Plant Methods最新文献

Background: Bread wheat (Triticum aestivum L.) is a vital global crop, supplying 20% of the protein in the human diet. Improving its productivity and resilience, particularly under water-limited conditions, is a major breeding priority. Genomic selection offers a promising approach to accelerate genetic gains by predicting complex traits using genome-wide marker data. This study evaluated the performance of various genomic selection (GS) models in predicting key agronomic traits under contrasting well-watered (WW) and water-stressed (WS) conditions, with the goal of enhancing drought adaptation in wheat breeding programs.

Results: A panel of 179 wheat lines was evaluated for grain yield, yield components, and grain protein content. Models were trained on data from well-watered and water-stressed regimes independently, as well as on combined data from both conditions. Predictive approaches included linear models (Ridge Regression Best Linear Unbiased Prediction and Bayesian methods), semi-parametric models (Reproducing Kernel Hilbert Space Regression), and machine learning algorithms (Random Forest, Support Vector Machine, and Extreme Gradient Boosting). Ridge regression consistently delivered strong performance across all traits and conditions, with mean r_MG of 0.70 (water-stressed), 0.64 (well-watered), and 0.65 (combined). Machine learning models, especially Random Forest and Extreme Gradient Boosting, performed competitively for complex traits such as grain yield and thousand kernel weight. Random Forest achieved a r_MG of 0.81 for grain yield and 0.73 for thousand kernel weight under well-watered conditions. Trait stability was observed under well-watered conditions for thousand kernel weight and plant height, supported by moderate heritability estimates (0.69-0.74). Cross-validation comparisons showed consistent model performance across validation schemes, with leave-one-out cross-validation offering slightly improved accuracy for select traits and models. Notably, models trained under water-stressed conditions generalized better when tested on well-watered data than the reverse, highlighting the value of diverse training environments.

Conclusions: Genomic selection models, particularly ridge regression and machine learning approaches, demonstrated reliable predictive performance across environments and traits. Incorporating data from multiple environmental conditions improves prediction accuracy and supports the development of drought-resilient wheat lines. These results reinforce the utility of genomic selection in modern wheat breeding programs for enhancing both productivity and stress tolerance.

Early-stage, accurate and high-throughput phenotyping‌ through leaf area estimation is ‌critical‌ for future rapeseed breeding, but faces ‌two key constraints‌: expensive data annotation and persistent challenge of leaf occlusion. To address these issues, we present a ‌data-efficient‌ deep learning framework using smartphone-captured top-down RGB images for rapeseed leaf area quantification. Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated dataset of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning method. This pre-trained model is then fine-tuned on a custom rapeseed dataset using a novel Canopy-Mix data augmentation technique to handle fragmented views analogous to occlusion, and a hybrid loss function combining Smooth L1 and Log-Cosh for robust convergence. Through rigorous 5-fold cross-validation, our proposed model achieved strong predictive performance (Coefficient of Determination, R[Formula: see text]=0.805). Moreover, the predicted leaf area demonstrated a remarkably strong correlation with both fresh weight (r=0.900) and dry weight (r=0.885). The model significantly outperformed a range of baselines, including models trained from scratch, those pre-trained on ImageNet, and a heuristic method based on manually annotated bounding boxes. Ablation studies confirmed the essential contribution of each component, while qualitative analysis of attention maps demonstrated the model's ability to precisely localize the leaf canopy and ignore background distractors. This study demonstrates that domain-specific self-supervised pre-training offers a powerful solution to overcome data limitations in agricultural vision, providing a robust and scalable tool for non-destructive phenotyping that can potentially accelerate the rapeseed breeding cycle.

