scholarly journals High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

2021 ◽  
Vol 8 ◽  
Author(s):  
Daoliang Li ◽  
Chaoqun Quan ◽  
Zhaoyang Song ◽  
Xiang Li ◽  
Guanghui Yu ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Agronomic Traits ◽  
Global Climate ◽  
Plant Phenotyping ◽  
Phenotypic Data ◽  
Operating Modes ◽  
Sensing Platform ◽  
Exploitation And Utilization ◽  
High Throughput Phenotyping

Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

scholarly journals High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

scholarly journals SorGSD: updating and expanding the sorghum genome science database with new contents and tools

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yuanming Liu ◽  
Zhonghuang Wang ◽  
Xiaoyuan Wu ◽  
Junwei Zhu ◽  
Hong Luo ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Agronomic Traits ◽  
Genetic Research ◽  
General Information ◽  
Genomic Variation ◽  
Nucleotide Polymorphisms ◽  
Sorghum Genome ◽  
Online Data ◽  
Phenotypic Data

Abstract Background As the fifth major cereal crop originated from Africa, sorghum (Sorghum bicolor) has become a key C4 model organism for energy plant research. With the development of high-throughput detection technologies for various omics data, much multi-dimensional and multi-omics information has been accumulated for sorghum. Integrating this information may accelerate genetic research and improve molecular breeding for sorghum agronomic traits. Results We updated the Sorghum Genome SNP Database (SorGSD) by adding new data, new features and renamed it to Sorghum Genome Science Database (SorGSD). In comparison with the original version SorGSD, which contains SNPs from 48 sorghum accessions mapped to the reference genome BTx623 (v2.1), the new version was expanded to 289 sorghum lines with both single nucleotide polymorphisms (SNPs) and small insertions/deletions (INDELs), which were aligned to the newly assembled and annotated sorghum genome BTx623 (v3.1). Moreover, phenotypic data and panicle pictures of critical accessions were provided in the new version. We implemented new tools including ID Conversion, Homologue Search and Genome Browser for analysis and updated the general information related to sorghum research, such as online sorghum resources and literature references. In addition, we deployed a new database infrastructure and redesigned a new user interface as one of the Genome Variation Map databases. The new version SorGSD is freely accessible online at http://ngdc.cncb.ac.cn/sorgsd/. Conclusions SorGSD is a comprehensive integration with large-scale genomic variation, phenotypic information and incorporates online data analysis tools for data mining, genome navigation and analysis. We hope that SorGSD could provide a valuable resource for sorghum researchers to find variations they are interested in and generate customized high-throughput datasets for further analysis.

scholarly journals PhenoImage: an open-source GUI for plant image analysis

2020 ◽  
Author(s):  
Feiyu Zhu ◽  
Manny Saluja ◽  
Jaspinder Singh ◽  
Puneet Paul ◽  
Scott E. Sattler ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Plant Species ◽  
High Throughput ◽  
Large Scale ◽  
Imaging System ◽  
Crop Improvement ◽  
Plant Phenotyping ◽  
High Throughput Phenotyping ◽  
Improvement Programs

AbstractHigh-throughput genotyping coupled with molecular breeding approaches has dramatically accelerated crop improvement programs. More recently, improved plant phenotyping methods have led to a shift from manual measurements to automated platforms with increased scalability and resolution. Considerable effort has also gone into the development of large-scale downstream processing of the imaging datasets derived from high-throughput phenotyping (HTP) platforms. However, most available tools require some programing skills. We developed PhenoImage – an open-source GUI based cross-platform solution for HTP image processing with the aim to make image analysis accessible to users with either little or no programming skills. The open-source nature provides the possibility to extend its usability to meet user-specific requirements. The availability of multiple functions and filtering parameters provides flexibility to analyze images from a wide variety of plant species and platforms. PhenoImage can be run on a personal computer as well as on high-performance computing clusters. To test the efficacy of the application, we analyzed the LemnaTec Imaging system derived RGB and fluorescence shoot images from two plant species: sorghum and wheat differing in their physical attributes. In the study, we discuss the development, implementation, and working of the PhenoImage.HighlightPhenoImage is an open-source application designed for analyzing images derived from high-throughput phenotyping.

