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van Hooren M, van Wijk R, Vaseva II, Van Der Straeten D, Haring M, Munnik T. Ectopic Expression of Distinct PLC Genes Identifies 'Compactness' as a Possible Architectural Shoot Strategy to Cope with Drought Stress. PLANT & CELL PHYSIOLOGY 2024; 65:885-903. [PMID: 37846160 PMCID: PMC11209554 DOI: 10.1093/pcp/pcad123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 09/13/2023] [Accepted: 11/13/2023] [Indexed: 10/18/2023]
Abstract
Phospholipase C (PLC) has been implicated in several stress responses, including drought. Overexpression (OE) of PLC has been shown to improve drought tolerance in various plant species. Arabidopsis contains nine PLC genes, which are subdivided into four clades. Earlier, OE of PLC3, PLC5 or PLC7 was found to increase Arabidopsis' drought tolerance. Here, we confirm this for three other PLCs: PLC2, the only constitutively expressed AtPLC; PLC4, reported to have reduced salt tolerance and PLC9, of which the encoded enzyme was presumed to be catalytically inactive. To compare each PLC and to discover any other potential phenotype, two independent OE lines of six AtPLC genes, representing all four clades, were simultaneously monitored with the GROWSCREEN-FLUORO phenotyping platform, under both control- and mild-drought conditions. To investigate which tissues were most relevant to achieving drought survival, we additionally expressed AtPLC5 using 13 different cell- or tissue-specific promoters. While no significant differences in plant size, biomass or photosynthesis were found between PLC lines and wild-type (WT) plants, all PLC-OE lines, as well as those tissue-specific lines that promoted drought survival, exhibited a stronger decrease in 'convex hull perimeter' (= increase in 'compactness') under water deprivation compared to WT. Increased compactness has not been associated with drought or decreased water loss before although a hyponastic decrease in compactness in response to increased temperatures has been associated with water loss. We propose that the increased compactness could lead to decreased water loss and potentially provide a new breeding trait to select for drought tolerance.
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Affiliation(s)
- Max van Hooren
- Plant Cell Biology, Swammerdam Institute for Life Sciences, University of Amsterdam, PO Box 1210, Amsterdam 1000BE, The Netherlands
| | - Ringo van Wijk
- Plant Cell Biology, Swammerdam Institute for Life Sciences, University of Amsterdam, PO Box 1210, Amsterdam 1000BE, The Netherlands
| | - Irina I Vaseva
- Laboratory of Functional Plant Biology, Department of Biology, Ghent University, K.L. Ledeganckstraat 35, Ghent B-9000, Belgium
| | - Dominique Van Der Straeten
- Laboratory of Functional Plant Biology, Department of Biology, Ghent University, K.L. Ledeganckstraat 35, Ghent B-9000, Belgium
| | - Michel Haring
- Plant Physiology, Swammerdam Institute for Life Sciences, University of Amsterdam, PO Box 1210, Amsterdam 1000BE, The Netherlands
| | - Teun Munnik
- Plant Cell Biology, Swammerdam Institute for Life Sciences, University of Amsterdam, PO Box 1210, Amsterdam 1000BE, The Netherlands
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Lee JH, Lee U, Yoo JH, Lee TS, Jung JH, Kim HS. AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings. PLANT METHODS 2024; 20:44. [PMID: 38493119 PMCID: PMC10943777 DOI: 10.1186/s13007-024-01171-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis. RESULTS Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity. CONCLUSIONS AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.
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Grants
- Grant No. 2022R1C1C1012137 The National Research Foundation of Korea
- Grant No. 421002-04) The Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) and Korea Smart Farm R&D (KosFarm) through the Smart Farm Innovation Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Ministry of Science and ICT (MSIT), Rural Development Administration (RDA)
- The Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) and Korea Smart Farm R&D (KosFarm) through the Smart Farm Innovation Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Ministry of Science and ICT (MSIT), Rural Development Administration (RDA)
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Affiliation(s)
- Jae Hoon Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Unseok Lee
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Ji Hye Yoo
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Taek Sung Lee
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Je Hyeong Jung
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Hyoung Seok Kim
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea.
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Zsigmond L, Juhász-Erdélyi A, Valkai I, Aleksza D, Rigó G, Kant K, Szepesi Á, Fiorani F, Körber N, Kovács L, Szabados L. Mitochondrial complex I subunit NDUFS8.2 modulates responses to stresses associated with reduced water availability. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2024; 208:108466. [PMID: 38428158 DOI: 10.1016/j.plaphy.2024.108466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 03/03/2024]
Abstract
Mitochondria are important sources of energy in plants and are implicated in coordination of a number of metabolic and physiological processes including stabilization of redox balance, synthesis and turnover of a number of metabolites, and control of programmed cell death. Mitochondrial electron transport chain (mETC) is the backbone of the energy producing process which can influence other processes as well. Accumulating evidence suggests that mETC can affect responses to environmental stimuli and modulate tolerance to extreme conditions such as drought or salinity. Screening for stress responses of 13 Arabidopsis mitochondria-related T-DNA insertion mutants, we identified ndufs8.2-1 which has an increased ability to withstand osmotic and oxidative stresses compared to wild type plants. Insertion in ndufs8.2-1 disrupted the gene that encodes the NADH dehydrogenase [ubiquinone] fragment S subunit 8 (NDUFS8) a component of Complex I of mETC. ndufs8.2-1 tolerated reduced water availability, retained photosynthetic activity and recovered from severe water stress with higher efficiency compared to wild type plants. Several mitochondrial functions were altered in the mutant including oxygen consumption, ROS production, ATP and ADP content as well as activities of genes encoding alternative oxidase 1A (AOX1A) and various alternative NAD(P)H dehydrogenases (ND). Our results suggest that in the absence of NDUFS8.2 stress-induced ROS generation is restrained leading to reduced oxidative damage and improved tolerance to water deficiency. mETC components can be implicated in redox and energy homeostasis and modulate responses to stresses associated with reduced water availability.
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Affiliation(s)
- Laura Zsigmond
- Institute of Plant Biology, HUN-REN Biological Research Centre, Szeged, Hungary.
| | - Annabella Juhász-Erdélyi
- Department of Plant Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary
| | - Ildikó Valkai
- Institute of Plant Biology, HUN-REN Biological Research Centre, Szeged, Hungary
| | - Dávid Aleksza
- Institute of Plant Biology, HUN-REN Biological Research Centre, Szeged, Hungary
| | - Gábor Rigó
- Institute of Plant Biology, HUN-REN Biological Research Centre, Szeged, Hungary
| | - Kamal Kant
- Institute of Plant Biology, HUN-REN Biological Research Centre, Szeged, Hungary
| | - Ágnes Szepesi
- Department of Plant Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary
| | - Fabio Fiorani
- Institute of Bio- and Geo-Sciences, IBG2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Niklas Körber
- Nunhems - BASF Vegetable Seeds, Department of Data Science and Technology, Roermond, Netherlands
| | - László Kovács
- Institute of Plant Biology, HUN-REN Biological Research Centre, Szeged, Hungary
| | - László Szabados
- Institute of Plant Biology, HUN-REN Biological Research Centre, Szeged, Hungary
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4
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Meraj T, Sharif MI, Raza M, Alabrah A, Kadry S, Gandomi AH. Computer vision-based plants phenotyping: A comprehensive survey. iScience 2024; 27:108709. [PMID: 38269095 PMCID: PMC10805646 DOI: 10.1016/j.isci.2023.108709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024] Open
Abstract
The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders need computer vision-based plant monitoring systems to analyze different plants' productivity and environmental suitability. It leads to performing feasible quantitative analysis, geometric analysis, and yield rate analysis of the plants. Many of the data collection methods have been used by plant breeders according to their needs. In the presented review, most of them are discussed with their corresponding challenges and limitations. Furthermore, the traditional approaches of segmentation and classification of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted, which somehow solve the problem but are not genuine. The available datasets and current issues are enlightened. The presented study covers the plants phenotyping problems, suggested solutions, and current challenges from data collection to classification steps.
