Academic literature on the topic 'High-throughput shoot phenotyping'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'High-throughput shoot phenotyping.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "High-throughput shoot phenotyping"
1
Wu, Sheng, Weiliang Wen, Yongjian Wang, Jiangchuan Fan, Chuanyu Wang, Wenbo Gou, and Xinyu Guo. "MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction." Plant Phenomics 2020 (March 12, 2020): 1–17. http://dx.doi.org/10.34133/2020/1848437.
Full textAbstract:
Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform’s device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R2) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.
2
Joshi, Sameer, Emily Thoday-Kennedy, Hans D. Daetwyler, Matthew Hayden, German Spangenberg, and Surya Kant. "High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance." PLOS ONE 16, no. 7 (July 23, 2021): e0254908. http://dx.doi.org/10.1371/journal.pone.0254908.
Full textAbstract:
Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria’s high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.
3
Li, Yinglun, Weiliang Wen, Xinyu Guo, Zetao Yu, Shenghao Gu, Haipeng Yan, and Chunjiang Zhao. "High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network." PLOS ONE 16, no. 1 (January 12, 2021): e0241528. http://dx.doi.org/10.1371/journal.pone.0241528.
Full textAbstract:
Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or tractor-based high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R2 = 0.96–0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.
4
Banerjee, Bikram P., Sameer Joshi, Emily Thoday-Kennedy, Raj K. Pasam, Josquin Tibbits, Matthew Hayden, German Spangenberg, and Surya Kant. "High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response." Journal of Experimental Botany 71, no. 15 (March 18, 2020): 4604–15. http://dx.doi.org/10.1093/jxb/eraa143.
Full textAbstract:
Abstract The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.
5
Aharon, Shlomi, Zvi Peleg, Eli Argaman, Roi Ben-David, and Ran N. Lati. "Image-Based High-Throughput Phenotyping of Cereals Early Vigor and Weed-Competitiveness Traits." Remote Sensing 12, no. 23 (November 26, 2020): 3877. http://dx.doi.org/10.3390/rs12233877.
Full textAbstract:
Cereals grains are the prime component of the human diet worldwide. To promote food security and sustainability, new approaches to non-chemical weed control are needed. Early vigor cultivars with enhanced weed-competitiveness ability are a potential tool, nonetheless, the introduction of such trait in breeding may be a long and labor-intensive process. Here, two image-driven plant phenotyping methods were evaluated to facilitate effective and accurate selection for early vigor in cereals. For that purpose, two triticale genotypes differentiating in vigor and growth rate early in the season were selected as model plants: X-1010 (high) and Triticale1 (low). Two modeling approaches, 2-D and 3-D, were applied on the plants offering an evaluation of various morphological growth parameters for the triticale canopy development, under controlled and field conditions. The morphological advantage of X-1010 was observed only at the initial growth stages, which was reflected by significantly higher growth parameter values compared to the Triticale1 genotype. Both modeling approaches were sensitive enough to detect phenotypic differences in growth as early as 21 days after sowing. All growth parameters indicated a faster early growth of X-1010. However, the 2-D related parameter [projected shoot area (PSA)] is the most available one that can be extracted via end user-friendly imaging equipment. PSA provided adequate indication for the triticale early growth under weed-competition conditions and for the improved weed-competition ability. The adequate phenotyping ability for early growth and competition was robust under controlled and field conditions. PSA can be extracted from close and remote sensing platforms, thus, facilitate high throughput screening. Overall, the results of this study may improve cereal breeding for early vigor and weed-competitiveness.
6
Momen, Mehdi, Malachy T. Campbell, Harkamal Walia, and Gota Morota. "Predicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre Polynomials and B-Splines." G3: Genes|Genomes|Genetics 9, no. 10 (August 19, 2019): 3369–80. http://dx.doi.org/10.1534/g3.119.400346.
Full textAbstract:
Recent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories. Prediction accuracy declined slightly for the water-limited plants compared to control plants. Overall, RRM delivered reasonable prediction performance and yielded better prediction than the baseline multi-trait model. The difference between the results obtained using Legendre polynomials and that using B-splines was small; however, the former yielded a higher prediction accuracy. Prediction accuracy for forecasting the last five time points was highest when the entire trajectory from earlier growth stages was used to train the basis functions. Our results suggested that it was possible to decrease phenotyping frequency by only phenotyping every other day in order to reduce costs while minimizing the loss of prediction accuracy. This is the first study showing that RRM could be used to model changes in growth over time under abiotic stress conditions.