scholarly journals OpenStats: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput Phenotypic Data

2020 ◽  
Author(s):  
Hamed Haselimashhadi ◽  
Jeremy C Mason ◽  
Ann-Marie Mallon ◽  
Damian Smedley ◽  
Terrence F Meehan ◽  
...  
Keyword(s):  
High Throughput ◽  
Software Package ◽  
Computational Time ◽  
Phenotypic Data ◽  
Creative Commons ◽  
International Mouse Phenotyping Consortium ◽  
Software Packages ◽  
Current Production ◽  
High Throughput Phenotyping ◽  
Reproducible Analysis

AbstractReproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.

scholarly journals Large-scale, high-throughput screening for coagulation and hematologic phenotypes in mice*

2002 ◽  
Vol 11 (3) ◽  
pp. 185-193 ◽  
Author(s):  
Luanne L. Peters ◽  
Eleanor M. Cheever ◽  
Heather R. Ellis ◽  
Phyllis A. Magnani ◽  
Karen L. Svenson ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Inbred Mouse ◽  
Inbred Strains ◽  
Sample Volume ◽  
Small Sample ◽  
Mouse Strains ◽  
Phenotypic Data ◽  
Multiple Tests

The Mouse Phenome Project is an international effort to systematically gather phenotypic data for a defined set of inbred mouse strains. For such large-scale projects the development of high-throughput screening protocols that allow multiple tests to be performed on a single mouse is essential. Here we report hematologic and coagulation data for more than 30 inbred strains. Complete blood counts were performed using an Advia 120 analyzer. For coagulation testing, we successfully adapted the Dade Behring BCS automated coagulation analyzer for use in mice by lowering sample and reagent volume requirements. Seven automated assay procedures were developed. Small sample volume requirements make it possible to perform multiple tests on a single animal without euthanasia, while reductions in reagent volume requirements reduce costs. The data show that considerable variation in many basic hematological and coagulation parameters exists among the inbred strains. These data, freely available on the World Wide Web, allow investigators to knowledgeably select the most appropriate strain(s) to meet their individual study designs and goals.

scholarly journals Predicting plant biomass accumulation from image-derived parameters

2016 ◽  
Author(s):  
Dijun Chen ◽  
Rongli Shi ◽  
Jean-Michel Pape ◽  
Christian Klukas
Keyword(s):  
High Throughput ◽  
Plant Biomass ◽  
Quantitative Relationship ◽  
Biomass Accumulation ◽  
Plant Phenotyping ◽  
Suitable Model ◽  
The European Union ◽  
Relative Contribution ◽  
Crop Plant Research ◽  
High Throughput Phenotyping

AbstractImage-based high-throughput phenotyping technologies have been rapidly developed in plant science recently and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologist. However, it is a great challenge to find a suitable model to predict plant biomass in the context of high-throughput phenotyping. In the present study, we constructed several models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to three consecutive barley experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model, in particular the cross-experiment performance, is promising to relieve the phenotyping bottleneck in biomass measurement in breeding applications. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of plant biomass outcome. What’s more, the methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass.One-sentence SummaryWe demonstrated that plant biomass can be accurately predicted from image-based parameters in the context of high-throughput phenotyping.FootnotesThis work was supported by the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), the Robert Bosch Stiftung (32.5.8003.0116.0) and the Federal Agency for Agriculture and Food (BEL, 15/12-13, 530-06.01-BiKo CHN) and the Federal Ministry of Education and Research (BMBF, 0315958A and 031A053B). This research was furthermore enabled with support of the European Plant Phenotyping Network (EPPN, grant agreement no. 284443) funded by the FP7 Research Infrastructures Programme of the European Union.

scholarly journals Conventional and hyperspectral time-series imaging of maize lines widely used in field trials

2017 ◽  
Author(s):  
Zhikai Liang ◽  
Piyush Pandey ◽  
Vincent Stoerger ◽  
Yuhang Xu ◽  
Yumou Qiu ◽  
...  
Keyword(s):  
High Throughput ◽  
Measurement Errors ◽  
Hyperspectral Image ◽  
Image Data ◽  
Field Trials ◽  
Plant Phenotyping ◽  
Wide Range ◽  
High Throughput Phenotyping ◽  
University Of Nebraska Lincoln ◽  
The Cost