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Affiliation(s)
- Talha Meraj
- Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan
| | - Muhammad Imran Sharif
- Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan
| | - Amerah Alabrah
- Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Amir H. Gandomi
- Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
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5
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Haq SAU, Bashir T, Roberts TH, Husaini AM. Ameliorating the effects of multiple stresses on agronomic traits in crops: modern biotechnological and omics approaches. Mol Biol Rep 2023; 51:41. [PMID: 38158512 DOI: 10.1007/s11033-023-09042-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 10/13/2023] [Indexed: 01/03/2024]
Abstract
While global climate change poses a significant environmental threat to agriculture, the increasing population is another big challenge to food security. To address this, developing crop varieties with increased productivity and tolerance to biotic and abiotic stresses is crucial. Breeders must identify traits to ensure higher and consistent yields under inconsistent environmental challenges, possess resilience against emerging biotic and abiotic stresses and satisfy customer demands for safer and more nutritious meals. With the advent of omics-based technologies, molecular tools are now integrated with breeding to understand the molecular genetics of genotype-based traits and develop better climate-smart crops. The rapid development of omics technologies offers an opportunity to generate novel datasets for crop species. Identifying genes and pathways responsible for significant agronomic traits has been made possible by integrating omics data with genetic and phenotypic information. This paper discusses the importance and use of omics-based strategies, including genomics, transcriptomics, proteomics and phenomics, for agricultural and horticultural crop improvement, which aligns with developing better adaptability in these crop species to the changing climate conditions.
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Affiliation(s)
- Syed Anam Ul Haq
- Genome Engineering and Societal Biotechnology Lab, Division of Plant Biotechnology, SKUAST-K, Shalimar, Srinagar, Jammu and Kashmir, 190025, India
| | - Tanzeel Bashir
- Genome Engineering and Societal Biotechnology Lab, Division of Plant Biotechnology, SKUAST-K, Shalimar, Srinagar, Jammu and Kashmir, 190025, India
| | - Thomas H Roberts
- Plant Breeding Institute, School of Life and Environmental Sciences, Faculty of Science, Sydney Institute of Agriculture, The University of Sydney, Eveleigh, Australia
| | - Amjad M Husaini
- Genome Engineering and Societal Biotechnology Lab, Division of Plant Biotechnology, SKUAST-K, Shalimar, Srinagar, Jammu and Kashmir, 190025, India.
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Meyer RC, Weigelt-Fischer K, Tschiersch H, Topali G, Altschmied L, Heuermann MC, Knoch D, Kuhlmann M, Zhao Y, Altmann T. Dynamic growth QTL action in diverse light environments: characterization of light regime-specific and stable QTL in Arabidopsis. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5341-5362. [PMID: 37306093 DOI: 10.1093/jxb/erad222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/10/2023] [Indexed: 06/13/2023]
Abstract
Plant growth is a complex process affected by a multitude of genetic and environmental factors and their interactions. To identify genetic factors influencing plant performance under different environmental conditions, vegetative growth was assessed in Arabidopsis thaliana cultivated under constant or fluctuating light intensities, using high-throughput phenotyping and genome-wide association studies. Daily automated non-invasive phenotyping of a collection of 382 Arabidopsis accessions provided growth data during developmental progression under different light regimes at high temporal resolution. Quantitative trait loci (QTL) for projected leaf area, relative growth rate, and PSII operating efficiency detected under the two light regimes were predominantly condition-specific and displayed distinct temporal activity patterns, with active phases ranging from 2 d to 9 d. Eighteen protein-coding genes and one miRNA gene were identified as potential candidate genes at 10 QTL regions consistently found under both light regimes. Expression patterns of three candidate genes affecting projected leaf area were analysed in time-series experiments in accessions with contrasting vegetative leaf growth. These observations highlight the importance of considering both environmental and temporal patterns of QTL/allele actions and emphasize the need for detailed time-resolved analyses under diverse well-defined environmental conditions to effectively unravel the complex and stage-specific contributions of genes affecting plant growth processes.
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Affiliation(s)
- Rhonda C Meyer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Kathleen Weigelt-Fischer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Henning Tschiersch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Georgia Topali
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Lothar Altschmied
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Marc C Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Dominic Knoch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Markus Kuhlmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Breeding Research, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
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7
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Poorter H, Hummel GM, Nagel KA, Fiorani F, von Gillhaussen P, Virnich O, Schurr U, Postma JA, van de Zedde R, Wiese-Klinkenberg A. Pitfalls and potential of high-throughput plant phenotyping platforms. FRONTIERS IN PLANT SCIENCE 2023; 14:1233794. [PMID: 37680357 PMCID: PMC10481964 DOI: 10.3389/fpls.2023.1233794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/01/2023] [Indexed: 09/09/2023]
Abstract
Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.
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Affiliation(s)
- Hendrik Poorter
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Natural Sciences, Macquarie University, North Ryde, NSW, Australia
| | | | - Kerstin A. Nagel
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Fabio Fiorani
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Olivia Virnich
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Ulrich Schurr
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | - Anika Wiese-Klinkenberg
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Bioinformatics (IBG-4), Forschungszentrum Jülich GmbH, Jülich, Germany
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8
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Wada KC, Hayashi A, Lee U, Tanabata T, Isobe S, Itoh H, Maeda H, Fujisako S, Kochi N. A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling-A Case Study in Leaf Lettuce. SENSORS (BASEL, SWITZERLAND) 2023; 23:6825. [PMID: 37571608 PMCID: PMC10422436 DOI: 10.3390/s23156825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 07/24/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023]
Abstract
Three-dimensional measurement is a high-throughput method that can record a large amount of information. Three-dimensional modelling of plants has the possibility to not only automate dimensional measurement, but to also enable visual assessment to be quantified, eliminating ambiguity in human judgment. In this study, we have developed new methods that could be used for the morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The latter is particularly novel in this paper. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The coefficient of determination between manual measurements and the scale-related methods were all above 0.9. Furthermore, the differences in LC calculated from the normal vector data allowed us to visualise and quantify the concavity and convexity of leaves. This technique revealed that there were differences in the time point at which leaf blistering began to develop among the varieties. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment. This research paper was able to demonstrate the following possibilities as outcomes: (1) the automation of conventional manual measurements, and (2) the elimination of variability caused by human subjectivity, thereby rendering evaluations by skilled experts unnecessary.
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Affiliation(s)
- Kaede C Wada
- Breeding Big Data Management and Utilization Group, Division of Smart Breeding Research, Institute of Crop Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0856, Japan
| | - Atsushi Hayashi
- Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan
| | - Unseok Lee
- Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan
| | - Takanari Tanabata
- Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, Kisarazu 292-0818, Japan
| | - Sachiko Isobe
- Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, Kisarazu 292-0818, Japan
| | - Hironori Itoh
- Breeding Big Data Management and Utilization Group, Division of Smart Breeding Research, Institute of Crop Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0856, Japan
| | - Hideki Maeda
- Center for Seeds and Seedlings, Nishinihon Station (NARO), Kasaoka 714-0054, Japan
| | - Satoshi Fujisako
- Center for Seeds and Seedlings, Nishinihon Station (NARO), Kasaoka 714-0054, Japan
| | - Nobuo Kochi
- Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan
- Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, Kisarazu 292-0818, Japan
- R&D Initiative, Chuo University, Kasuga, Tokyo 112-8551, Japan
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9
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Zhang C, Kong J, Wu D, Guan Z, Ding B, Chen F. Wearable Sensor: An Emerging Data Collection Tool for Plant Phenotyping. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0051. [PMID: 37408737 PMCID: PMC10318905 DOI: 10.34133/plantphenomics.0051] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/09/2023] [Indexed: 07/07/2023]
Abstract
The advancement of plant phenomics by using optical imaging-based phenotyping techniques has markedly improved breeding and crop management. However, there remains a challenge in increasing the spatial resolution and accuracy due to their noncontact measurement mode. Wearable sensors, an emerging data collection tool, present a promising solution to address these challenges. By using a contact measurement mode, wearable sensors enable in-situ monitoring of plant phenotypes and their surrounding environments. Although a few pioneering works have been reported in monitoring plant growth and microclimate, the utilization of wearable sensors in plant phenotyping has yet reach its full potential. This review aims to systematically examine the progress of wearable sensors in monitoring plant phenotypes and the environment from an interdisciplinary perspective, including materials science, signal communication, manufacturing technology, and plant physiology. Additionally, this review discusses the challenges and future directions of wearable sensors in the field of plant phenotyping.
