUrl":"10.3389/fpls.2026.1971866","url":null,"abstract":"<p><p>Global climatic and abiotic stresses severely affect agricultural production, while millets are stress-tolerant and nutritious cereal crops adapted to diverse environments. Although single-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, ionomics, and phenomics, have substantially advanced our understanding of individual components of plant stress responses, each approach captures only one molecular layer and therefore cannot fully resolve the interconnected regulatory networks that govern complex stress adaption. The challenge now is to bring together these widely varied molecular data to make them biologically meaningful and to be able to predict genotype-phenotype relationships under varying field conditions. This review critically covers recent developments in multi-omics technologies that have been used to uncover abiotic stress tolerance mechanisms, and how they can be complementary to identify stress-responsive genes, regulatory pathways, stress markers, and adaptive metabolic processes. Additionally, the review covers recent developments in AI and ML integration with multi-omics data, predictive modeling, and precision breeding to enhance the identification of better genotypes and breeding decisions. Finally, future prospects for the integration of multi-omics, AI analytics, digital phenotyping and breeding pipelines for next-generation cultivars of millets for future climate change are outlined, which will enable the improvement of current cultivars with higher resilience, yield and nutritional value, while simultaneously supporting sustainable agriculture.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"17 ","pages":"1971866"},"PeriodicalIF":5.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216040","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}