Accurate measurement of key phenotypic traits, including the horizontal and vertical diameters, the weights of both fruit and pit, is essential for the selection of elite litchi cultivars and the advancement of breeding research. Manual measurement, however, is laborious, inefficient, and subjective, highlighting the urgent need for automated and precise phenotyping tools. Unlike apples, mangoes, and grapes, litchi combines a spiny, highly variable pericarp (heterogeneous areoles/tubercles across cultivars) with diverse seed morphology (including irregular, wrinkled aborted seeds), thereby increasing the difficulty of semantic segmentation and biasing diameters and weight estimation. This study presents LitchiPhenoNet, a multimodal learning framework for litchi phenotypic analysis that employs a dual-branch architecture integrating RGB (color/texture) and depth (spatial/structural) information. Experiments were conducted on an RGB-D dataset comprising 1,198 image pairs (1280×720) across 10 cultivars, using a stratified train/test split of 958/240 pairs by cultivar. To address inherent semantic and scale inconsistencies between modalities, the framework incorporates the RD-Fusion module for precise cross-modal feature extraction, improving robustness under complex and variable pericarp surfaces. Comparative experiments show that LitchiPhenoNet consistently outperforms leading YOLO-based models, achieving millimeter-level diameter estimation with coefficients of determination approaching 0.98 and mean errors within 2 mm. For weight estimation, gram-level precision is attained across whole fruit, pit, and pulp, with coefficients of determination up to 0.98 and mean errors comparable to repeated manual measurements. By handling fine-scale surface relief and cross-cultivar variability, the framework is readily extensible to other textured fruits and scalable for high-throughput phenotyping in breeding programs. Collectively, these results demonstrate that LitchiPhenoNet provides an efficient, reliable, and accurate solution for quantifying litchi phenotypic traits, substantially advancing the objectivity and efficiency of phenotypic analysis and breeding selection.

Background: Wheat ranks third among cereal crops in terms of global production, and its demand is expected to increase as the human population grows. Plant breeding can increase crop production without burdening natural resources, and one way to accelerate genetic gain is through shortening breeding cycles with speed breeding (SB). Speed breeding protocols for winter wheat have been adapted by adding a vernalization phase to existing spring wheat protocols. Although a protocol for the vernalization phase was previously developed, it was not tested for genotypes grown in the Midwest US, which may have higher vernalization requirements. The transition from vegetative to reproductive stages in winter wheat depends mainly on photoperiod, vernalization temperature, and vernalization length, which determines the time needed to reach flowering. Optimizing vernalization under SB in a greenhouse setting is important for applications in breeding programs. Our objectives were to develop a speed breeding protocol for winter wheat that meets the vernalization requirements of all genotypes and to evaluate the interaction between vernalization temperature and sowing depth.

Results: A significant reduction in the time to flowering via speed breeding was achieved. Compared with normal vernalization, high-throughput vernalization adds on average ten days to the time to harvest. A shallow planting depth results in maturity five days earlier than a deep planting depth.

Conclusions: A combination of speed breeding, shallow planting, and high-throughput vernalization will shorten the breeding cycle by 22 days per generation or 44 days per year compared to normal greenhouse conditions. This system is suitable for genotypes with high vernalization requirements and can be combined with high-throughput systems.

Rubber powdery mildew, caused by the fungal pathogen Oidium heveae Steinm., is a prevalent disease in rubber plantation regions worldwide. This disease significantly impacts the growth and yield of rubber trees, leading to substantial economic losses within the rubber industry. In recent years, due to climate change and adjustments in planting structures, both the geographical spread and severity of the disease have increased. Consequently, there is an urgent need to develop efficient remote sensing monitoring methods for early warning and effective management. To fully exploit disease information within hyperspectral data, this study first extracted spectral features using three methods: spectral mathematical transformations (MT), continuous wavelet transformation (CWT), and vegetation indices (VIs). Subsequently, correlation analysis (CA), least absolute shrinkage and selection operator (LASSO), and principal component analysis (PCA) were employed to select optimal features from each set, resulting in the construction of nine independent basic feature sets. To further enhance model performance, features selected by these three strategies (CA, LASSO, and PCA) were combined to form three fused feature sets. Finally, all basic and fused feature sets were input into a Random Forest (RF) model to evaluate the impact of different feature combinations on the accuracy of disease severity classification. The results revealed that, among the spectral data processing methods, CWT performed the best. Among the feature selection methods, PCA was the most effective. The feature fusion methods significantly improved model performance. Specifically, the fused feature set based on PCA selection (PCA_ALL) achieved the highest classification accuracy, with an overall accuracy (OA) of 98.89% and a Kappa coefficient of 0.98. This OA was 8.89% higher than that of CA_ALL and 4.42% higher than the best-performing basic feature set (PCA_CWT). This study establishes a remote sensing monitoring framework for classifying rubber leaf powdery mildew severity based on the fusion of multi-dimensional hyperspectral features. This framework not only lays a technical foundation for the transition of the natural rubber industry from experience-based control to intelligent decision-making but also provides crucial parameters for large-scale dynamic disease monitoring using UAV and satellite platforms.