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Affiliation(s)
- Cheng Zhang
- College of Engineering,
Nanjing Agricultural University, Nanjing 210095, China
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture,
Nanjing Agricultural University, Nanjing 210095, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing 210014, China
| | - Jingjing Kong
- College of Engineering,
Nanjing Agricultural University, Nanjing 210095, China
| | - Daosheng Wu
- College of Engineering,
Nanjing Agricultural University, Nanjing 210095, China
| | - Zhiyong Guan
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture,
Nanjing Agricultural University, Nanjing 210095, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing 210014, China
| | - Baoqing Ding
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture,
Nanjing Agricultural University, Nanjing 210095, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing 210014, China
| | - Fadi Chen
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture,
Nanjing Agricultural University, Nanjing 210095, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing 210014, China
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10
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Krishna TPA, Veeramuthu D, Maharajan T, Soosaimanickam M. The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement. Curr Genomics 2023; 24:24-35. [PMID: 37920729 PMCID: PMC10334699 DOI: 10.2174/1389202924666230517115912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 11/04/2023] Open
Abstract
Plant breeding has made a significant contribution to increasing agricultural production. Conventional breeding based on phenotypic selection is not effective for crop improvement. Because phenotype is considerably influenced by environmental factors, which will affect the selection of breeding materials for crop improvement. The past two decades have seen tremendous progress in plant breeding research. Especially the availability of high-throughput molecular markers followed by genomic-assisted approaches significantly contributed to advancing plant breeding. Integration of speed breeding with genomic and phenomic facilities allowed rapid quantitative trait loci (QTL)/gene identifications and ultimately accelerated crop improvement programs. The advances in sequencing technology helps to understand the genome organization of many crops and helped with genomic selection in crop breeding. Plant breeding has gradually changed from phenotype-to-genotype-based to genotype-to-phenotype-based selection. High-throughput phenomic platforms have played a significant role in the modern breeding program and are considered an essential part of precision breeding. In this review, we discuss the rapid advance in plant breeding technology for efficient crop improvements and provide details on various approaches/platforms that are helpful for crop improvement. This review will help researchers understand the recent developments in crop breeding and improvements.
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Affiliation(s)
| | - Duraipandiyan Veeramuthu
- Division of Plant Biotechnology, Entomology Research Institute, Loyola College, Chennai, Tamil Nadu, India
| | - Theivanayagam Maharajan
- Division of Plant Biotechnology, Entomology Research Institute, Loyola College, Chennai, Tamil Nadu, India
| | - Mariapackiam Soosaimanickam
- Division of Plant Biotechnology, Entomology Research Institute, Loyola College, Chennai, Tamil Nadu, India
- Department of Advanced Zoology & Biotechnology, Loyola College, Nungambakkam, Chennai, 600034, India
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11
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Roychowdhury R, Das SP, Gupta A, Parihar P, Chandrasekhar K, Sarker U, Kumar A, Ramrao DP, Sudhakar C. Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses. Genes (Basel) 2023; 14:1281. [PMID: 37372461 DOI: 10.3390/genes14061281] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/03/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.
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Affiliation(s)
- Rajib Roychowdhury
- Department of Plant Pathology and Weed Research, Institute of Plant Protection, Agricultural Research Organization (ARO)-The Volcani Institute, Rishon Lezion 7505101, Israel
| | - Soumya Prakash Das
- School of Bioscience, Seacom Skills University, Bolpur 731236, West Bengal, India
| | - Amber Gupta
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology, Faculty of Science, Maharaja Sayajirao University of Baroda, Vadodara 390002, Gujarat, India
| | - Parul Parihar
- Department of Biotechnology and Bioscience, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Kottakota Chandrasekhar
- Department of Plant Biochemistry and Biotechnology, Sri Krishnadevaraya College of Agricultural Sciences (SKCAS), Affiliated to Acharya N.G. Ranga Agricultural University (ANGRAU), Guntur 522034, Andhra Pradesh, India
| | - Umakanta Sarker
- Department of Genetics and Plant Breeding, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
| | - Ajay Kumar
- Department of Botany, Maharshi Vishwamitra (M.V.) College, Buxar 802102, Bihar, India
| | - Devade Pandurang Ramrao
- Department of Biotechnology, Mizoram University, Pachhunga University College Campus, Aizawl 796001, Mizoram, India
| | - Chinta Sudhakar
- Plant Molecular Biology Laboratory, Department of Botany, Sri Krishnadevaraya University, Anantapur 515003, Andhra Pradesh, India
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12
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Hu Y, Schmidhalter U. Opportunity and challenges of phenotyping plant salt tolerance. TRENDS IN PLANT SCIENCE 2023; 28:552-566. [PMID: 36628656 DOI: 10.1016/j.tplants.2022.12.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 12/03/2022] [Accepted: 12/15/2022] [Indexed: 05/22/2023]
Abstract
Salinity is a key factor limiting agricultural production worldwide. Recent advances in field phenotyping have enabled the recording of the environmental history and dynamic response of plants by considering both genotype × environment (G×E) interactions and envirotyping. However, only a few studies have focused on plant salt tolerance phenotyping. Therefore, we analyzed the potential opportunities and major challenges in improving plant salt tolerance using advanced field phenotyping technologies. RGB imaging and spectral and thermal sensors are the most useful and important sensing techniques for assessing key morphological and physiological traits of plant salt tolerance. However, field phenotyping faces challenges owing to its practical applications and high costs, limiting its use in early generation breeding and in developing countries.
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Affiliation(s)
- Yuncai Hu
- Chair of Plant Nutrition, School of Life Sciences, Technical University of Munich, D-85354 Freising, Germany.
| | - Urs Schmidhalter
- Chair of Plant Nutrition, School of Life Sciences, Technical University of Munich, D-85354 Freising, Germany
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13
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Ye D, Wu L, Li X, Atoba TO, Wu W, Weng H. A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping. PLANTS (BASEL, SWITZERLAND) 2023; 12:1698. [PMID: 37111921 PMCID: PMC10146287 DOI: 10.3390/plants12081698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/08/2023] [Accepted: 04/16/2023] [Indexed: 06/19/2023]
Abstract
Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.
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Affiliation(s)
- Dapeng Ye
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Libin Wu
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Xiaobin Li
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Tolulope Opeyemi Atoba
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Wenhao Wu
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Haiyong Weng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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14
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Li J, Mintgen MAC, D'Haeyer S, Helfer A, Nelissen H, Inzé D, Dhondt S. PhenoWell®-A novel screening system for soil-grown plants. PLANT-ENVIRONMENT INTERACTIONS (HOBOKEN, N.J.) 2023; 4:55-69. [PMID: 37288161 PMCID: PMC10243540 DOI: 10.1002/pei3.10098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 06/09/2023]
Abstract
As agricultural production is reaching its limits regarding outputs and land use, the need to further improve crop yield is greater than ever. The limited translatability from in vitro lab results into more natural growth conditions in soil remains problematic. Although considerable progress has been made in developing soil-growth assays to tackle this bottleneck, the majority of these assays use pots or whole trays, making them not only space- and resource-intensive, but also hampering the individual treatment of plants. Therefore, we developed a flexible and compact screening system named PhenoWell® in which individual seedlings are grown in wells filled with soil allowing single-plant treatments. The system makes use of an automated image-analysis pipeline that extracts multiple growth parameters from individual seedlings over time, including projected rosette area, relative growth rate, compactness, and stockiness. Macronutrient, hormone, salt, osmotic, and drought stress treatments were tested in the PhenoWell® system. The system is also optimized for maize with results that are consistent with Arabidopsis while different in amplitude. We conclude that the PhenoWell® system enables a high-throughput, precise, and uniform application of a small amount of solution to individually soil-grown plants, which increases the replicability and reduces variability and compound usage.
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Affiliation(s)
- Ji Li
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- Center for Plant Systems BiologyVIBGhentBelgium
| | - Michael A. C. Mintgen
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- Center for Plant Systems BiologyVIBGhentBelgium
| | - Sam D'Haeyer
- Discovery SciencesVIBGhentBelgium
- Screening CoreVIBGhentBelgium
| | | | - Hilde Nelissen
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- Center for Plant Systems BiologyVIBGhentBelgium
| | - Dirk Inzé
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- Center for Plant Systems BiologyVIBGhentBelgium
| | - Stijn Dhondt
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- Center for Plant Systems BiologyVIBGhentBelgium
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15
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Herrera-Romero B, Almeida-Galarraga D, Salum GM, Villalba-Meneses F, Gudino-Gomezjurado ME. GUSignal: An Informatics Tool to Analyze Glucuronidase Gene Expression in Arabidopsis Thaliana Roots. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1073-1080. [PMID: 35830410 DOI: 10.1109/tcbb.2022.3190427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The uidA gene codifies for a glucuronidase (GUS) enzyme which has been used as a biotechnological tool during the last years. When uidA gene is fused to a gene's promotor region, it is possible to evaluate the activity of this one in response to a stimulus. Arabidopsis thaliana has served as the biological platform to elucidate molecular and regulatory signaling responses in plants. Transgenic lines of A. thaliana, tagged with the uidA gene, have allowed explaining how plants modify their hormonal pathways depending on the environmental conditions. Although the information extracted from microscopic images of these transgenic plants is often qualitative and in many publications is not subjected to quantification, in this paper we report the development of an informatics tool focused on computer vision for processing and analysis of digital images in order to analyze the expression of the GUS signal in A. thaliana roots, which is strongly correlated with the intensity of the grayscale images. This means that the presence of the GUS-induced color indicates where the gene has been actively expressed, such as our statistical analysis has demonstrated after treatment of A. thaliana DR5::GUS with naphtalen-acetic acid (0.0001 mM and 1 mM). GUSignal is a free informatics tool that aims to be fast and systematic during the image analysis since it executes specific and ordered instructions, to offer a segmented analysis by areas or regions of interest, providing quantitative results of the image intensity levels.
