Plant Methods最新文献

{"title":"Mapping and quantifying unique branching structures in lentil (Lens culinaris Medik.).","authors":"Adam M Dimech, Sukhjiwan Kaur, Edmond J Breen","doi":"10.1186/s13007-024-01223-1","DOIUrl":"10.1186/s13007-024-01223-1","url":null,"abstract":"<p><strong>Background: </strong>Lentil (Lens culinaris Medik.) is a globally-significant agricultural crop used to feed millions of people. Lentils have been cultivated in the Australian states of Victoria and South Australia for several decades, but efforts are now being made to expand their cultivation into Western Australia and New South Wales. Plant architecture plays a pivotal role in adaptation, leading to improved and stable yields especially in new expansion regions. Image-based high-throughput phenomics technologies provide opportunities for an improved understanding of plant development, architecture, and trait genetics. This paper describes a novel method for mapping and quantifying individual branch structures on immature glasshouse-grown lentil plants grown using a LemnaTec Scanalyser 3D high-throughput phenomics platform, which collected side-view RGB images at regular intervals under controlled photographic conditions throughout the experiment. A queue and distance-based algorithm that analysed morphological skeletons generated from images of lentil plants was developed in Python. This code was incorporated into an image analysis pipeline using open-source software (PlantCV) to measure the number, angle, and length of individual branches on lentil plants.</p><p><strong>Results: </strong>Branching structures could be accurately identified and quantified in immature plants, which is sufficient for calculating early vigour traits, however the accuracy declined as the plants matured. Absolute accuracy for branch counts was 77.9% for plants at 22 days after sowing (DAS), 57.9% at 29 DAS and 51.9% at 36 DAS. Allowing for an error of ± 1 branch, the associated accuracies for the same time periods were 97.6%, 90.8% and 79.2% respectively. Occlusion in more mature plants made the mapping of branches less accurate, but the information collected could still be useful for trait estimation. For branch length calculations, the amount of variance explained by linear mixed-effects models was 82% for geodesic length and 87% for Euclidean branch lengths. Within these models, both the mean geodesic and Euclidean distance measurements of branches were found to be significantly affected by genotype, DAS and their interaction. Two informative metrices were derived from the calculations of branch angle; 'splay' is a measure of how far a branch angle deviates from being fully upright whilst 'angle-difference' is the difference between the smallest and largest recorded branch angle on each plant. The amount of variance explained by linear mixed-effects models was 38% for splay and 50% for angle difference. These lower R<sup>2</sup> values are likely due to the inherent difficulties in measuring these parameters, nevertheless both splay and angle difference were found to be significantly affected by cultivar, DAS and their interaction. When 276 diverse lentil genotypes with varying degrees of salt tolerance were grown in a glasshouse-based experiment where ","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"95"},"PeriodicalIF":4.7,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11188192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141427418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-cost and reliable substrate-based phenotyping platform for screening salt tolerance of cutting propagation-dependent grass, paspalum vaginatum.","authors":"Zhiwei Liu, Wentao Xue, Qijuan Jiang, Ademola Olufolahan Olaniran, Xiaoxian Zhong","doi":"10.1186/s13007-024-01225-z","DOIUrl":"10.1186/s13007-024-01225-z","url":null,"abstract":"<p><strong>Background: </strong>Salt tolerance in plants is defined as their ability to grow and complete their life cycle under saline conditions. Staple crops have limited salt tolerance, but forage grass can survive in large unexploited saline areas of costal or desert land. However, due to the restriction of self-incompatible fertilization in many grass species, vegetative propagation via stem cuttings is the dominant practice; this is incompatible with current methodologies of salt-tolerance phenotyping, which have been developed for germination-based seedling growth. Therefore, the performance of seedlings from cuttings under salt stress is still fuzzy. Moreover, the morphological traits involved in salt tolerance are still mostly unknown, especially under experimental conditions with varying levels of stress.</p><p><strong>Results: </strong>To estimate the salt tolerance of cutting propagation-dependent grasses, a reliable and low-cost workflow was established with multiple saline treatments, using Paspalum vaginatum as the material and substrate as medium, where cold stratification and selection of stem segments were the two variables used to control for experimental errors. Average leaf number (ALN) was designated as the best criterion for evaluating ion-accumulated salt tolerance. The reliability of ALN was revealed by the consistent results among four P. vaginatum genotypes, and three warm-season (pearl millet, sweet sorghum, and wild maize) and four cold-season (barley, oat, rye, and ryegrass) forage cultivars. Dynamic curves simulated by sigmoidal mathematical models were well-depicted for the calculation of the key parameter, Salt₅₀. The reliability of the integrated platform was further validated by screening 48 additional recombinants, which were previously generated from a self-fertile mutant of P. vaginatum. The genotypes displaying extreme ALN-based Salt₅₀ also exhibited variations in biomass and ion content, which not only confirmed the reliability of our phenotyping platform but also the representativeness of the aerial ALN trait for salt tolerance.</p><p><strong>Conclusions: </strong>Our phenotyping platform is proved to be compatible with estimations in both germination-based and cutting propagation-dependent seedling tolerance under salt stresses. ALN and its derived parameters are prone to overcome the species barriers when comparing salt tolerance of different species together. The accuracy and reliability of the developed phenotyping platform is expected to benefit breeding programs in saline agriculture.