Plant Methods最新文献

We report the successful cryopreservation of three economically important Rubus viruses: raspberry bushy dwarf virus (RBDV), black raspberry necrosis virus (BRNV), and Rubus yellow net virus (RYNV), using shoot tip cryopreservation in four raspberry cultivars. Virus-infected shoot tips (approximately 1.0 mm in length) containing 3-4 leaf primordia (LPs) were cryopreserved using the droplet-vitrification technique. In the cultivars 'Zlatá Královna (ZK)' and 'Tulameen (TUM)', over 90% of shoot tips survived, and more than 90% regenerated into whole shoots. All three viruses were successfully preserved in the cryopreserved tissues, with recovery rates varying depending on virus type and cultivar: RBDV was recovered at rates of 86% in 'ZK' and 87% in 'TUM'; BRNV at 66% in 'ZK' and 45% in 'TUM'; and RYNV at 96%, 94%, and 86% in 'Fairview', 'Stiora', and 'ZK', respectively. To investigate viral localization in shoot tips, in situ hybridization was used. RBDV and RYNV infected a broad range of meristematic tissues, including the apical dome and LPs, whereas BRNV showed a more limited distribution. Virus distribution varied not only among virus species but also across raspberry cultivars, suggesting genotype-specific patterns of virus localization. Post-cryopreservation viral activity was verified using micrografting and aphid transmission assays. RBDV, BRNV, and RYNV were all successfully transmitted to healthy plants via micrografting, indicating the preservation of viral infectivity. Furthermore, BRNV was effectively transmitted by large raspberry aphids from cryopreserved materials, confirming vector-mediated transmission capacity post-thaw. Overall, this study demonstrates that shoot tip cryopreservation via droplet-vitrification is a reliable and effective strategy for preservation of biologically active Rubus viruses. This approach offers a valuable biotechnological tool for virus maintenance in support of diagnostic, breeding, and virology research.

Background: Field-scale assessment of chili leaf curl complex presents a significant diagnostic challenge, as both chili leaf curl virus (ChiLCV) and mite infestations produce visually overlapping symptoms difficult to distinguish by untrained personnel. This diagnostic confusion frequently leads to inappropriate application of either insecticides or acaricides, resulting in economic losses and environmental concerns. To address this issue, we propose SCA-MobiPlant, an improved MobileNetV3-Small model integrated with a novel multistage Squeeze-and-Excitation Coordinate Attention (SCA) fusion mechanism, designed for accurate differentiation of these apparently similar symptoms and precise field assessment of the disease.

Results: The proposed model effectively focuses on subtle diagnostic features including leaf texture, petiole elongation, and irregular curling patterns to achieve reliable classification. The multistage SCA fusion module demonstrated superior performance, achieving 99.64% accuracy, 99.61% precision, 99.64% recall, and 99.62% F1-score through K = 5 cross-validation, outperforming other attention modules such as the Convolutional Block Attention Module (CBAM) and Coordinate Attention (CA). Gradient-Weighted Class Activation Mapping (Grad-CAM) provided visual interpretability of the model's decision-making process. Comparative evaluation against state-of-the-art architectures, including EfficientNetB0, ResNet50, VGG19 and YOLO advanced series, confirmed the computational efficiency of the proposed model for mobile deployment.

Conclusions: The final system, termed SCA-MobiPlant, has been successfully implemented on smartphones, along with a Disease Incidence (DI) calculation module, enabling rapid and accurate field assessment of the disease. This facilitates appropriate intervention strategies while minimizing unnecessary pesticide use. The study highlights the potential of lightweight, attention-enhanced models for real-world plant disease diagnostics, particularly in resource-constrained agricultural settings.