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16
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Automatic non-destructive multiple lettuce traits prediction based on DeepLabV3 +. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01660-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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17
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Banh ATM, Thiele B, Chlubek A, Hombach T, Kleist E, Matsubara S. Combination of long-term 13CO 2 labeling and isotopolog profiling allows turnover analysis of photosynthetic pigments in Arabidopsis leaves. PLANT METHODS 2022; 18:114. [PMID: 36183136 PMCID: PMC9526918 DOI: 10.1186/s13007-022-00946-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Living cells maintain and adjust structural and functional integrity by continual synthesis and degradation of metabolites and macromolecules. The maintenance and adjustment of thylakoid membrane involve turnover of photosynthetic pigments along with subunits of protein complexes. Quantifying their turnover is essential to understand the mechanisms of homeostasis and long-term acclimation of photosynthetic apparatus. Here we report methods combining whole-plant long-term 13CO2 labeling and liquid chromatography - mass spectrometry (LC-MS) analysis to determine the size of non-labeled population (NLP) of carotenoids and chlorophylls (Chl) in leaf pigment extracts of partially 13C-labeled plants. RESULTS The labeling chamber enabled parallel 13CO2 labeling of up to 15 plants of Arabidopsis thaliana with real-time environmental monitoring ([CO2], light intensity, temperature, relative air humidity and pressure) and recording. No significant difference in growth or photosynthetic pigment composition was found in leaves after 7-d exposure to normal CO2 (~ 400 ppm) or 13CO2 in the labeling chamber, or in ambient air outside the labeling chamber (control). Following chromatographic separation of the pigments and mass peak assignment by high-resolution Fourier-transform ion cyclotron resonance MS, mass spectra of photosynthetic pigments were analyzed by triple quadrupole MS to calculate NLP. The size of NLP remaining after the 7-d 13CO2 labeling was ~ 10.3% and ~ 11.5% for all-trans- and 9-cis-β-carotene, ~ 21.9% for lutein, ~ 18.8% for Chl a and 33.6% for Chl b, highlighting non-uniform turnover of these pigments in thylakoids. Comparable results were obtained in all replicate plants of the 13CO2 labeling experiment except for three that were showing anthocyanin accumulation and growth impairment due to insufficient water supply (leading to stomatal closure and less 13C incorporation). CONCLUSIONS Our methods allow 13CO2 labeling and estimation of NLP for photosynthetic pigments with high reproducibility despite potential variations in [13CO2] between the experiments. The results indicate distinct turnover rates of carotenoids and Chls in thylakoid membrane, which can be investigated in the future by time course experiments. Since 13C enrichment can be measured in a range of compounds, long-term 13CO2 labeling chamber, in combination with appropriate MS methods, facilitates turnover analysis of various metabolites and macromolecules in plants on a time scale of hours to days.
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Affiliation(s)
- Anh Thi-Mai Banh
- IBG-2: Plant Sciences, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Björn Thiele
- IBG-2: Plant Sciences, Forschungszentrum Jülich, 52425, Jülich, Germany
- IBG-3: Agrosphere, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Antonia Chlubek
- IBG-2: Plant Sciences, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Thomas Hombach
- IBG-2: Plant Sciences, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Einhard Kleist
- IBG-2: Plant Sciences, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Shizue Matsubara
- IBG-2: Plant Sciences, Forschungszentrum Jülich, 52425, Jülich, Germany.
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18
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Singh P, Pani A, Mujumdar AS, Shirkole SS. New strategies on the application of artificial intelligence in the field of phytoremediation. INTERNATIONAL JOURNAL OF PHYTOREMEDIATION 2022; 25:505-523. [PMID: 35802802 DOI: 10.1080/15226514.2022.2090500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Artificial Intelligence (AI) is expected to play a crucial role in the field of phytoremediation and its effective management in monitoring the growth of the plant in different contaminated soils and their phenotype characteristic such as the biomass of plants. This review focuses on recent applications of various AI techniques and remote sensing approaches in the field of phytoremediation to monitor plant growth with relevant morphological parameters using novel sensors, cameras, and associated modern technologies. Novel sensing and various measurement techniques are highlighted. Input parameters are used to develop futuristic models utilizing AI and statistical approaches. Additionally, a brief discussion has been presented on the use of AI techniques to detect metal hyperaccumulation in all parts of the plant, carbon capture, and sequestration along with its effect on food production to ensure food safety and security. This article highlights the application, limitation, and future perspectives of phytoremediation in monitoring the mobility, bioavailability, seasonal variation, effect of temperature on plant growth, and plant response to the heavy metals in soil by using the AI technique. Suggestions are made for future research in this area to analyze which would help to enhance plant growth and improve food security in long run.
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Affiliation(s)
- Pratyasha Singh
- Department of Civil Engineering, KIIT University, Bhubaneswar, Odisha, India
| | - Aparupa Pani
- Department of Civil Engineering, KIIT University, Bhubaneswar, Odisha, India
| | - Arun S Mujumdar
- Department of Bioresource Engineering, McGill University, Montreal, Canada
| | - Shivanand S Shirkole
- Department of Food Engineering and Technology, Institute of Chemical Technology Mumbai, Bhubaneshwar, Odisha, India
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19
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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20
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Sarić R, Nguyen VD, Burge T, Berkowitz O, Trtílek M, Whelan J, Lewsey MG, Čustović E. Applications of hyperspectral imaging in plant phenotyping. TRENDS IN PLANT SCIENCE 2022; 27:301-315. [PMID: 34998690 DOI: 10.1016/j.tplants.2021.12.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
Our ability to interrogate and manipulate the genome far exceeds our capacity to measure the effects of genetic changes on plant traits. Much effort has been made recently by the plant science research community to address this imbalance. The responses of plants to environmental conditions can now be defined using a variety of imaging approaches. Hyperspectral imaging (HSI) has emerged as a promising approach to measure traits using a wide range of wavebands simultaneously in 3D to capture information in lab, glasshouse, or field settings. HSI has been applied to define abiotic, biotic, and quality traits for optimisation of crop management.
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Affiliation(s)
- Rijad Sarić
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Viet D Nguyen
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Timothy Burge
- Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Oliver Berkowitz
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Martin Trtílek
- Photon Systems Instruments plant phenotyping research centre, Photon System Instruments, 664 24 Drasov, Brno, Czech Republic
| | - James Whelan
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia.
| | - Mathew G Lewsey
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Edhem Čustović
- Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
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21
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Padilla-Chacón D, Peña-Valdivia CB. High-Throughput Screening to Examine the Dynamic of Stay-Green by an Imaging System. Methods Mol Biol 2022; 2539:3-9. [PMID: 35895190 DOI: 10.1007/978-1-0716-2537-8_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The development of RGB (red, green, blue) sensors has opened the way for plant phenotyping. This is relevant because plant phenotyping allows us to visualize the product of the interaction between the plant ontogeny, anatomy, physiology, and biochemistry. Better yet, this can be achieved at any stage of plant development, i.e., from seedling to maturity. Here, we describe the use of phenotyping, based on the stay-green trait, of common bean (Phaseolus vulgaris L.) plant, as a model, stressed by water deficit, to elucidate the result of that interaction. Description is based on interpretation of RGB digital images acquired using a phenomic platform and a specific software. These images allow us to obtain a data group related to the color parameters that quantify the changes and alterations in each plant growth and development.
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Affiliation(s)
- Daniel Padilla-Chacón
- Programa de Posgrado en Botánica, CONACyT-Colegio de Postgraduados. Km 36.5 Carretera Mexico-Texcoco, Montecillo, MX, Mexico.
| | - Cecilia B Peña-Valdivia
- Programa de Posgrado en Botánica, Colegio de Postgraduados. Km 36.5 Carretera México-Texcoco, Montecillo, MX, Mexico
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22
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Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:1. [PMID: 37309486 PMCID: PMC10248672 DOI: 10.1007/s11032-021-01272-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.