In agricultural images acquired under natural conditions, pomegranate fruits are often partially occluded by leaves and branches, resulting in missing structural information that compromises the accuracy of yield estimation and automated harvesting. To overcome the challenges of recovering structural integrity in occluded agricultural imagery, we propose the Conditional Segmentation-guided Diffusion Network (CSD-Net). CSD-Net is a lightweight, unified framework, representing the first conditional diffusion model specifically designed for the joint tasks of pomegranate image completion and segmentation. CSD-Net aims to address the structural fidelity limitations of traditional completion methods. It utilizes a shared encoder, a segmentation branch, and an RGB diffusion branch. Crucially, the network leverages the segmentation mask as a key structural prior condition to guide the diffusion generation process. This innovative conditional guidance mechanism ensures high-fidelity reconstruction of fruit structures while maintaining spatial and textural consistency. Experimental results demonstrate that CSD-Net substantially outperforms conventional methods across metrics, achieving 30.37 dB in PSNR and 0.9490 in SSIM. Furthermore, its model size is only 117 MB, striking an effective balance between high completion quality and inference efficiency. This study offers a novel and highly effective solution for mitigating occlusion issues in agricultural visual perception tasks. Upon acceptance of this paper, the source code will be made publicly available at https://github.com/zdkd/PCSN .

Background: Pyricularia oryzae is a major fungal pathogen responsible for significant yield losses in rice. In recent years, diverse pathotypes have emerged as threats to other economically important grasses, including ryegrass, oats, wheat and foxtail millet. Research on host-pathogen interactions involving this species requires reliable spore production for inoculation. However, as a hemibiotrophic pathogen, P. oryzae often sporulates poorly on artificial media and typically requires specialized two-stage protocols for consistent spore production. Although several such methods have been developed, all were optimized for rice-derived strains and have not been systematically evaluated across strains from other hosts. There is also a practical need for a simple setup that allows advance preparation and frozen storage of spore stocks. Therefore, we developed a new two-stage filter paper method and compared it with four published protocols across 23 strains from 13 grass hosts.

Results: Comparative analysis showed strain specific differences in sporulation across methods, with no consistent link to phylogenetic lineage. The filter paper method reached an inoculum-competent concentration (defined here as [Formula: see text] spores/mL, suitable for routine spray inoculation) without any concentration step in 18 of 23 strains (78%), compared with TARI 16/23 (70%), IRRI 15/23 (65%), corn grain 14/23 (61%), and mycelial mat 3/23 (13%). Spores dried on filter paper were ready to use upon thawing and retained germination with no change in virulence after six months of storage at -40 [Formula: see text]C. Step by step protocols with illustrations are provided for all five methods, together with practical guidance for choosing a method based on laboratory conditions, available resources, and research objectives.

Conclusions: This study provides a comparative evaluation of two-stage sporulation methods for Pyricularia strains across diverse grass hosts. Among the five methods, the newly developed filter paper method shows the broadest applicability across strains while maintaining yields comparable to established protocols. It can be prepared for frozen storage and used directly after thawing, enabling advance preparation and bulk stocking of inoculum for virulence profiling, resistance breeding, and disease management. These findings are particularly relevant for laboratories in regions that are affected by, or at risk of, outbreaks caused by this pathogen.

