Plant Methods最新文献

Background: Quantifying plant transpiration via thermal imaging is desirable for applications in agriculture, plant breeding, and plant science. However, thermal imaging under natural non-steady state conditions is currently limited by the difficulty of quantifying thermal properties of leaves, especially specific heat capacity (C_p). Existing literature offers only rough estimates of C_p and lacks simple and accurate methods to determine it.

Results: We developed a non-invasive method to quantify k (the product of leaf thickness (lt), leaf density(ρ), and C_p), by fitting a leaf energy balance model to a leaf temperature (T_leaf) transient during and after a ~ 10 s light pulse. C_p was then estimated by dividing k by lt*ρ. Using this method, we quantified C_p for 13 horticultural and tropical plant species, and explored the relationship between C_p and leaf water content, specific leaf area and T_leaf response rate during the light pulse. Values of C_p ranged between 3200-4000 J kg^-1 K^-1, and were positively correlated with leaf water content. In species with very thick leaves, such as Phalaenopsis amabilis, we found leaf thickness to be a major factor in the temperature response to a short light pulse.

Conclusions: Our method allows for easy determination of leaf C_p of different species, and may help pave the way to apply more accurate thermal imaging under natural non-steady state conditions.

Background: Plant cell walls are made of a complex network of interacting polymers that play a critical role in plant development and responses to environmental changes. Thus, improving plant biomass and fitness requires the elucidation of the structural organization of plant cell walls in their native environment. The ¹³C-based multi-dimensional solid-state nuclear magnetic resonance (ssNMR) has been instrumental in revealing the structural information of plant cell walls through 2D and 3D correlation spectral analyses. However, the requirement of enriching plants with ¹³C limits the applicability of this method. To our knowledge, there is only a very limited set of methods currently available that achieve high levels of ¹³C-labeling of plant materials using ¹³CO_2, and most of them require large amounts of ¹³CO₂ in larger growth chambers.

Results: In this study, a simplified protocol for ¹³C-labeling of plant materials is introduced that allows ca 60% labeling of the cell walls, as quantified by comparison with commercially labeled samples. This level of ¹³C-enrichment is sufficient for all conventional 2D and 3D correlation ssNMR experiments for detailed analysis of plant cell wall structure. The protocol is based on a convenient and easy setup to supply both ¹³C-labeled glucose and ¹³CO₂ using a vacuum-desiccator. The protocol does not require large amounts of ¹³CO₂.

Conclusion: This study shows that our ¹³C-labeling of plant materials can make the accessibility to ssNMR technique easy and affordable. The derived high-resolution 2D and 3D correlation spectra are used to extract structural information of plant cell walls. This helps to better understand the influence of polysaccharide-polysaccharide interaction on plant performance and allows for a more precise parametrization of plant cell wall models.

Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques. Despite this, the academic realm currently lacks extensive, realistic datasets and deep learning strategies specifically crafted for apricot trees. This study introduces ATZD01, a publicly accessible dataset encompassing 11 categories of apricot tree pests and diseases, meticulously compiled under genuine field conditions. Furthermore, we introduce an innovative detection algorithm founded on convolutional neural networks, specifically devised for the management of apricot tree pests and diseases. To enhance the accuracy of detection, we have developed a novel object detection framework, APNet, alongside a dedicated module, the Adaptive Thresholding Algorithm (ATA), tailored for the detection of apricot tree afflictions. Experimental evaluations reveal that our proposed algorithm attains an accuracy rate of 87.1% on ATZD01, surpassing the performance of all other leading algorithms tested, thereby affirming the effectiveness of our dataset and model. The code and dataset will be made available at https://github.com/meanlang/ATZD01 .

Background: The rapid production of doubled haploids by anther culture technology is an important breeding method for awnless triticale. The aim of this study was to explore the effects of triticale genotype and the types and ratios of exogenous hormones in the medium on the efficiency of triticale anther culture.

Results: Anthers of five triticale genotypes were cultured on four different callus induction media and the calli were induced to differentiate into green plants by culture on three different differentiation media. The triticale genotype T8004 showed the best performance in anther culture, with a callus induction rate of 28.64%, a green plantlet differentiation frequency of 33.33%, and a green plantlet production rate of 2.78%. The highest callus induction rates were obtained by culturing anthers on C3 medium (the main components were potassium nitrate, glutamine, inositol, etc.), and the highest green plantlet differentiation frequency was obtained by culturing calli on D2 differentiation medium (the main components were potassium nitrate, ammonium nitrate, calcium chloride dihydrate, etc.). Flow cytometry analyses showed that 15 of the 20 DH0 generation plants that grew normally in the field were doubled haploids. The average chromosome doubling success rate was 55.6%. Analyses of agronomic traits showed that the 11 DH1 doubled haploid plants reached the standard for awnless triticale, so they are candidate materials for breeding new awnless triticale varieties.

Conclusion: The anther culture technology of triticale was optimized in this paper, which made it possible to rapidly breed homozygous varieties of awnless triticale.

Background: Virus-induced gene silencing (VIGS) is a rapid and powerful method for gene functional analysis in plants that pose challenges in stable transformation. Numerous VIGS systems based on Agrobacterium infiltration has been widely developed for tender tissues of various plant species, yet none is available for recalcitrant perennial woody plants with firmly lignified capsules, such as tea oil camellia. Therefore, there is an urgent need for an efficient, robust, and cost-effective VIGS system for recalcitrant tissues.