7
Watts-Williams, S. J., N. Jewell, C. Brien, B. Berger, T. Garnett, and T. R. Cavagnaro. "Using High-Throughput Phenotyping to Explore Growth Responses to Mycorrhizal Fungi and Zinc in Three Plant Species." Plant Phenomics 2019 (March 25, 2019): 1–12. http://dx.doi.org/10.34133/2019/5893953.
Full textAbstract:
There are many reported benefits to plants of arbuscular mycorrhizal fungi (AMF), including positive plant biomass responses; however, AMF can also induce biomass depressions in plants, and this response receives little attention in the literature. High-throughput phenotyping (HTP) technology permits repeated measures of an individual plant’s aboveground biomass. We examined the effect on AMF inoculation on the shoot biomass of three contrasting plant species: a vegetable crop (tomato), a cereal crop (barley), and a pasture legume (Medicago). We also considered the interaction of mycorrhizal growth responses with plant-available soil zinc (Zn) and phosphorus (P) concentrations. The appearance of a depression in shoot biomass due to inoculation with AMF occurred at different times for each plant species; depressions appeared earliest in tomato, then Medicago, and then barley. The usually positive-responding Medicago plants were not responsive at the high level of soil available P used. Mycorrhizal growth responsiveness in all three species was also highly interactive with soil Zn supply; tomato growth responded negatively to AMF inoculation in all soil Zn treatments except the toxic soil Zn treatment, where it responded positively. Our results illustrate how context-dependent mycorrhizal growth responses are and the value of HTP approaches to exploring the complexity of mycorrhizal responses.
8
Watts-Williams, S. J., N. Jewell, C. Brien, B. Berger, T. Garnett, and T. R. Cavagnaro. "Using High-Throughput Phenotyping to Explore Growth Responses to Mycorrhizal Fungi and Zinc in Three Plant Species." Plant Phenomics 2019 (March 25, 2019): 1–12. http://dx.doi.org/10.1155/2019/5893953.
Full textAbstract:
There are many reported benefits to plants of arbuscular mycorrhizal fungi (AMF), including positive plant biomass responses; however, AMF can also induce biomass depressions in plants, and this response receives little attention in the literature. High-throughput phenotyping (HTP) technology permits repeated measures of an individual plant’s aboveground biomass. We examined the effect on AMF inoculation on the shoot biomass of three contrasting plant species: a vegetable crop (tomato), a cereal crop (barley), and a pasture legume (Medicago). We also considered the interaction of mycorrhizal growth responses with plant-available soil zinc (Zn) and phosphorus (P) concentrations. The appearance of a depression in shoot biomass due to inoculation with AMF occurred at different times for each plant species; depressions appeared earliest in tomato, then Medicago, and then barley. The usually positive-responding Medicago plants were not responsive at the high level of soil available P used. Mycorrhizal growth responsiveness in all three species was also highly interactive with soil Zn supply; tomato growth responded negatively to AMF inoculation in all soil Zn treatments except the toxic soil Zn treatment, where it responded positively. Our results illustrate how context-dependent mycorrhizal growth responses are and the value of HTP approaches to exploring the complexity of mycorrhizal responses.
9
Dambreville, Anaëlle, Mélanie Griolet, Gaëlle Rolland, Myriam Dauzat, Alexis Bédiée, Crispulo Balsera, Bertrand Muller, Denis Vile, and Christine Granier. "Phenotyping oilseed rape growth-related traits and their responses to water deficit: the disturbing pot size effect." Functional Plant Biology 44, no. 1 (2017): 35. http://dx.doi.org/10.1071/fp16036.
Full textAbstract:
Following the recent development of high-throughput phenotyping platforms for plant research, the number of individual plants grown together in a same experiment has raised, sometimes at the expense of pot size. However, root restriction in excessively small pots affects plant growth and carbon partitioning, and may interact with other stresses targeted in these experiments. In work reported here, we investigated the interactive effects of pot size and soil water deficit on multiple growth-related traits from the cellular to the whole-plant scale in oilseed rape (Brassica napus L.). The effects of pot size on responses to water deficit and allometric relationships revealed strong, multilevel interactions between pot size and watering regime. Notably, water deficit increased the root : shoot ratio in large pots, but not in small pots. At the cellular scale, water deficit decreased epidermal leaf cell area in large pots, but not in small pots. These results were consistent with changes in the level of endoreduplication factor in leaf cells. Our study illustrates the disturbing interaction of pot size with water deficit and raises the need to carefully consider this factor in the frame of the current development of high-throughput phenotyping experiments.
