Plant Methods最新文献

Foxtail millet is an important minor cereal crop rich in nutrients. Due to the small size of its seeds, there is little information on the diversity of its seed structure among germplasms, limiting the identification of genes controlling seed development and germination. This paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds, gaining insights into their internal features, distribution, and composition. A total of 100 foxtail millet varieties were scanned with X-ray computed tomography to obtain 3D reconstruction images and slices. Pre-processing steps were adopted to improve image segmentation accuracy, including noise reduction, rotation, contrast enhancement, and brightness enhancement. The experiment revealed that traditional OpenCV image processing methods failed to achieve precise segmentation, whereas deep learning models exhibited outstanding performance in segmenting seed CT slice images. We compared UNet, PSPNet, and DeepLabV3 models, selected different backbones and optimizers based on the dataset, and continuously adjusted learning rates and maximum training epochs to train the models. Results demonstrated that VGG16-UNet achieved an accuracy of 99.19% on the foxtail millet seed CT slice image dataset, outperforming PSPNet and DeepLabV3 models. Compared to ResNet-UNet, VGG16-UNet shows an improvement of approximately 3.18% in accuracy, demonstrating superior performance in accurately segmenting the inner glume, outer glume, embryo, and endosperm under various adhesion conditions. Accurate segmentation of foxtail millet CT images enables analysis of embryo size, endosperm size, and glume thickness, which impact germination, growth, and nutrition. This study fills a gap in small grain structure research, offering insights to optimize agriculture and molecular breeding for improved yield and quality.

Background: Foxtail millet [Setaria italica (L.) Beauv] is a C₄ graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation.

Results: This study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH.

Conclusion: The HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.

With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy.

Nanomaterial-mediated plant genetic engineering holds promise for developing new crop cultivars but can be hindered by nanomaterial toxicity to protoplasts. We present a fast, high-throughput method for assessing protoplast viability using resazurin, a non-toxic dye converted to highly fluorescent resorufin during respiration. Protoplasts isolated from hypocotyl canola (Brassica napus L.) were evaluated at varying temperatures (4, 10, 20, 30 ˚C) and time intervals (1-24 h). Optimal conditions for detecting protoplast viability were identified as 20,000 cells incubated with 40 µM resazurin at room temperature for 3 h. The assay was applied to evaluate the cytotoxicity of silver nanospheres, silica nanospheres, cholesteryl-butyrate nanoemulsion, and lipid nanoparticles. The cholesteryl-butyrate nanoemulsion and lipid nanoparticles exhibited toxicity across all tested concentrations (5-500 ng/ml), except at 5 ng/ml. Silver nanospheres were toxic across all tested concentrations (5-500 ng/ml) and sizes (20-100 nm), except for the larger size (100 nm) at 5 ng/ml. Silica nanospheres showed no toxicity at 5 ng/ml across all tested sizes (12-230 nm). Our results highlight that nanoparticle size and concentration significantly impact protoplast toxicity. Overall, the results showed that the resazurin assay is a precise, rapid, and scalable tool for screening nanomaterial cytotoxicity, enabling more accurate evaluations before using nanomaterials in genetic engineering.

Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.

In spatio-temporal plant monitoring, optical sensing (including hyperspectral imaging), is being deployed to, non-invasively, detect and diagnose plant responses to abiotic and biotic stressors. Early and accurate detection and diagnosis of stressors are key objectives. Level of radiometric repeatability of optical sensing data and ability to accurately detect and diagnose biotic stress are inversely correlated. Accordingly, it may be argued that one of the most significant frontiers and challenges regarding widespread adoption of optical sensing in plant research and crop production hinges on methods to maximize radiometric repeatability. In this study, we acquired hyperspectral optical sensing data at noon and midnight from soybean (Glycine max) and coleus wizard velvet red (Solenostemon scutellarioides) plants with/without experimentally infestation of two-spotted spider mites (Tetranychus urticae). We addressed three questions related to optimization of radiometric repeatability: (1) are reflectance-based plant responses affected by time of optical sensing? (2) if so, are plant responses to two-spotted spider mite infestations (biotic stressor) more pronounced at midnight versus at noon? (3) Is detection of biotic stress enhanced by spatial binning (smoothing) of hyperspectral imaging data? Results from this study provide insight into calculations of radiometric repeatability. Results strongly support claims that acquisition of optical sensing data to detect and characterize stress responses by plants to detect biotic stressors should be performed at night. Moreover, the combination of midnight imaging and spatial binning increased classification accuracies with 29% and 31% for soybean and coleus, respectively. Practical implications of these findings are discussed. Study results are relevant to virtually all applications of optical sensing to detect and diagnose abiotic and biotic stress responses by plants in both controlled environments and in outdoor crop production systems.