Background: Calcium oxalate (CaOx) crystals are commonly found in many plant species. These crystals vary in distribution and morphology and to elucidate their role in plants multiple methods have been applied. Raman imaging and polarized light microscopy (PLM) easily visualize the crystals within plant tissues, but both methods are limited in spatial resolution by the diffraction of light. To unravel the distinctive shape and morphology of CaOx crystals down to the nanoscale and how they are embedded within cells, high resolution scanning electron microscopy is needed. To grasp the full potential of multiple methods in CaOx studies, a novel and easy-to-build correlative sampling approach is presented on different nut species (pecan (Carya illinoinensis), Turkish hazel (Corylus colurna) and black walnut (Juglans nigra)), including soft tissues (young developmental stages) as well as hard tissues (mature nutshells).

Result: Young seed coat tissues as well as mature nutshells included distinct morphological CaOx features, like druses and prismatic crystals. By Raman imaging the chemical composition of all investigated crystals was verified as calcium oxalate monohydrate (COM) and Raman band intensity changed according to crystal plane orientation with respect to incident laser polarisation. Calcium oxalate dihydrate (COD) was only found in the young C. illinoinensis seed coat and was restricted to a few pixels adjacent to cell walls. These thin cell walls were identified as pectin-rich, while in the mature nutshells the crystals were surrounded by thicker and highly lignified cell walls. The Raman and light microscopy results were correlated with SEM images, which gave additional information on crystal surface structure and/or internal porosity on the nanoscale.

Conclusion: The presented correlative approach preserved the structural integrity of crystals and cellular structures during cutting and transferring between microscopes. Analysing exactly the same sample (position) by Raman, polarized light microscopy and SEM opens the view on the distribution within tissues and cells as well as the molecular structure of the crystals and adjacent cell structures. Such a comprehensive in-situ characterization paves the way for a better understanding of mineralization processes of different minerals in all kinds of biological tissues.

Background: The genomic and evolutionary study of allopolyploid organisms involves multiple copies of homeologous chromosomes, making their assembly, annotation, and phylogenetic analysis challenging. Bioinformatics tools and protocols have been developed to study polyploid genomes, but sometimes require the assembly of their genomes, or at least the genes, limiting their use.

Results: We have developed AlloSHP, a command-line tool for detecting and extracting single homeologous polymorphisms (SHPs) from the subgenomes of allopolyploid species. This tool integrates three main algorithms, WGA, VCF2ALIGNMENT and VCF2SYNTENY, and allows the detection of SHPs for the study of diploid-polyploid complexes with available diploid progenitor genomes, without assembling and annotating the genomes of the allopolyploids under study. AlloSHP has been validated on three diploid-polyploid plant complexes, Brachypodium, Brassica, and Triticum-Aegilops, and a set of synthetic hybrid yeasts and their progenitors of the genus Saccharomyces. The results and congruent phylogenies obtained from the four datasets demonstrate the potential of AlloSHP for the evolutionary analysis of allopolyploids with a wide range of ploidy and genome sizes.

Conclusions: AlloSHP combines the strategies of simultaneous mapping against multiple reference genomes and syntenic alignment of these genomes to call SHPs, using as input data a single VCF file and the reference genomes of the known or closest extant diploid progenitor species. This novel approach provides a valuable tool for the evolutionary study of allopolyploid species, both at the interspecific and intraspecific levels, allowing the simultaneous analysis of a large number of accessions and avoiding the complex process of assembling polyploid genomes.

Background: Wheat diseases significantly impair production efficiency and grain quality in the wheat industry. In recent research, deep learning techniques have been widely applied to plant disease detection. However, wheat disease images collected in field conditions often face complex backgrounds and diverse lesion shapes, making accurate disease classification difficult. In real-world applications, agricultural disease recognition systems must also deal with limited computational resources and edge device constraints, emphasizing the need for lightweight methods.