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Affiliation(s)
- Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| | - Anand Kumar
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 202002 India
| | - Sumandeep Bazzer
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
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23
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Zhang H, Zhang Y, Xu N, Rui C, Fan Y, Wang J, Han M, Wang Q, Sun L, Chen X, Lu X, Wang D, Chen C, Ye W. Genome-wide expression analysis of phospholipase A1 (PLA1) gene family suggests phospholipase A1-32 gene responding to abiotic stresses in cotton. Int J Biol Macromol 2021; 192:1058-1074. [PMID: 34656543 DOI: 10.1016/j.ijbiomac.2021.10.038] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/05/2021] [Accepted: 10/06/2021] [Indexed: 01/01/2023]
Abstract
Cotton is the most important crop for the production of natural fibres used in the textile industry. High salinity, drought, cold and high temperature represent serious abiotic stresses, which seriously threaten cotton production. Phospholipase AS has an irreplaceable role in lipid signal transmission, growth and development and stress events. Phospholipase A can be divided into three families: PLA1, PLA2 and pPLA. Among them, the PLA1 family is rarely studied in plants. In order to study the potential functions of the PLA1 family in cotton, the bioinformatics analysis of the PLA1 family was correlated with cotton adversity, and tissue-specific analysis was performed. Explore the structure-function relationship of PLA1 members. It is found that the expression of GbPLA1-32 gene is affected by a variety of environmental stimuli, indicating that it plays a very important role in stress and hormone response, and closely associates the cotton adversity with this family. Through further functional verification, we found that virus-induced GbPLA1-32 gene silencing (VIGS) caused Gossypium barbadense to be sensitive to salt stress. This research provides an important basis for further research on the molecular mechanism of cotton resistance to abiotic stress.
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Affiliation(s)
- Hong Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China; Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, 830052 Urumqi, China
| | - Yuexin Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Nan Xu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Cun Rui
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Yapeng Fan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Jing Wang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Mingge Han
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, 830052 Urumqi, China
| | - Qinqin Wang
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, 830052 Urumqi, China
| | - Liangqing Sun
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Xiugui Chen
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Xuke Lu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Delong Wang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Chao Chen
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China
| | - Wuwei Ye
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Zhengzhou Research Base, School of Agricultural Sciences, Zhengzhou University, Key Laboratory for Cotton Genetic Improvement, MOA, Anyang, Henan 455000, China; Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, 830052 Urumqi, China.
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Liu L, Yu L, Wu D, Ye J, Feng H, Liu Q, Yang W. PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping. FRONTIERS IN PLANT SCIENCE 2021; 12:770217. [PMID: 34899792 PMCID: PMC8656718 DOI: 10.3389/fpls.2021.770217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/05/2021] [Indexed: 05/31/2023]
Abstract
A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phenotyping systems, which provide fast deployment and potential for wide usage in rural and wild areas, is quite limited. In this study, we developed a portable whole-plant on-device phenotyping smartphone application running on Android that can measure up to 45 traits, including 15 plant traits, 25 leaf traits and 5 stem traits, based on images. To avoid the influence of outdoor environments, we trained a DeepLabV3+ model for segmentation. In addition, an angle calibration algorithm was also designed to reduce the error introduced by the different imaging angles. The average execution time for the analysis of a 20-million-pixel image is within 2,500 ms. The application is a portable on-device fast phenotyping platform providing methods for real-time trait measurement, which will facilitate maize phenotyping in field and benefit crop breeding in future.
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Affiliation(s)
- Lingbo Liu
- Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Lejun Yu
- Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Dan Wu
- Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Junli Ye
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Qian Liu
- Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
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Kaur B, Sandhu KS, Kamal R, Kaur K, Singh J, Röder MS, Muqaddasi QH. Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects. PLANTS 2021; 10:plants10101989. [PMID: 34685799 PMCID: PMC8541486 DOI: 10.3390/plants10101989] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 12/22/2022]
Abstract
Omics technologies, namely genomics, transcriptomics, proteomics, metabolomics, and phenomics, are becoming an integral part of virtually every commercial cereal crop breeding program, as they provide substantial dividends per unit time in both pre-breeding and breeding phases. Continuous advances in omics assure time efficiency and cost benefits to improve cereal crops. This review provides a comprehensive overview of the established omics methods in five major cereals, namely rice, sorghum, maize, barley, and bread wheat. We cover the evolution of technologies in each omics section independently and concentrate on their use to improve economically important agronomic as well as biotic and abiotic stress-related traits. Advancements in the (1) identification, mapping, and sequencing of molecular/structural variants; (2) high-density transcriptomics data to study gene expression patterns; (3) global and targeted proteome profiling to study protein structure and interaction; (4) metabolomic profiling to quantify organ-level, small-density metabolites, and their composition; and (5) high-resolution, high-throughput, image-based phenomics approaches are surveyed in this review.
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Affiliation(s)
- Balwinder Kaur
- Everglades Research and Education Center, University of Florida, 3200 E. Palm Beach Rd., Belle Glade, FL 33430, USA;
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163, USA;
| | - Roop Kamal
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Stadt Seeland, Germany; (R.K.); or (M.S.R.)
| | - Kawalpreet Kaur
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada;
| | - Jagmohan Singh
- Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India;
| | - Marion S. Röder
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Stadt Seeland, Germany; (R.K.); or (M.S.R.)
| | - Quddoos H. Muqaddasi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Stadt Seeland, Germany; (R.K.); or (M.S.R.)
- Correspondence: or
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Zendonadi Dos Santos N, Piepho HP, Condorelli GE, Licieri Groli E, Newcomb M, Ward R, Tuberosa R, Maccaferri M, Fiorani F, Rascher U, Muller O. High-throughput field phenotyping reveals genetic variation in photosynthetic traits in durum wheat under drought. PLANT, CELL & ENVIRONMENT 2021; 44:2858-2878. [PMID: 34189744 DOI: 10.1111/pce.14136] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 04/14/2021] [Accepted: 06/13/2021] [Indexed: 06/13/2023]
Abstract
Chlorophyll fluorescence (ChlF) is a powerful non-invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high-throughput field phenotyping capabilities. The light-induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II ( Fq'/Fm' ) and the kinetics of electron transport measured by reoxidation rates ( Fr1' and Fr2' ). Short- and long-term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, Fq'/Fm' and Fr2' were little affected, while Fr1' was consistently accelerated in water-limited compared to well-watered plants, increasingly so with rising vapour pressure deficit. This high-throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis.
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Affiliation(s)
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany
| | | | - Eder Licieri Groli
- Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy
| | - Maria Newcomb
- Maricopa Agricultural Center, University of Arizona, Maricopa, Arizona, USA
| | - Richard Ward
- Maricopa Agricultural Center, University of Arizona, Maricopa, Arizona, USA
| | - Roberto Tuberosa
- Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy
| | - Marco Maccaferri
- Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy
| | - Fabio Fiorani
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Uwe Rascher
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Onno Muller
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
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27
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Genzel F, Dicke MD, Junker-Frohn LV, Neuwohner A, Thiele B, Putz A, Usadel B, Wormit A, Wiese-Klinkenberg A. Impact of Moderate Cold and Salt Stress on the Accumulation of Antioxidant Flavonoids in the Leaves of Two Capsicum Cultivars. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:6431-6443. [PMID: 34081868 DOI: 10.1021/acs.jafc.1c00908] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The horticultural production of bell peppers generates large quantities of residual biomass. Abiotic stress stimulates the production of protective flavonoids, so the deliberate application of stress to the plants after fruit harvest could provide a strategy to valorize horticultural residuals by increasing flavonoid concentrations, facilitating their industrial extraction. Here we exposed two Capsicum cultivars, a chilli and a bell pepper, to cold and salt stress and combinations thereof to determine their valorization potential. Noninvasive image-based phenotyping and multiparametric fluorescence measurements indicated that all stress treatments inhibited plant growth and reduced the leaf chlorophyll fluorescence index, with the chilli cultivar showing greater sensitivity. The fluorescence-based FLAV index allowed the noninvasive assessment of foliar luteolin glycosides. High-performance liquid chromatography-mass spectrometry (HPLC-MS) analysis showed that moderate cold increased the levels of two foliar antioxidant luteolin glycosides in both cultivars, with bell pepper containing the highest amounts (induced to maximum 5.5 mg g-1 DW cynaroside and 37.0 mg g-1 DW graveobioside A) after combined stress treatment. These data confirm the potential of abiotic stress for the valorization of residual leaf biomass to enhance the industrial extraction of antioxidant and bioactive flavonoids.