Background: The use of renewable energy for sustainable and climate-neutral electricity production is increasing worldwide. High-voltage direct-current (HVDC) transmission via underground cables helps connect large production sides with consumer regions. In Germany, almost 5,000 km of new power line projects is planned, with an initial start date of 2038 or earlier. During transmission, heat is emitted to the surrounding soil, but the effects of the emitted heat on root growth and yield of the overlying crop plants remain uncertain and must be investigated.

Results: For this purpose, we designed and constructed a low-cost large HeAted soiL-Monolith (HAL-M) model for simulating heat flow within soil with a natural composition and density. We could observe root growth, soil temperature and soil water content over an extended period. We performed a field trial-type experiment involving three-part crop rotation in a greenhouse. We showed that under the simulated conditions, heat emission could reduce the yield and root growth depending on the crop type and soil.

Conclusions: This experimental design could serve as a low-cost, fast and reliable standard for investigating thermal issues related to various soil compositions and types, precipitation regimes and crop plants affected by similar projects. Beyond our research question, the HAL-M technique could serve as a link between pot and field trials with the advantages of both approaches. This method could enrich many research areas with the aim of controlling natural soil and plant conditions.

Background: The cotton whitefly (Bemisia tabaci) is a major global pest, causing significant crop damage through viral infestation and feeding. Traditional B. tabaci recognition relies on human eyes, which requires a large amount of work and high labor costs. The pests overlapping generations, high reproductive capacity, small size, and migratory behavior present challenges for the real-time monitoring and early warning systems. This study aims to develop an efficient, high-throughput automated system for detection of the cotton whiteflies. In this work, a novel tool for cotton whitefly fast identification and quantification was developed based on deep learning-based model. This approach enhances the effectiveness of B. tabaci control by facilitating earlier detection of its establishment in cotton, thereby allowing for a quicker implementation of management strategies.

Results: We compiled a dataset of 1200 annotated images of whiteflies on cotton leaves, augmented using techniques like flipping and rotation. We modified the YOLO v8s model by replacing the C2f module with the Swin-Transformer and introducing a P2 structure in the Head, achieving a precision of 0.87, mAP₅₀ of 0.92, and F1 score of 0.88 through ablation studies. Additionally, we employed SAHI for image preprocessing and integrated the whitefly detection algorithm on a Raspberry Pi, and developed a GUI-based visual interface. Our preliminary analysis revealed a higher density of whiteflies on cotton leaves in the afternoon and the middle-top, middle, and middle-down plant sections.

Conclusion: Utilizing the enhanced YOLO v8s deep learning model, we have achieved precise detection and counting of whiteflies, enabling its application on hardware devices like the Raspberry Pi. This approach is highly suitable for research requiring accurate quantification of cotton whiteflies, including phenotypic analyses. Future work will focus on deploying such equipment in large fields to manage whitefly infestations.

Background: Plant breeding played a very important role in transforming strawberries from being a niche crop with a small geographical footprint into an economically important crop grown across the planet. But even modern marker assisted breeding takes a considerable amount of time, over multiple plant generations, to produce a plant with desirable traits. As a quicker alternative, plants with desirable traits can be raised through tissue culture by doing precise genetic manipulations. Overexpression of morphogenic regulators previously known for meristem development, the transcription factors Growth-Regulating Factors (GRFs) and the GRF-Interacting Factors (GIFs), provided an efficient strategy for easier regeneration and transformation in multiple crops.

Results: We present here a comprehensive protocol for the diploid strawberry Fragaria vesca Hawaii 4 (strawberry) regeneration and transformation under control condition as compared to ectopic expression of different GRF4-GIF1 chimeras from different plant species. We report that ectopic expression of Vitis vinifera VvGRF4-GIF1 provides significantly higher regeneration efficiency during re-transformation over wild-type plants. On the other hand, deregulated expression of miRNA resistant version of VvGRF4-GIF1 or Triticum aestivum (wheat) TaGRF4-GIF1 resulted in abnormalities. Transcriptomic analysis between the different chimeric GRF4-GIF1 lines indicate that differential expression of FvExpansin might be responsible for the observed pleiotropic effects. Similarly, cytokinin dehydrogenase/oxygenase and cytokinin responsive response regulators also showed differential expression indicating GRF4-GIF1 pathway playing important role in controlling cytokinin homeostasis.

Conclusion: Our data indicate that ectopic expression of Vitis vinifera VvGRF4-GIF1 chimera can provide significant advantage over wild-type plants during strawberry regeneration without producing any pleiotropic effects seen for the miRNA resistant VvGRF4-GIF1 or TaGRF4-GIF1.