Results: Herein, we initiated the Tobacco rattle virus (TRV)-elicited VIGS in Camellia drupifera capsules with an orthogonal analysis including three factors: silencing target, virus inoculation approach, and capsule developmental stage. To facilitate observation and statistical analysis, two genes predominantly involved in pericarp pigmentation were selected for silencing efficiency: CdCRY1 (coding for a key photoreceptor affecting light-responsive perceivable anthocyanin accumulation in exocarps) and CdLAC15 (coding for an oxidase catalyzing the oxidative polymerization of proanthocyanidins in mesocarps, resulting in unperceivable red-hued mesocarps). The infiltration efficiency achieved for each gene was ~ 93.94% by pericarp cutting immersion. The optimal VIGS effect for each gene was observed at early (~ 69.80% for CdCRY1) and mid stages (~ 90.91% for CdLAC15) of capsule development.

Conclusions: Using our optimized VIGS system, CdCRY1 and CdLAC15 expression was successfully knocked down in Camellia drupifera pericarps, leading to fading phenotypes in exocarps and mesocarps, respectively. The established VIGS system may facilitate functional genomic studies in tea oil camellia and other recalcitrant fruits of woody plants.

The genus Flaveria has been studied extensively as a model for the evolution of C₄ photosynthesis. Thus far, molecular analyses in this genus have been limited due to a dearth of genomic information and the lack of a rapid and efficient transformation protocol. Since their development, Agrobacterium-mediated transient transformation protocols have been instrumental in understanding many biological processes in a range of plant species. However, this technique has not been applied to the genus Flaveria. Here, an efficient protocol for the Agrobacterium-mediated transient transformation of the leaves of the C₄ species Flaveria bidentis is presented. This technique has the distinct advantages of rapid turnaround, the ability to co-transform with multiple constructs, and the capacity to assay coding and non-coding regions of Flaveria genomes in a homologous context. To illustrate the utility of this protocol, the quantitative transcriptional regulation of phosphoenolpyruvate carboxylase, the primary carboxylase of C₄ plants, was investigated. A 24 bp region in the ppcA1 proximal promoter was found to elicit high levels of reporter gene expression. The Agrobacterium-mediated transient transformation of F. bidentis leaves will accelerate the understanding of the biology and evolution of C₄ photosynthesis in the genus Flaveria as well as in other C₄ lineages.

Background: Genetic markers are crucial for breeding crops with desired agronomic traits, and their development can be expedited using next-generation sequencing (NGS) and bioinformatics tools. Numerous tools have been developed to design molecular markers, enhancing the convenience, accuracy, and efficiency of molecular breeding. However, these tools primarily focus on genetic variants within short user-input sequences, despite the availability of extensive omics data for genomic variants. To design molecular markers encompassing a vast number of genetic variants at the genome-wide scale in soybean, an automatic system capable of handling NGS-based big data is necessary.

Results: In this study, we developed a robust digital platform, the CAPS Maker, for designing cleaved amplified polymorphic sequence (CAPS)/derived CAPS (dCAPS) markers in soybeans. This platform simplifies the systematic design of genomic markers with a user-friendly graphical interface, featuring a 'SNP Browser' and 'Primer Table', along with internal programs (e.g., the eHT-PCR module) to design unique primer pairs for highly duplicated genomes like soybean.

Conclusions: The CAPS Maker's efficiency and reliability were experimentally verified by comparing its marker predictions with actual experimental results. Consequently, breeders can easily design CAPS/dCAPS markers using the CAPS Maker platform to develop new soybean cultivars with beneficial agronomic traits. This platform is freely accessible at https://tgil.donga.ac.kr/CAPSMaker .

Background: Pest infestation poses a major challenge in the field of global plant protection, seriously threatening crop safety. To enhance crop protection and optimize control strategies, this study is dedicated to the precise identification of various pests that harm crops, thereby ensuring the efficient use of agricultural pesticides and achieving optimal plant protection.

Results: Currently, pest identification technologies lack accuracy, especially in recognizing pests across different growth stages. To address this issue, we constructed a large pest dataset that includes 102 pest species and 369 pest stages, totaling 51,670 images. This dataset focuses on the identification of pest growth stages, aimed at improving the efficiency of pest management and the effectiveness of plant protection. Moreover, we have introduced two innovative technologies to tackle the significant differences between pest growth stages: a Multi-stage Co-supervision mechanism and a Spatial Attention module. These technologies significantly enhance the model's ability to extract key features, thus boosting recognition accuracy. Compared to the industry-leading Vision Transformer-based methods, our model shows a significant improvement, increasing accuracy by 3.67% and the F1 score by 2.49%, without a significant increase in the number of parameters.

Conclusions: Extensive experimental validation has demonstrated our model's significant advantages in enhancing pest identification accuracy, which holds substantial practical significance for the precise application of pesticides and crop protection.

Verticillium wilt greatly hampers Chinese cabbage growth, causing significant yield limitations. Rapid and accurate detection of Verticillium wilt in the Chinese cabbage (Brassica rapa L. ssp. pekinensis) can provide significant agronomic benefits. Here, we propose a detection model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV). Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. To reduce the difficulty of acquiring diseased Chinese cabbage data, a cycle-consistent generative adversarial network (CycleGAN) was used to simulate and generate images of the Verticillium wilt characteristics for multiple fields. The detection of lightly infected plants achieved precision, recall, mean average precision (mAP), and F1-score of 81.3, 86.6, 87.7, and 83.9%, respectively. DSConv-GAN outperforms other models in terms of precision, detection speed, robustness, and generalization. The model is combined with software to improve the practicability of the proposed method. Our results demonstrate DSConv-GAN to be an effective intelligent farming tool that provides early, rapid, and accurate detection of Chinese cabbage Verticillium wilt in complex growing environments.