Plant Methods最新文献

Crop phenotyping of important agronomic traits in field conditions at single-plant resolution has long been a major bottleneck in both genetic analysis (e.g. large-scale association/linkage analysis) and breeding applications (e.g. genomic prediction/selection). Despite growing interest, ultra-affordable, high-throughput and accurate phenotyping tools for maize ears remain limited. Here, we developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline. The imaging platform is composed of 3D-printed parts and electronics components easily available from local retailers to perform high-quality 360° surface scanning of maize ears. Our pipeline first employs CNN-based models to identify normally-developed ears suitable for phenotyping, followed by reliable segmentation of ears and ear surface projection images by YOLOv11-based models, from which ten key traits are subsequently extracted. OpenEar demonstrates reliable agreement with manual measurements across a diverse set of ear- and kernel-related traits, including ear length (R² = 0.972), ear diameter (R² = 0.905), ear volume (R² = 0.976), ear weight (R² = 0.878), kernel number (R² = 0.98), kernel row number (R² = 0.888), kernel number per row (R² = 0.852), kernel thickness (R² = 0.705), kernel width (R² = 0.515), and thousand kernel weight (R² = 0.605). A user-friendly graphical interface is developed for manual inspection of ears after computer annotation. Manually annotated ear videos and images are publicly released as a resource for the crop phenomics community. Our study highlights the potential of DIY-based low-cost solutions to make phenotyping more accessible in crop genetic analysis and breeding.

Fine-grained pest recognition is a key component of intelligent pest monitoring and precise control, and it is important for ensuring agricultural production safety. This paper proposes a generative self-supervised learning-based pest recognition model, termed PAFT-WPest, to address challenges in fine-grained pest recognition, including small inter-class differences, large intra-class variations, complex background interference, and limited annotated data. The model employs partial-convolution spatial attention to focus on pest regions while suppressing redundant background information. Channel semantic selection and frequency-domain modeling are introduced to enhance the model's ability to perceive subtle detail differences. In addition, the model captures dependency relationships among different parts of the pest body to improve the modeling of global structure and semantic information. Furthermore, two fine-grained wolfberry pest datasets that distinguish pest growth stages and damage locations are constructed, and a continual pre-training strategy is adopted to enhance cross-scenario adaptability. Experimental results show that PAFT-WPest achieves accuracies of 76.83%, 91.53%, 98.70%, 79.27%, and 97.34% on the public pest datasets IP102, Butterfly-200, WPIT9K, Rice Pest, and Jute Pest, respectively, and accuracies of 97.82% and 94.69% on the self-built wolfberry pest datasets WP45 and WP11. These results indicate that the proposed model can improve fine-grained pest recognition performance under complex backgrounds, providing a feasible approach for agricultural pest monitoring and classification.

Background: Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.

Results: The dual-perspective strategy reduces occlusion-induced errors by 75% compared to single-view approaches, enabling robust organ-level segmentation via a multi-constraint clustering strategy. Automated measurements of leaf length and width across five developmental stages demonstrate exceptional agreement with manual references (R² > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development.

Conclusions: This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.

Eukaryotic cells consist of various organelles, each responsible for specific biological processes, making the understanding of protein subcellular localization essential for determining their potential functions. However, the rapid polar growth of pollen tubes requires careful consideration of organelle trafficking when analyzing subcellular localization in related studies. Fluorescence-tagged organelle markers have shown limited utility for studying pollen during the reproductive stage. In this study, we developed pollen-specific fluorescent marker sets for organelles and other cell structures using the promoter of the OsTAPE gene, which is highly expressed in mature pollen and pollen tubes of rice (Oryza sativa L.). These marker sets enable the visualization of cell membranes, nuclei, endoplasmic reticulum, Golgi apparatus, prevacuolar compartments, and filamentous actin by tagging fluorescent proteins (FP) at the amino N-terminal end. Specifically designed to accommodate the rapid tube elongation of rice pollen, this system offers a valuable resource for gene function research and colocalization analysis, helping to elucidate the pollen tube elongation process. This study expands the potential for using fluorescent labeling in monocotyledonous plants like rice during reproductive stages, facilitating gene function studies under varying environmental conditions through subcellular localization analysis in growing pollen tubes.

Background: The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signaling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole's bounding membrane-the tonoplast-can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks-it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets.

Results: We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods´ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation.

Conclusions: We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.

Single-cell technologies are transforming plant biology, yet broadly transferable nuclei isolation remains a key bottleneck for snRNA-seq. We developed a reproducible, cost-efficient Percoll-based workflow that is applicable to multiple maize tissues and nine additional plant species. In maize, nuclei from root, shoot, leaf, and embryo consistently concentrated at the 80% Percoll interface and exhibited high integrity, with typical recoveries > 50,000 nuclei per sample. For other species, gradient compositions were tuned according to genome size to achieve efficient enrichment and clean suspensions, and yields ranged from 17,000 to 40,000 nuclei per sample. Downstream validation showed that nuclei from special interest maize and Tripsacum generated high-quality snRNA-seq libraries, as supported by cDNA quality profiles. These results demonstrate the versatility and robustness of the method across species and tissues.

Background: Translation is a fundamental process for every living organism. In plants, the rate of translation is tightly modulated during development and in responses to environmental cues. However, it is challenging to measure the actual translation state of the tissues in vivo.

Results: Here, we report the introduction of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC. We combined a method originally developed for the fruitflies with an improved low background split-mVenus BiFC system previously described in plants. We labelled small subunit ribosomal proteins (RPS) and large subunit ribosomal proteins (RPL) of Arabidopsis thaliana with fragments of the mVenus fluorescent protein (FP). We tested the Ribo-BiFC method using transiently expressed recombinant ribosomal proteins in epidermal cells of Nicotiana benthamiana. The BiFC-tagged ribosomal proteins complemented the mVenus molecule and were detected by fluorescence microscopy, potentially visualizing the close proximity of translating assembled 80S ribosomal subunits. Although the resulting signal is less intense than that of known interactors, its detection points to the functionality of the system.

Conclusions: This Ribo-BiFC approach has further potential for use in stable transgenic lines in enabling the visualisation of translational rate in plant tissues and changing translation dynamics during plant development, under abiotic stress or in different genetic backgrounds.