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"94"},"PeriodicalIF":4.7,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11186238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141427417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks.","authors":"Lukas Drees, Dereje T Demie, Madhuri R Paul, Johannes Leonhardt, Sabine J Seidel, Thomas F Döring, Ribana Roscher","doi":"10.1186/s13007-024-01205-3","DOIUrl":"10.1186/s13007-024-01205-3","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Experiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predi","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"93"},"PeriodicalIF":5.1,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11179353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141327777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Versatile DNA extraction from diverse plant taxa using ionic liquids and magnetic ionic liquids: a methodological breakthrough for enhanced sample utility.","authors":"Shashini De Silva, Cecilia Cagliero, Morgan R Gostel, Gabriel Johnson, Jared L Anderson","doi":"10.1186/s13007-024-01217-z","DOIUrl":"10.1186/s13007-024-01217-z","url":null,"abstract":"<p><strong>Background: </strong>There is a growing demand for fast and reliable plant biomolecular analyses. DNA extraction is the major bottleneck in plant nucleic acid-based applications especially due to the complexity of tissues in different plant species. Conventional methods for plant cell lysis and DNA extraction typically require extensive sample preparation processes and large quantities of sample and chemicals, elevated temperatures, and multiple sample transfer steps which pose challenges for high throughput applications.</p><p><strong>Results: </strong>In a prior investigation, an ionic liquid (IL)-based modified vortex-assisted matrix solid phase dispersion approach was developed using the model plant, Arabidopsis thaliana (L.) Heynh. Building upon this foundational study, the present study established a simple, rapid and efficient protocol for DNA extraction from milligram fragments of plant tissue representing a diverse range of taxa from the plant Tree of Life including 13 dicots and 4 monocots. Notably, the approach was successful in extracting DNA from a century old herbarium sample. The isolated DNA was of sufficient quality and quantity for sensitive molecular analyses such as qPCR. Two plant DNA barcoding markers, the plastid rbcL and nuclear ribosomal internal transcribed spacer (nrITS) regions were selected for DNA amplification and Sanger sequencing was conducted on PCR products of a representative dicot and monocot species. Successful qPCR amplification of the extracted DNA up to 3 weeks demonstrated that the DNA extracted using this approach remains stable at room temperature for an extended time period prior to downstream analysis.</p><p><strong>Conclusions: </strong>The method presented here is a rapid and simple approach enabling cell lysis and DNA extraction from 1.5 mg of plant tissue across a broad range of plant taxa. Additional purification prior to DNA amplification is not required due to the compatibility of the extraction solvents with qPCR. The method has tremendous potential for applications in plant biology that require DNA, including barcoding methods for agriculture, conservation, ecology, evolution, and forensics.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"91"},"PeriodicalIF":5.1,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141321378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Publisher Correction: New improvements in grapevine genome editing: high efficiency biallelic homozygous knock-out from regenerated plantlets by using an optimized zCas9i.","authors":"Jérémy Villette, Fatma Lecourieux, Eliot Bastiancig, Marie-Claire Héloir, Benoit Poinssot","doi":"10.1186/s13007-024-01204-4","DOIUrl":"10.1186/s13007-024-01204-4","url":null,"abstract":"","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"92"},"PeriodicalIF":5.1,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141321377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenotyping grapevine resistance to downy mildew: deep learning as a promising tool to assess sporulation and necrosis.","authors":"Felicià Maviane Macia, Tyrone Possamai, Marie-Annick Dorne, Marie-Céline Lacombe, Eric Duchêne, Didier Merdinoglu, Nemo Peeters, David Rousseau, Sabine Wiedemann-Merdinoglu","doi":"10.1186/s13007-024-01220-4","DOIUrl":"10.1186/s13007-024-01220-4","url":null,"abstract":"<p><strong>Background: </strong>Downy mildew is a plant disease that affects all cultivated European grapevine varieties. The disease is caused by the oomycete Plasmopara viticola. The current strategy to control this threat relies on repeated applications of fungicides. The most eco-friendly and sustainable alternative solution would be to use bred-resistant varieties. During breeding programs, some wild Vitis species have been used as resistance sources to introduce resistance loci in Vitis vinifera varieties. To ensure the durability of resistance, resistant varieties are built on combinations of these loci, some of which are unfortunately already overcome by virulent pathogen strains. The development of a high-throughput machine learning phenotyping method is now essential for identifying new resistance loci.</p><p><strong>Results: </strong>Images of grapevine leaf discs infected with P. viticola were annotated with OIV 452-1 values, a standard scale, traditionally used by experts to assess resistance visually. This descriptor takes two variables into account the complete phenotype of the symptom: sporulation and necrosis. This annotated dataset was used to train neural networks. Various encoders were used to incorporate prior knowledge of the scale's ordinality. The best results were obtained with the Swin transformer encoder which achieved an accuracy of 81.7%. Finally, from a biological point of view, the model described the studied trait and identified differences between genotypes in agreement with human observers, with an accuracy of 97% but at a high-throughput 650% faster than that of humans.