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Affiliation(s)
- Franziska Genzel
- Institute of Bio- and Geosciences-Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Bioeconomy Science Center, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Max Daniel Dicke
- Institute of Bio- and Geosciences-Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Bioeconomy Science Center, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Laura Verena Junker-Frohn
- Institute of Bio- and Geosciences-Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Bioeconomy Science Center, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Andrea Neuwohner
- Institute of Bio- and Geosciences-Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Bioeconomy Science Center, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Björn Thiele
- Institute of Bio- and Geosciences-Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Bioeconomy Science Center, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Alexander Putz
- Institute of Bio- and Geosciences-Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Bioeconomy Science Center, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Björn Usadel
- Bioeconomy Science Center, Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Bio- and Geosciences-Bioinformatics (IBG-4), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Alexandra Wormit
- Bioeconomy Science Center, Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute for Biology I-Botany, RWTH Aachen University, 52074 Aachen, Germany
| | - Anika Wiese-Klinkenberg
- Institute of Bio- and Geosciences-Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Bioeconomy Science Center, Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Bio- and Geosciences-Bioinformatics (IBG-4), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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28
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Shrestha A, Cudjoe DK, Kamruzzaman M, Siddique S, Fiorani F, Léon J, Naz AA. Abscisic acid-responsive element binding transcription factors contribute to proline synthesis and stress adaptation in Arabidopsis. JOURNAL OF PLANT PHYSIOLOGY 2021; 261:153414. [PMID: 33895677 DOI: 10.1016/j.jplph.2021.153414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/19/2021] [Accepted: 03/31/2021] [Indexed: 06/12/2023]
Abstract
Proline accumulation is one of the most common adaptive responses of higher plants against abiotic stresses like drought. It plays multiple roles in osmotic adjustment, cell homeostasis and stress recovery. Genetic regulation of proline accumulation under drought is complex, and transcriptional cascades modulating proline is poorly understood. Here, we employed quadruple mutant (abf1 abf2 abf3 abf4) to dissect the role of ABA-responsive elements (ABREs) binding transcription factors (ABFs) in modulating proline accumulation across varying stress scenarios. ABREs are present across the promoter of the P5CS1 gene, whose upregulation is considered a hallmark for drought inducible proline accumulation. Upon ABA treatment, P5CS1 mRNA expression and proline content in the shoot were significantly higher in Col-0 compared to the quadruple mutant. Similar results were found at 2 h and 3 h after acute dehydration. We quantified proline at different time points after drought stress treatment. The proline content was higher in wild type (Col-0) than the quadruple mutant at the early stage of drought. Notably, the proline accumulation in wild type increased at a slower rate than the quadruple mutant 7 d after drought stress. Besides, the quadruple mutant displayed significant oxidative damage, low tissue turgidity and higher membrane damage under terminal drought stress. Both terminal drought stress and long-term constant water stress revealed substantial differences in growth rate between wild type and quadruple mutant. The study provides evidence that ABFs are involved in drought stress response, such as proline biosynthesis in Arabidopsis.
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Affiliation(s)
- Asis Shrestha
- Department of Plant Breeding, Institute of Crop Science and Resource Conservation, University of Bonn, Germany.
| | - Daniel Kingsley Cudjoe
- Department of Plant Breeding, Institute of Crop Science and Resource Conservation, University of Bonn, Germany.
| | - Mohammad Kamruzzaman
- Department of Plant Breeding, Institute of Crop Science and Resource Conservation, University of Bonn, Germany.
| | | | - Fabio Fiorani
- IBG-2- Plant Sciences, Forschungszentrum Jülich, Jülich, Germany.
| | - Jens Léon
- Department of Plant Breeding, Institute of Crop Science and Resource Conservation, University of Bonn, Germany.
| | - Ali Ahmad Naz
- Department of Plant Breeding, Institute of Crop Science and Resource Conservation, University of Bonn, Germany.
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29
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Yao L, van de Zedde R, Kowalchuk G. Recent developments and potential of robotics in plant eco-phenotyping. Emerg Top Life Sci 2021; 5:289-300. [PMID: 34013965 PMCID: PMC8166337 DOI: 10.1042/etls20200275] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 02/04/2023]
Abstract
Automated acquisition of plant eco-phenotypic information can serve as a decision-making basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for plant breeders and plant physiologists. Because the microscopic components and macroscopic morphology of plants will be affected by the ecological environment, research on plant eco-phenotyping is more meaningful than the study of single-plant phenotyping. To achieve high-throughput acquisition of phenotyping information, the combination of high-precision sensors and intelligent robotic platforms have become an emerging research focus. Robotic platforms and automated systems are the important carriers of phenotyping monitoring sensors that enable large-scale screening. Through the diverse design and flexible systems, an efficient operation can be achieved across a range of experimental and field platforms. The combination of robot technology and plant phenotyping monitoring tools provides the data to inform novel artificial intelligence (AI) approaches that will provide steppingstones for new research breakthroughs. Therefore, this article introduces robotics and eco-phenotyping and examines research significant to this novel domain of plant eco-phenotyping. Given the monitoring scenarios of phenotyping information at different scales, the used intelligent robot technology, efficient automation platform, and advanced sensor equipment are summarized in detail. We further discuss the challenges posed to current research as well as the future developmental trends in the application of robot technology and plant eco-phenotyping. These include the use of collected data for AI applications and high-bandwidth data transfer, and large well-structured (meta) data storage approaches in plant sciences and agriculture.
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Affiliation(s)
- Lili Yao
- Wageningen University & Research, Wageningen, Netherlands
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30
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Jangra S, Chaudhary V, Yadav RC, Yadav NR. High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement. PHENOMICS (CHAM, SWITZERLAND) 2021; 1:31-53. [PMID: 36939738 PMCID: PMC9590473 DOI: 10.1007/s43657-020-00007-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.
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Affiliation(s)
- Sumit Jangra
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Vrantika Chaudhary
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Ram C. Yadav
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Neelam R. Yadav
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
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31
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Ruffing AM, Anthony SM, Strickland LM, Lubkin I, Dietz CR. Identification of Metal Stresses in Arabidopsis thaliana Using Hyperspectral Reflectance Imaging. FRONTIERS IN PLANT SCIENCE 2021; 12:624656. [PMID: 33664759 PMCID: PMC7921809 DOI: 10.3389/fpls.2021.624656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 01/27/2021] [Indexed: 05/14/2023]
Abstract
Industrial accidents, such as the Fukushima and Chernobyl disasters, release harmful chemicals into the environment, covering large geographical areas. Natural flora may serve as biological sensors for detecting metal contamination, such as cesium. Spectral detection of plant stresses typically employs a few select wavelengths and often cannot distinguish between different stress phenotypes. In this study, we apply hyperspectral reflectance imaging in the visible and near-infrared along with multivariate curve resolution (MCR) analysis to identify unique spectral signatures of three stresses in Arabidopsis thaliana: salt, copper, and cesium. While all stress conditions result in common stress physiology, hyperspectral reflectance imaging and MCR analysis produced unique spectral signatures that enabled classification of each stress. As the level of potassium was previously shown to affect cesium stress in plants, the response of A. thaliana to cesium stress under variable levels of potassium was also investigated. Increased levels of potassium reduced the spectral response of A. thaliana to cesium and prevented changes to chloroplast cellular organization. While metal stress mechanisms may vary under different environmental conditions, this study demonstrates that hyperspectral reflectance imaging with MCR analysis can distinguish metal stress phenotypes, providing the potential to detect metal contamination across large geographical areas.
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Affiliation(s)
- Anne M. Ruffing
- Department of Molecular and Microbiology, Sandia National Laboratories, Albuquerque, NM, United States
- *Correspondence: Anne M. Ruffing,
| | - Stephen M. Anthony
- Department of Computational Biology and Biophysics, Sandia National Laboratories, Albuquerque, NM, United States
| | - Lucas M. Strickland
- Department of Molecular and Microbiology, Sandia National Laboratories, Albuquerque, NM, United States
| | - Ian Lubkin
- Department of Molecular and Microbiology, Sandia National Laboratories, Albuquerque, NM, United States
| | - Carter R. Dietz
- Department of Electrical and Computer Engineering, Sandia National Laboratories, Albuquerque, NM, United States
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Beck MA, Liu CY, Bidinosti CP, Henry CJ, Godee CM, Ajmani M. An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture. PLoS One 2020; 15:e0243923. [PMID: 33332382 PMCID: PMC7745972 DOI: 10.1371/journal.pone.0243923] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 12/01/2020] [Indexed: 11/18/2022] Open
Abstract
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and image segementation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.