ABSTRACTMaize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds – primarily recently off patent lines – were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.

scholarly journals Automatic Traits Extraction and Fitting for Field High-throughput Phenotyping Systems

2020 ◽  
Author(s):  
Xingche Guo ◽  
Yumou Qiu ◽  
Dan Nettleton ◽  
Cheng-Ting Yeh ◽  
Zihao Zheng ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Plant Traits ◽  
Network Models ◽  
Modern Technology ◽  
Neural Network Models ◽  
Novel Approach ◽  
Field Phenotyping ◽  
High Throughput Phenotyping ◽  
Rich Data

ABSTRACTHigh-throughput phenotyping is a modern technology to measure plant traits efficiently and in large scale by imaging systems over the whole growth season. Those images provide rich data for statistical analysis of plant phenotypes. We propose a pipeline to extract and analyze the plant traits for field phenotyping systems. The proposed pipeline include the following main steps: plant segmentation from field images, automatic calculation of plant traits from the segmented images, and functional curve fitting for the extracted traits. To deal with the challenging problem of plant segmentation for field images, we propose a novel approach on image pixel classification by transform domain neural network models, which utilizes plant pixels from greenhouse images to train a segmentation model for field images. Our results show the proposed procedure is able to accurately extract plant heights and is more stable than results from Amazon Turks, who manually measure plant heights from original images.

An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems

2020 ◽  
Vol 63 (4) ◽  
pp. 1133-1146
Author(s):  
Beichen Lyu ◽  
Stuart D. Smith ◽  
Yexiang Xue ◽  
Katy M. Rainey ◽  
Keith Cherkauer
Keyword(s):  
Data Processing ◽  
High Throughput ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Unmanned Aerial Systems ◽  
Growth Stages ◽  
Processing Pipeline ◽  
High Throughput Phenotyping ◽  
Aerial Systems

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

Pengembangan Nontransgenik F1 dan Bc1f1 Padi Ciherang Toleran Genangan secara Site-Directed Crossing

2014 ◽  
Vol 1 (3) ◽  
pp. 136-145
Author(s):  
Djarot Sasongko Hami Seno ◽  
Satya Nugroho ◽  
Tri Joko Santoso ◽  
Joel Rivandi Sinaga ◽  
Euis Marlina ◽  
...  
Keyword(s):  
Large Scale ◽  
Agronomic Traits ◽  
Global Climate ◽  
Rice Variety ◽  
Submergence Tolerance ◽  
Rice Varieties ◽  
Global Climate Changes ◽  
Tolerance Gene ◽  
The Stability ◽  
Large Scale Cultivation

The development of submergence tolerant rice varieties is urgently required to maintain the stability of future food production, to anticipate the unpredictable global climate changes. Due to in-economical agronomic traits of native submergence tolerant varieties for large scale cultivation, submergence tolerance gene (sub1) must be introduced into popular high-yielding rice variety, such as Ciherang. To develop new submergence tolerant variety with good agronomic traits as those of Ciherang, in this research, submergence tolerance gene (sub1) was introduced into Ciherang variety. To avoid strict GMO regulation, gene introduction was carried out through site-directed crossing. Donor sub1 was crossed with Ciherang host. The selected F1 progenies were further backcrossed to Ciherang 4 x to obtain BC5F1 progeny having ~98% agronomic traits of those of Ciherang. In every cross/backcross generation, submergence test was performed, followed by sub1 marker-assisted PCR. F1 and BC1F1 submergence-tolerant Ciherang were successfully constructed. Co-dominant RM464A marker was not able to discriminate between host, donor, and progenies (F1 and BC1). Co-dominant RM219 maker showed slightly different size between donor and host amplicon, but it was difficult to see their heterozygous progenies. Both C173 and AEX1 dominant markers were able to show sub1 introgression from donor to host. PCR results confirmed that progenies-submergence tolerance was due to sub1 introgression, not escape mechanisms. AEX1 was chosen for subsequent experiments. Backcross until BC5 is in progress, to obtain maximum host retention for engineering new submergence tolerant varieties with good agronomic traits as those of Ciherang.

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