10
Yasrab, Robail, Jincheng Zhang, Polina Smyth, and Michael P. Pound. "Predicting Plant Growth from Time-Series Data Using Deep Learning." Remote Sensing 13, no. 3 (January 20, 2021): 331. http://dx.doi.org/10.3390/rs13030331.
Full textAbstract:
Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of Arabidopsis (A. thaliana) and Brassica rapa (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations.
Dissertations / Theses on the topic "High-throughput shoot phenotyping"
1
Thomas, C. L. "High throughput phenotyping of root and shoot traits in Brassica to identify novel genetic loci for improved crop nutrition." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/43440/.
Full textAbstract:
Despite the success of breeding for high-yielding varieties during and since the ‘Green Revolution’, there are still an ever increasing number of people who suffer from malnutrition, due to both inadequate calorie intake and ‘hidden hunger’ from insufficient essential nutrients. There are also adverse impacts of such high-input, intensive agriculture on the wider environment. It is necessary therefore to focus breeding efforts on improving nutrient uptake and composition of crops, as well as improved yield. Roots have been an under-utilised focus of crop breeding, because of difficulty in observation and accurate measurement. Furthermore, genetic diversity in crop roots may have been lost in commercial varieties because of the focus on above-ground traits and the use of fertilisers. Techniques which can accurately measure phenotypic variation in roots, of a diverse range of germplasm at a high throughput, would increase the potential for identifying novel genetic loci related to improved nutrient uptake and composition. The aim of this PhD was to screen at high throughput in a controlled-environment, the roots of an array of Brassica napus germplasm. The validity of the system to predict field performance, in traits including early vigour, nutrient composition and yield was assessed. Genetic loci underlying variation for the root traits were also investigated. A high throughput screen of the mineral composition of a mutagenised B. rapa population was also conducted, with the aim of identifying mutants with enhanced mineral composition of human essential elements, particularly magnesium. It has been demonstrated that root traits in the high throughput system can predict field performance, particularly primary root length which has the greatest ‘broad-sense heritability’ and relates to early vigour and yield. Lateral root density on the otherhand was found across the studies to relate to mineral composition, particularly of micro-nutrients. Genetic loci underlying root traits, and enhanced magnesium accumulation have been identified, and have potential for use in breeding Brassica with improved mineral nutrition.
2
Ngo, Thi Thanh Hue. "Optimising phosphorus supply to plants by combining inorganic P fertilisers and organic amendments, and the roles of arbuscular mycorrhizas." Thesis, 2021. https://hdl.handle.net/2440/133787.
Full textAbstract:
Inorganic phosphorus (P) fertilisers are used widely to maximise crop yields. However, in many
cases, much of the P fertiliser applied is unavailable to plants due to P adsorption. Consequently,
excess inorganic P fertiliser application often ensues. The consumption of inorganic P fertiliser has
increased over the last five decades causing increasing concern around the availability of mineral P
resources, and growing interest in identifying alternative sources of P to support sustainable
agricultural production. Inorganic P fertilisers, organic P-rich materials and arbuscular mycorrhizal
(AM) fungi are valued for their abilities to supply P to plants. It may be possible to integrate these
three elements to optimise plant P nutrition. To this end, the research presented in this thesis
worked to understand how plants respond to the combined use of inorganic P and organic
amendments as fertilisers, and further, how they interact with the AM symbiosis.
Across four independent experiments, I examined the growth and nutritional responses of
76R (mycorrhizal) and rmc (non-mycorrhizal) tomato plants to different P sources (inorganic,
organic and mixed). In the first experiment, I used inorganic P fertiliser alone, organic P-rich material
alone, or combinations of the two P sources, at a low P application rate. The second experiment
was developed based on the findings of the first experiment, in which three P fertiliser sources were
tested at low, medium and high application rates. The third experiment was established where low
and high nitrogen (N) application rates were investigated together with low and high P application
rates. In addition, shoot growth responses of the plants were examined over time using high
throughput phenotyping. Finally, I formulated six P fertilisers by co-granulating different ratios of
MAP/chicken litter. The N/P ratios of the fertilisers were balanced by adding urea into the
formulations. A MAP only control (no additional urea) was also included. Physical and chemical
characteristics of formulations were measured along with the kinetics of NH4
+-N and P release from
the fertilisers, before testing their impacts on plant growth, nutrition and arbuscular mycorrhizal
responses.