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Affiliation(s)
- Michael A. Beck
- Department of Physics, University of Winnipeg, Winnipeg, Manitoba, Canada
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, Canada
| | - Chen-Yi Liu
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Christopher P. Bidinosti
- Department of Physics, University of Winnipeg, Winnipeg, Manitoba, Canada
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, Canada
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Christopher J. Henry
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, Canada
| | - Cara M. Godee
- Department of Biology, University of Winnipeg, Winnipeg, Manitoba, Canada
| | - Manisha Ajmani
- Department of Physics, University of Winnipeg, Winnipeg, Manitoba, Canada
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, Canada
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Mikołajczak K, Ogrodowicz P, Ćwiek-Kupczyńska H, Weigelt-Fischer K, Mothukuri SR, Junker A, Altmann T, Krystkowiak K, Adamski T, Surma M, Kuczyńska A, Krajewski P. Image Phenotyping of Spring Barley ( Hordeum vulgare L.) RIL Population Under Drought: Selection of Traits and Biological Interpretation. FRONTIERS IN PLANT SCIENCE 2020; 11:743. [PMID: 32582262 PMCID: PMC7296146 DOI: 10.3389/fpls.2020.00743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 05/08/2020] [Indexed: 05/03/2023]
Abstract
Image-based phenotyping is a non-invasive method that permits the dynamic evaluation of plant features during growth, which is especially important for understanding plant adaptation and temporal dynamics of responses to environmental cues such as water deficit or drought. The aim of the present study was to use high-throughput imaging in order to assess the variation and dynamics of growth and development during drought in a spring barley population and to investigate associations between traits measured in time and yield-related traits measured after harvesting. Plant material covered recombinant inbred line population derived from a cross between European and Syrian cultivars. After placing the plants on the platform (28th day after sowing), drought stress was applied for 2 weeks. Top and side cameras were used to capture images daily that covered the visible range of the light spectrum, fluorescence signals, and the near infrared spectrum. The image processing provided 376 traits that were subjected to analysis. After 32 days of image phenotyping, the plants were cultivated in the greenhouse under optimal watering conditions until ripening, when several architecture and yield-related traits were measured. The applied data analysis approach, based on the clustering of image-derived traits into groups according to time profiles of statistical and genetic parameters, permitted to select traits representative for inference from the experiment. In particular, drought effects for 27 traits related to convex hull geometry, texture, proportion of brown pixels and chlorophyll intensity were found to be highly correlated with drought effects for spike traits and thousand grain weight.
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Affiliation(s)
| | - Piotr Ogrodowicz
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
| | | | | | | | - Astrid Junker
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | | | - Tadeusz Adamski
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
| | - Maria Surma
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
| | - Anetta Kuczyńska
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
| | - Paweł Krajewski
- Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland
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Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. MOLECULAR PLANT 2020; 13:187-214. [PMID: 31981735 DOI: 10.1016/j.molp.2020.01.008] [Citation(s) in RCA: 239] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/06/2020] [Accepted: 01/10/2020] [Indexed: 05/18/2023]
Abstract
Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
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Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crops Science/College of Agronomy, Henan Agricultural University, Zhengzhou 450002, P.R. China
| | - Jian Zhang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - John H Doonan
- The National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | | | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
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Martignago D, Rico-Medina A, Blasco-Escámez D, Fontanet-Manzaneque JB, Caño-Delgado AI. Drought Resistance by Engineering Plant Tissue-Specific Responses. FRONTIERS IN PLANT SCIENCE 2020; 10:1676. [PMID: 32038670 PMCID: PMC6987726 DOI: 10.3389/fpls.2019.01676] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 11/28/2019] [Indexed: 05/18/2023]
Abstract
Drought is the primary cause of agricultural loss globally, and represents a major threat to food security. Currently, plant biotechnology stands as one of the most promising fields when it comes to developing crops that are able to produce high yields in water-limited conditions. From studies of Arabidopsis thaliana whole plants, the main response mechanisms to drought stress have been uncovered, and multiple drought resistance genes have already been engineered into crops. So far, most plants with enhanced drought resistance have displayed reduced crop yield, meaning that there is still a need to search for novel approaches that can uncouple drought resistance from plant growth. Our laboratory has recently shown that the receptors of brassinosteroid (BR) hormones use tissue-specific pathways to mediate different developmental responses during root growth. In Arabidopsis, we found that increasing BR receptors in the vascular plant tissues confers resistance to drought without penalizing growth, opening up an exceptional opportunity to investigate the mechanisms that confer drought resistance with cellular specificity in plants. In this review, we provide an overview of the most promising phenotypical drought traits that could be improved biotechnologically to obtain drought-tolerant cereals. In addition, we discuss how current genome editing technologies could help to identify and manipulate novel genes that might grant resistance to drought stress. In the upcoming years, we expect that sustainable solutions for enhancing crop production in water-limited environments will be identified through joint efforts.
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Affiliation(s)
| | | | | | | | - Ana I. Caño-Delgado
- Department of Molecular Genetics, Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Barcelona, Spain
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36
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Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field. REMOTE SENSING 2020. [DOI: 10.3390/rs12020269] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.
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37
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Gao G, Tester MA, Julkowska MM. The Use of High-Throughput Phenotyping for Assessment of Heat Stress-Induced Changes in Arabidopsis. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:3723916. [PMID: 33313552 PMCID: PMC7706305 DOI: 10.34133/2020/3723916] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 05/30/2020] [Indexed: 05/20/2023]
Abstract
The worldwide rise in heatwave frequency poses a threat to plant survival and productivity. Determining the new marker phenotypes that show reproducible response to heat stress and contribute to heat stress tolerance is becoming a priority. In this study, we describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated noninvasive phenotyping system. Heat stress exposure resulted in an acute reduction of the quantum yield of photosystem II and increased leaf angle. In longer term, the exposure to heat also affected plant growth and morphology. By tracking the recovery period of the WT and mutants impaired in thermotolerance (hsp101), we observed that the difference in maximum quantum yield, quenching, rosette size, and morphology. By examining the correlation across the traits throughout time, we observed that early changes in photochemical quenching corresponded with the rosette size at later stages, which suggests the contribution of quenching to overall heat tolerance. We also determined that 6 h of heat stress provides the most informative insight in plant's responses to heat, as it shows a clear separation between treated and nontreated plants as well as the WT and hsp101. Our work streamlines future discoveries by providing an experimental protocol, data analysis pipeline, and new phenotypes that could be used as targets in thermotolerance screenings.
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Affiliation(s)
- Ge Gao
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Mark A. Tester
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Magdalena M. Julkowska
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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38
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Keller B, Matsubara S, Rascher U, Pieruschka R, Steier A, Kraska T, Muller O. Genotype Specific Photosynthesis x Environment Interactions Captured by Automated Fluorescence Canopy Scans Over Two Fluctuating Growing Seasons. FRONTIERS IN PLANT SCIENCE 2019; 10:1482. [PMID: 31998328 PMCID: PMC6962999 DOI: 10.3389/fpls.2019.01482] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 10/25/2019] [Indexed: 05/19/2023]
Abstract
Photosynthesis reacts dynamic and in different time scales to changing conditions. Light and temperature acclimation balance photosynthetic processes in a complex interplay with the fluctuating environment. However, due to limitations in the measurements techniques, these acclimations are often described under steady-state conditions leading to inaccurate photosynthesis estimates in the field. Here we analyze the photosynthetic interaction with the fluctuating environment and canopy architecture over two seasons using a fully automated phenotyping system. We acquired over 700,000 chlorophyll fluorescence transients and spectral measurements under semi-field conditions in four crop species including 28 genotypes. As expected, the quantum efficiency of the photosystem II (Fv/Fm in the dark and Fq'/Fm' in the light) was determined by light intensity. It was further significantly affected by spectral indices representing canopy structure effects. In contrast, a newly established parameter, monitoring the efficiency of electron transport (Fr2/Fv in the dark respective Fr2'/Fq' in the light), was highly responsive to temperature (R2 up to 0.75). This parameter decreased with temperature and enabled the detection of cold tolerant species and genotypes. We demonstrated the ability to capture and model the dynamic photosynthesis response to the environment over entire growth seasons. The improved linkage of photosynthetic performance to canopy structure, temperature and cold tolerance offers great potential for plant breeding and crop growth modeling.