Background: Black huckleberry (Vaccinium membranaceum) is a native fruit species of high nutritional, medicinal, ecological, and economic value. The black huckleberries, abundant in bioactive compounds, offer significant antioxidants and anti-inflammatory effects and play a key role in maintaining wildlife and forest ecosystems. Despite its importance, protoplast isolation and gene editing have not been reported in this species. These techniques are essential for functional genomics and crop improvement, but the recalcitrant nature of this species, complex genome, and variable ploidy present significant challenges for cellular and molecular manipulation. This study aimed to establish a reliable protocol for efficient mesophyll protoplast isolation and transient gene expression in V. membranaceum using in vitro-grown leaves.

Results: A systematic optimization of enzyme composition, osmotic concentration, antioxidant supplementation, and pH was undertaken to enhance protoplast yield and viability in V. membranaceum. The optimized enzymatic combination of 2% cellulase R-10, 1% hemicellulase, 1% Macerozyme R-10, and 1.5% pectinase facilitated efficient cell wall degradation while maintaining structural integrity. The inclusion of 0.6 M mannitol ensured osmotic stability, and 1% PVP-40 effectively suppressed phenolic oxidation, significantly improving protoplast viability. A near-neutral pH of 5.8 supported optimal enzyme activity without inducing cellular damage. Under these optimized conditions, 14 h enzymatic digestion produced 7.20 × 10⁶ protoplasts g⁻¹ FW with 95.1% viability. Subsequent optimization of PEG-mediated transformation identified 40% PEG-4000 with 30 µg plasmid DNA as the most effective combination, achieving 75.1% transient expression efficiency. Nuclear localization of GFP-tagged proteins, confirmed by DAPI staining, validated the robustness of the optimized system.

Conclusions: This study presents a standardized, PVP-40-enhanced protocol for mesophyll protoplast isolation with notable yield and viability in V. membranaceum, supporting efficient transient gene expression. The method provides a robust platform for functional genomics, gene editing, and biotechnological applications in this underutilized species and other related plant species.

Plant growth and development strongly depend on the uptake of soil minerals and their distribution within plants. Various electrophysiological techniques have been developed to study these ion transport processes and the role of ions in signal transduction pathways. An important non-invasive method is provided by Scanning Ion-Selective Electrodes (SISE), which are used to detect ion fluxes. These SISE-measurements depend on software that coordinates the continuous electrode movement between two positions, as well as data collection and analysis. We developed two LabView-based programs; the SISE-Monitor and SISE-Analyser that enable ion flux recordings and their analysis, respectively. These applications are freely available, both as windows-executable files that enable routine measurements, as well as the LabView source code that allows insights into the routines used for measurement and analysis. Moreover, the source code can be used to develop new functions, such as the combined measurement of extracellular ion fluxes with SISE and cellular ion concentrations with fluorescent dyes, or proteins.

Plant reproduction involves dynamic spatiotemporal changes that occur deep within maternal tissues. In ovules of Arabidopsis thaliana (A. thaliana), one of the two synergid cells degenerates at fertilization, while the fertilized egg cell (zygote) undergoes directional elongation followed by asymmetric division to initiate embryonic patterning. However, morphological analysis of these events has been hampered by the limitations of conventional cell wall staining, which fails to label cells lacking complete walls, and by the requirement for transgenic fluorescent reporters to visualize cell outlines. Here, we report that the membrane-specific fluorescent dye FM4-64 readily permeates ovules, allowing clear visualization of reproductive cell morphology both before and after fertilization. This staining method supports high-resolution time-lapse imaging and quantitative analysis of early embryogenesis in living tissues. Importantly, it is applicable not only to the angiosperm A. thaliana but also to the liverwort Marchantia polymorpha (M. polymorpha) and the fern Ceratopteris richardii (C. richardii), enabling the visualization of live reproductive cell structures within maternal tissues and revealing fertilization-associated morphological changes. This simple and robust method thus provides a valuable tool for spatiotemporal and quantitative analyses of reproductive processes across a broad range of plant species, without the need to generate transgenic lines.