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 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 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.

scholarly journals High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254908
Author(s):  
Sameer Joshi ◽  
Emily Thoday-Kennedy ◽  
Hans D. Daetwyler ◽  
Matthew Hayden ◽  
German Spangenberg ◽  
...  
Keyword(s):  
Drought Stress ◽  
Drought Tolerance ◽  
High Throughput ◽  
Crop Yields ◽  
Geometric Mean ◽  
Shoot Biomass ◽  
Screening Methods ◽  
Tolerance Index ◽  
Genotypic Differences ◽  
High Throughput Phenotyping

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.

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.

Strategies to increase the yield and yield stability of crops under drought – are we making progress?

2014 ◽  
Vol 41 (11) ◽  
pp. 1199 ◽  
Author(s):  
Neil C. Turner ◽  
Abraham Blum ◽  
Mehmet Cakir ◽  
Pasquale Steduto ◽  
Roberto Tuberosa ◽  
...  
Keyword(s):  
Crop Production ◽  
Crop Yields ◽  
Agricultural Research ◽  
Water Productivity ◽  
Yield Stability ◽  
Growth And Yield ◽  
Plant Responses ◽  
Key Issues ◽  
High Throughput Phenotyping ◽  
Research Centres

The objective of the InterDrought conferences is to be a platform for debating key issues that are relevant for increasing the yield and yield stability of crops under drought via integrated approaches. InterDrought-IV, held in Perth, Australia, in September 2013, followed previous InterDrought conferences in bringing together researchers in agronomy, soil science, modelling, physiology, biochemistry, molecular biology, genetics and plant breeding. Key themes were (i) maximising water productivity; (ii) maximising dryland crop production; (iii) adaptation to water-limited environments; (iv) plant productivity under drought through effective water capture, improved transpiration efficiency, and growth and yield; and (v) breeding for water-limited environments through variety development, and trait-based genomics-assisted and transgenic approaches. This paper highlights some key issues and presents recommendations for future action. Improved agronomic interventions were recognised as being important contributors to improved dryland crop yields in water-limited environments, and new methods for exploring root architecture and water capture were highlighted. The increase in crop yields under drought through breeding and selection, the development of high-throughput phenotyping facilities for field-grown and pot-grown plants, and advances in understanding the molecular basis of plant responses and resistance to drought stress were recognised. Managed environment phenotyping facilities, a range of field environments, modelling, and genomic molecular tools are being used to select and release drought-resistant cultivars of all major crops. Delegates discussed how individuals and small teams can contribute to progress, and concluded that interdisciplinary research, linkages to international agricultural research centres, public–private partnerships and continuation of the InterDrought conferences will be instrumental for progress.

scholarly journals Transcriptomic and Proteomic Analysis of Drought Stress Response in Opium Poppy Plants during the First Week of Germination

Plants ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1878
Author(s):  
Kristýna Kundrátová ◽  
Martin Bartas ◽  
Petr Pečinka ◽  
Ondřej Hejna ◽  
Andrea Rychlá ◽  
...  
Keyword(s):  
Drought Stress ◽  
Stress Response ◽  
Stress Responses ◽  
Proteomic Analysis ◽  
Large Scale ◽  
Crop Yields ◽  
Opium Poppy ◽  
Metabolic Pathway Analysis ◽  
Ubiquitin E3 Ligase ◽  
Key Genes

Water deficiency is one of the most significant abiotic stresses that negatively affects growth and reduces crop yields worldwide. Most research is focused on model plants and/or crops which are most agriculturally important. In this research, drought stress was applied to two drought stress contrasting varieties of Papaver somniferum (the opium poppy), a non-model plant species, during the first week of its germination, which differ in responses to drought stress. After sowing, the poppy seedlings were immediately subjected to drought stress for 7 days. We conducted a large-scale transcriptomic and proteomic analysis for drought stress response. At first, we found that the transcriptomic and proteomic profiles significantly differ. However, the most significant findings are the identification of key genes and proteins with significantly different expressions relating to drought stress, e.g., the heat-shock protein family, dehydration responsive element-binding transcription factors, ubiquitin E3 ligase, and others. In addition, metabolic pathway analysis showed that these genes and proteins were part of several biosynthetic pathways most significantly related to photosynthetic processes, and oxidative stress responses. A future study will focus on a detailed analysis of key genes and the development of selection markers for the determination of drought-resistant varieties and the breeding of new resistant lineages.

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.

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