Overall, while the inorganic P source alone led to faster early shoot growth, the later shoot
growth was overtaken by the combined P sources. The combined P sources increased plant growth,
which was explained by gradual P release that was more closely aligned to plant P demand over the
growth cycle. Thus, the combined use of inorganic P and organic P-rich material was a suitable
method to supply P to plants. Furthermore, my research also highlighted an important knowledge
gap on the interacting effects of AM fungi with different P source materials, which may help
improve P use efficiency and crop yields. I also successfully produced combined P source fertilisers
that differed in their nutrient release properties and impacts on plants.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 2021
Book chapters on the topic "High-throughput shoot phenotyping"
1
Berger, Bettina, Bas de Regt, and Mark Tester. "High-Throughput Phenotyping of Plant Shoots." In Methods in Molecular Biology, 9–20. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-995-2_2.
Full text
2
Habyarimana, Ephrem, and Sofia Michailidou. "Genomics Data." In Big Data in Bioeconomy, 69–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_6.
Full textAbstract:
AbstractIn silico prediction of plant performance is gaining increasing breeders’ attention. Several statistical, mathematical and machine learning methodologies for analysis of phenotypic, omics and environmental data typically use individual or a few data layers. Genomic selection is one of the applications, where heterogeneous data, such as those from omics technologies, are handled, accommodating several genetic models of inheritance. There are many new high throughput Next Generation Sequencing (NGS) platforms on the market producing whole-genome data at a low cost. Hence, large-scale genomic data can be produced and analyzed enabling intercrosses and fast-paced recurrent selection. The offspring properties can be predicted instead of manually evaluated in the field . Breeders have a short time window to make decisions by the time they receive data, which is one of the major challenges in commercial breeding. To implement genomic selection routinely as part of breeding programs, data management systems and analytics capacity have therefore to be in order. The traditional relational database management systems (RDBMS), which are designed to store, manage and analyze large-scale data, offer appealing characteristics, particularly when they are upgraded with capabilities for working with binary large objects. In addition, NoSQL systems were considered effective tools for managing high-dimensional genomic data. MongoDB system, a document-based NoSQL database, was effectively used to develop web-based tools for visualizing and exploring genotypic information. The Hierarchical Data Format (HDF5), a member of the high-performance distributed file systems family, demonstrated superior performance with high-dimensional and highly structured data such as genomic sequencing data.
3
Civan, Peter, Renaud Rincent, Alice Danguy-Des-Deserts, Jean-Michel Elsen, and Sophie Bouchet. "Population Genomics Along With Quantitative Genetics Provides a More Efficient Valorization of Crop Plant Genetic Diversity in Breeding and Pre-breeding Programs." In Population Genomics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/13836_2021_97.
Full textAbstract:
AbstractThe breeding efforts of the twentieth century contributed to large increases in yield but selection may have increased vulnerability to environmental perturbations. In that context, there is a growing demand for methodology to re-introduce useful variation into cultivated germplasm. Such efforts can focus on the introduction of specific traits monitored through diagnostic molecular markers identified by QTL/association mapping or selection signature screening. A combined approach is to increase the global diversity of a crop without targeting any particular trait.A considerable portion of the genetic diversity is conserved in genebanks. However, benefits of genetic resources (GRs) in terms of favorable alleles have to be weighed against unfavorable traits being introduced along. In order to facilitate utilization of GR, core collections are being identified and progressively characterized at the phenotypic and genomic levels. High-throughput genotyping and sequencing technologies allow to build prediction models that can estimate the genetic value of an entire genotyped collection. In a pre-breeding program, predictions can accelerate recurrent selection using rapid cycles in greenhouses by skipping some phenotyping steps. In a breeding program, reduced phenotyping characterization allows to increase the number of tested parents and crosses (and global genetic variance) for a fixed budget. Finally, the whole cross design can be optimized using progeny variance predictions to maximize short-term genetic gain or long-term genetic gain by constraining a minimum level of diversity in the germplasm. There is also a potential to further increase the accuracy of genomic predictions by taking into account genotype by environment interactions, integrating additional layers of omics and environmental information.Here, we aim to review some relevant concepts in population genomics together with recent advances in quantitative genetics in order to discuss how the combination of both disciplines can facilitate the use of genetic diversity in plant (pre) breeding programs.
Conference papers on the topic "High-throughput shoot phenotyping"
1
Kumar, Pankaj, Jason Connor, and Stan Mikiavcic. "High-throughput 3D reconstruction of plant shoots for phenotyping." In 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014. http://dx.doi.org/10.1109/icarcv.2014.7064306.
Full textReports on the topic "High-throughput shoot phenotyping"
1
Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
Full textAbstract:
Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.