</p><p><strong>Conclusion: </strong>This work provides a fast, full pipeline for image processing, including machine learning, to describe the symptoms of grapevine leaf discs infected with P. viticola using the OIV 452-1, a two-symptom standard scale that considers sporulation and necrosis. If symptoms are frequently assessed by visual observation, which is time-consuming, low-throughput, tedious, and expert dependent, the method developed sweeps away all these constraints. This method could be extended to other pathosystems studied on leaf discs where disease symptoms are scored with ordinal scales.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"90"},"PeriodicalIF":5.1,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141317967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pin-based probe for electronic moisture meters to determine moisture content in a single wheat kernel.","authors":"Jatinder S Sangha, Brad Meyer, Yuefeng Ruan, Richard D Cuthbert, Ron Knox, Gaozhi Xiao","doi":"10.1186/s13007-024-01194-3","DOIUrl":"10.1186/s13007-024-01194-3","url":null,"abstract":"<p><strong>Background: </strong>Optimum moisture in straw and grain at maturity is important for timely harvesting of wheat. Grain harvested at the right time has reduced chance of being affected by adverse weather conditions which is important to maintain grain quality and end use functionality. Wheat varieties with a short dry down period could help in timely harvest of the crop. However, measuring single kernel moisture in wheat and other small grain crops is a phenotyping bottleneck which requires characterising moisture content of the developing kernel at physiological maturity.</p><p><strong>Results: </strong>Here we report developing a pin-based probe to detect moisture in a developing wheat kernel required for determining physiological maturity. An in-house designed pin-based probe was used with different commercially available electronic moisture meters to assess the moisture content of the individual kernels in spikes with high accuracy (R² = 0.73 to 0.94, P < 0.001) compared with a reference method of oven drying. The average moisture values varied among different electronic moisture meters and the oven-dry method and differences in values were minimized at low kernel moisture content (< 50%). The single kernel moisture probe was evaluated in the field to predict the physiological maturity in wheat using 38% moisture content as the reference and visible notes on kernel stage.</p><p><strong>Conclusion: </strong>The pin-based moisture probe is a reliable tool for wheat physiologists and breeders to conveniently and accurately measure moisture content in developing grain that will aid in identifying wheat germplasm with fast dry-down characteristics.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"89"},"PeriodicalIF":5.1,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11163790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes.","authors":"Angeline Wanjiku Maina, Erich-Christian Oerke","doi":"10.1186/s13007-024-01215-1","DOIUrl":"10.1186/s13007-024-01215-1","url":null,"abstract":"<p><strong>Background: </strong>Precise evaluation of fungal conidia production may facilitate studies on resistance mechanisms and plant breeding for disease resistance. In this study, hyperspectral imaging (HSI) was used to quantify the sporulation of Magnaporthe oryzae on the leaves of rice cultivars grown under controlled conditions. Three rice genotypes (CO 39, Nipponbare, IR64) differing in susceptibility to blast were inoculated with M. oryzae isolates Guy 11 and Li1497. Spectral information (450-850 nm, 140 wavebands) of typical leaf blast symptoms was recorded before and after induction of sporulation of the pathogen.</p><p><strong>Results: </strong>M. oryzae produced more conidia on the highly susceptible genotype than on the moderately susceptible genotype, whereas the resistant genotype resulted in no sporulation. Changes in reflectance spectra recorded before and after induction of sporulation were significantly higher in genotype CO 39 than in Nipponbare. The spectral angle mapper algorithm for supervised classification allowed for the classification of blast symptom subareas and the quantification of lesion areas with M. oryzae sporulation. The correlation between the area under the difference spectrum (viz. spectral difference without and with sporulation) and the number of conidia per lesion and the number of conidia per lesion area was positive and count-based differences in rice - M. oryzae interaction could be reproduced in the spectral data.</p><p><strong>Conclusions: </strong>HSI provided a precise and objective method of assessing M. oryzae conidia production on infected rice plants, revealing differences that could not be detected visually.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"87"},"PeriodicalIF":5.1,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11161989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141288433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia.","authors":"Hastings Shamaoma, Paxie W Chirwa, Jules C Zekeng, Able Ramoelo, Andrew T Hudak, Ferdinand Handavu, Stephen Syampungani","doi":"10.1186/s13007-024-01212-4","DOIUrl":"10.1186/s13007-024-01212-4","url":null,"abstract":"<p><p>To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-R² = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics (R² = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"88"},"PeriodicalIF":5.1,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11162019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141288432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Identifcation and estimation of lodging in bread wheat genotypes using machine learning predictive algorithms.","authors":"Ehsan Rabieyan, Reza Darvishzadeh, Hadi Alipour","doi":"10.1186/s13007-024-01203-5","DOIUrl":"10.1186/s13007-024-01203-5","url":null,"abstract":"","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"86"},"PeriodicalIF":5.1,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11162050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141288431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}