Results: To solve these challenges, this paper introduces a lightweight dual-stream learning (LDSL) framework for wheat disease detection. The framework adopts a unique global-local dual-stream architecture that combines global semantic understanding with local discriminative analysis. The global learning stream extracts comprehensive semantic features and generates saliency maps to highlight key regions, while the local learning stream performs fine-grained inspection of these regions using a novel dynamic-static dual attention (DSDA) mechanism. Additionally, a Kullback-Leibler (KL) divergence perturbation strategy is implemented during training to boost the LDSL framework's robustness in noisy and complex settings. Experimental results show that the proposed LDSL framework achieves an accuracy of 94.44%, a precision of 94.47%, a recall of 94.44%, and an F1-score of 94.45%, outperforming several mainstream classification models in wheat disease recognition, such as ConvNeXt-T (92.66% accuracy, 92.69% precision, 92.66% recall, and 92.63% F1). The proposed LDSL framework is lightweight, using only 4.41 M parameters and 1.71G FLOPs. On the NVIDIA Jetson Orin Nano, it requires just 15.99 MB of storage, 39.49 MB of peak memory, and achieves an inference latency of 234.76 ms/image, demonstrating good potential for real-world deployment.

Conclusions: This study provides a novel detection framework for wheat disease research, which significantly improves various classification metrics. With low parameter and computation costs, the framework demonstrates good potential for practical deployment.

Pepper is an economically important crop. Owing to its recalcitrance to genetic transformation, virus-induced gene silencing (VIGS) is currently the major technique available for validating gene function in pepper. However, the low efficiency and difficulty of silencing genes in reproductive organs remain major challenges in pepper VIGS studies. To address these limitations, we developed an optimized VIGS system by structure-guided truncation of the Cucumber mosaic virus 2b (C2b) silencing suppressor. A silencing suppression assay revealed that the C2b^N43 mutant retained systemic silencing suppression while abrogated local silencing suppression activity in systemic leaves. The engineered TRV-C2b^N43 system significantly enhanced VIGS efficacy in pepper, providing a powerful tool for functional genomics studies in pepper. By leveraging transcriptomic profiles, we identified CaAN2, an anther-specific MYB transcription factor, whose suppression via TRV-C2b^N43 perturbation resulted in coordinated downregulation of structural genes in anthocyanin biosynthesis pathway and abolished anthocyanin accumulation in anthers establishing its essential regulatory role in pigmentation. This study validated and provided mechanistic insight for a further optimized VIGS system in pepper.

Fusarium head blight (FHB), caused by the Fusarium species complex, significantly endangers wheat yield and safety. Accurate and timely assessment of FHB epidemic level in the field is crucial for effective disease management. However, the complex environment and indistinct edges of diseased areas present substantial challenges in distinguishing between healthy and diseased ears, thereby impacting the accuracy of FHB epidemic level detection. This study proposes EBS-YOLO, a novel Edge-Optimized Bidirectional Spatial Feature Augmentation YOLO Network, specifically designed for the rapid and precise determination of FHB epidemic levels at the canopy level. The Focal-Edge Selection Module (FSM) within the backbone replaces original C2f module to enhance edge feature representation and facilitate multi-scale feature extraction. Furthermore, the Dual Spatial-Connection Feature Pyramid Network (DSCFPN), integrating Global-to-Local Spatial Aggregation (GLSA) with bidirectional pyramid interaction, balances global and local feature acquisition while optimizing the feature fusion mechanism. This design enables the model to effectively handle occlusions, scale variations, and complex environments. Experimental results demonstrate substantial improvements over eight comparative models in detecting healthy and diseased wheat ears, achieving mean Average Precision (mAP) of 86.1% and 82.9%, respectively. Notably, the model achieved a mean accuracy of 94.7% in detecting FHB epidemic levels through rigorous spatiotemporal validation using datasets collected from independent fields across different years, underscoring its robust generalization capability. Characterized by its low complexity and lightweight design, EBS-YOLO features a parameter count of 2.05 M, 7.4 GFLOPs, and a model size of 5.0 MB, making it an efficient approach for real-time FHB epidemic level detection.

Background: Flavescence dorée (FD) is one of the most damaging grapevine diseases in Europe, caused by the quarantine-listed Grapevine flavescence dorée phytoplasma (FDp). Given the absence of resistant cultivars and curative treatments, effective disease control relies on early and accurate FDp detection. PCR-based diagnostics are the gold standard, but their accuracy depends on DNA extraction quality. Grapevine tissues contain PCR inhibitors like polysaccharides and polyphenols, complicating DNA isolation. While CTAB methods yield high-quality DNA, they are time-consuming, and commercial kits provide purer but often lower DNA yields at high costs. A rapid and optimized DNA extraction method for FDp detection is urgently needed.

Results: We developed the "HotShot Vitis" (HSV) method, a modified HotSHOT protocol optimized for grapevine tissues. HSV was benchmarked against the CTAB method and a commercial silica membrane kit. Although HSV showed limitations in DNA quantification due to buffer composition, it efficiently extracted DNA suitable for amplifying the grapevine trnL-F gene and detecting FDp by two qPCR assays. DNA extracted by HSV also supported molecular typing and sequencing of FDp 16 S rRNA and map genes, performing comparably to CTAB and the commercial kit. Importantly, HSV reduced the extraction time to about 30 min, significantly faster than the CTAB (2 h) and kit (40 min) methods.

Conclusions: HSV is a fast, reliable, and chemically low-risk DNA extraction method for FDp detection and characterization in grapevine. Its efficiency and simplicity make HSV ideal for large-scale diagnostics and early disease management.

The photosynthetic pigments - chlorophyll a (Chl a), chlorophyll b (Chl b), and carotenoids (Car) - in juvenile ginkgo leaves are crucial for growth monitoring as they reflect physiological status and directly influence the biosynthesis of bioactive compounds such as flavonoids and terpene lactones. Traditional pigment measurement methods (acetone/ethanol extraction, SPAD, etc.) are inadequate for large-scale dynamic monitoring and high-throughput phenotyping analysis. To address this, this study developed a non-destructive prediction model for Chl a, Chl b, and Car contents in ginkgo seedlings using hyperspectral imaging combined with machine learning algorithms, which is applicable to seedlings with different genetic backgrounds and at various color development phases. A total of 3,460 seedlings from 590 families, sourced from ancient trees across 19 provinces in China, were analyzed using hyperspectral imaging and biochemical pigment quantification. A phased optimization strategy was implemented, including preprocessing method screening, model comparison, and feature wavelength selection. Among the four tested preprocessing methods (raw reflectance, normalization, first derivative, and second derivative), normalization significantly improved model accuracy. The Adaptive Boosting (AdaBoost) algorithm outperformed partial least squares regression (PLSR) and random forest (RF), achieving coefficients of determination (R²) above 0.83 and the ratio of performance to deviation (RPD) values exceeding 2.4 across all pigments. Compared with competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA) demonstrated more effective spectral dimensionality reduction while preserving predictive power. This framework enables efficient, accurate, and scalable pigment phenotyping in Ginkgo biloba, offering technical support for large-scale germplasm screening and precision breeding.

The tea industry plays a vital role in China's green economy. Tea trees (Melaleuca alternifolia) are susceptible to numerous diseases and pest threats, making timely pathogen detection and precise pest identification critical requirements for agricultural productivity. Current diagnostic limitations primarily arise from data scarcity and insufficient discriminative feature representation in existing datasets. This study presents a new tea disease and pest dataset (TDPD, 23-class taxonomy). Five lightweight convolutional neural networks (LCNNs) were systematically evaluated through two optimizers, three learning rate configurations and six distinct scheduling strategies. Additionally, an enhanced MnasNet variant was developed through the integration of SimAM attention mechanisms, which improved feature discriminability and increased the accuracy of tea leaf disease and pest classification. Model validation employs both our proprietary TDPD dataset and an open-access dataset, with performance evaluation metrics including average accuracy, F1 score, recall, and parameter size. The experimental results demonstrated the superior classification performance of the model, which achieved accuracies of 98.03% based on TDPD and 84.58% based on the public dataset. This research outlines an effective paradigm for automated tea disease and pest detection, with direct applications in precision agriculture through integration with UAV-mounted imaging systems and mobile diagnostic platforms. This study provides practical implementation pathways for intelligent tea plantation management.