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Affiliation(s)
- Beat Keller
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Shizue Matsubara
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Uwe Rascher
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Roland Pieruschka
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Angelina Steier
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Thorsten Kraska
- Field Lab Campus Klein-Altendorf, University of Bonn, Rheinbach, Germany
| | - Onno Muller
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
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Wang L, Poque S, Valkonen JPT. Phenotyping viral infection in sweetpotato using a high-throughput chlorophyll fluorescence and thermal imaging platform. PLANT METHODS 2019; 15:116. [PMID: 31649744 PMCID: PMC6805361 DOI: 10.1186/s13007-019-0501-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 10/10/2019] [Indexed: 05/12/2023]
Abstract
BACKGROUND Virus diseases caused by co-infection with Sweet potato feathery mottle virus (SPFMV) and Sweetpotato chlorotic stunt virus (SPCSV) are a severe problem in the production of sweetpotato (Ipomoea batatas L.). Traditional molecular virus detection methods include nucleic acid-based and serological tests. In this study, we aimed to validate the use of a non-destructive imaging-based plant phenotype platform to study plant-virus synergism in sweetpotato by comparing four virus treatments with two healthy controls. RESULTS By monitoring physiological and morphological effects of viral infection in sweetpotato over 29 days, we quantified photosynthetic performance from chlorophyll fluorescence (ChlF) imaging and leaf thermography from thermal infrared (TIR) imaging among sweetpotatoes. Moreover, the differences among different treatments observed from ChlF and TIR imaging were related to virus accumulation and distribution in sweetpotato. These findings were further validated at the molecular level by related gene expression in both photosynthesis and carbon fixation pathways. CONCLUSION Our study validated for the first time the use of ChlF- and TIR-based imaging systems to distinguish the severity of virus diseases related to SPFMV and SPCSV in sweetpotato. In addition, we demonstrated that the operating efficiency of PSII and photochemical quenching were the most sensitive parameters for the quantification of virus effects compared with maximum quantum efficiency, non-photochemical quenching, and leaf temperature.
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Affiliation(s)
- Linping Wang
- Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland
| | - Sylvain Poque
- Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland
| | - Jari P. T. Valkonen
- Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland
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40
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Bangash SAK, Müller-Schüssele SJ, Solbach D, Jansen M, Fiorani F, Schwarzländer M, Kopriva S, Meyer AJ. Low-glutathione mutants are impaired in growth but do not show an increased sensitivity to moderate water deficit. PLoS One 2019; 14:e0220589. [PMID: 31626663 PMCID: PMC6799929 DOI: 10.1371/journal.pone.0220589] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 10/04/2019] [Indexed: 11/19/2022] Open
Abstract
Glutathione is considered a key metabolite for stress defense and elevated levels have frequently been proposed to positively influence stress tolerance. To investigate whether glutathione affects plant performance and the drought tolerance of plants, wild-type Arabidopsis plants and an allelic series of five mutants (rax1, pad2, cad2, nrc1, and zir1) with reduced glutathione contents between 21 and 63% compared to wild-type glutathione content were phenotypically characterized for their shoot growth under control and water-limiting conditions using a shoot phenotyping platform. Under non-stress conditions the zir1 mutant with only 21% glutathione showed a pronounced dwarf phenotype. All other mutants with intermediate glutathione contents up to 62% in contrast showed consistently slightly smaller shoots than the wild-type. Moderate drought stress imposed through water withdrawal until shoot growth ceased showed that wild-type plants and all mutants responded similarly in terms of chlorophyll fluorescence and growth retardation. These results lead to the conclusion that glutathione is important for general plant performance but that the glutathione content does not affect tolerance to moderate drought conditions typically experienced by crops in the field.
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Affiliation(s)
| | | | - David Solbach
- INRES–Chemical Signalling, University of Bonn, Bonn, Germany
| | - Marcus Jansen
- IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Fabio Fiorani
- IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Markus Schwarzländer
- INRES–Chemical Signalling, University of Bonn, Bonn, Germany
- Bioeconomy Science Center, c/o Forschungszentrum Jülich, Jülich, Germany
| | - Stanislav Kopriva
- Botanical Institute, Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Germany
| | - Andreas J. Meyer
- INRES–Chemical Signalling, University of Bonn, Bonn, Germany
- Bioeconomy Science Center, c/o Forschungszentrum Jülich, Jülich, Germany
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Marín‐de la Rosa N, Lin C, Kang YJ, Dhondt S, Gonzalez N, Inzé D, Falter‐Braun P. Drought resistance is mediated by divergent strategies in closely related Brassicaceae. THE NEW PHYTOLOGIST 2019; 223:783-797. [PMID: 30955214 PMCID: PMC6771540 DOI: 10.1111/nph.15841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 03/29/2019] [Indexed: 05/08/2023]
Abstract
Droughts cause severe crop losses worldwide and climate change is projected to increase their prevalence in the future. Similar to the situation for many crops, the reference plant Arabidopsis thaliana (Ath) is considered drought-sensitive, whereas, as we demonstrate, its close relatives Arabidopsis lyrata (Aly) and Eutrema salsugineum (Esa) are drought-resistant. To understand the molecular basis for this plasticity we conducted a deep phenotypic, biochemical and transcriptomic comparison using developmentally matched plants. We demonstrate that Aly responds most sensitively to decreasing water availability with early growth reduction, metabolic adaptations and signaling network rewiring. By contrast, Esa is in a constantly prepared mode as evidenced by high basal proline levels, ABA signaling transcripts and late growth responses. The stress-sensitive Ath responds later than Aly and earlier than Esa, although its responses tend to be more extreme. All species detect water scarcity with similar sensitivity; response differences are encoded in downstream signaling and response networks. Moreover, several signaling genes expressed at higher basal levels in both Aly and Esa have been shown to increase water-use efficiency and drought resistance when overexpressed in Ath. Our data demonstrate contrasting strategies of closely related Brassicaceae to achieve drought resistance.
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Affiliation(s)
- Nora Marín‐de la Rosa
- Institute of Network Biology (INET)Helmholtz Zentrum München (HMGU)München‐Neuherberg85764Germany
| | - Chung‐Wen Lin
- Institute of Network Biology (INET)Helmholtz Zentrum München (HMGU)München‐Neuherberg85764Germany
| | - Yang Jae Kang
- Institute of Network Biology (INET)Helmholtz Zentrum München (HMGU)München‐Neuherberg85764Germany
- Division of Life ScienceGyeongsang National UniversityJinju52828Korea
| | - Stijn Dhondt
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- VIB‐UGent Center for Plant Systems BiologyVIBGhent9052Belgium
| | - Nathalie Gonzalez
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- VIB‐UGent Center for Plant Systems BiologyVIBGhent9052Belgium
- UMR 1332Biologie du Fruit et PathologieINRAUniv. BordeauxVillenave d'Ornon Cedex33882France
| | - Dirk Inzé
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- VIB‐UGent Center for Plant Systems BiologyVIBGhent9052Belgium
| | - Pascal Falter‐Braun
- Institute of Network Biology (INET)Helmholtz Zentrum München (HMGU)München‐Neuherberg85764Germany
- Microbe–Host InteractionsLudwig‐Maximilians‐Universität (LMU) MünchenMunich80539Germany
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Dodig D, Božinović S, Nikolić A, Zorić M, Vančetović J, Ignjatović-Micić D, Delić N, Weigelt-Fischer K, Junker A, Altmann T. Image-Derived Traits Related to Mid-Season Growth Performance of Maize Under Nitrogen and Water Stress. FRONTIERS IN PLANT SCIENCE 2019; 10:814. [PMID: 31297124 PMCID: PMC6607059 DOI: 10.3389/fpls.2019.00814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 06/06/2019] [Indexed: 06/09/2023]
Abstract
Phenotypic measurements under controlled cultivation conditions are essential to gain a mechanistic understanding of plant responses to environmental impacts and thus for knowledge-based improvement of their performance under natural field conditions. Twenty maize inbred lines (ILs) were phenotyped in response to two levels of water and nitrogen supply (control and stress) and combined nitrogen and water deficit. Over a course of 5 weeks (from about 4-leaf stage to the beginning of the reproductive stage), maize phenology and growth were monitored by using a high-throughput phenotyping platform for daily acquisition of images in different spectral ranges. The focus of the present study is on the measurements taken at the time of maximum water stress (for traits that reflect plant physiological properties) and at the end of the experiment (for traits that reflect plant architectural and biomass-related traits). Twenty-five phenotypic traits extracted from the digital image data that support biological interpretation of plant growth were selected for their predictive value for mid-season shoot biomass accumulation. Measured fresh and dry weights after harvest were used to calculate various indices (water-use efficiency, physiological nitrogen-use efficiency, specific plant weight) and to establish correlations with image-derived phenotypic features. Also, score indices based on dry weight were used to identify contrasting ILs in terms of productivity and tolerance to stress, and their means for image-derived and manually measured traits were compared. Color-related traits appear to be indicative of plant performance and photosystem II operating efficiency might be an importance physiological parameter of biomass accumulation, particularly under severe stress conditions. Also, genotypes showing greater leaf area may be better adapted to abiotic stress conditions.
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Affiliation(s)
- Dejan Dodig
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | - Sofija Božinović
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | - Ana Nikolić
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | - Miroslav Zorić
- Department for Maize, Institute of Field and Vegetable Crops, Novi Sad, Serbia
| | - Jelena Vančetović
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | | | - Nenad Delić
- Department for Research and Development, Maize Research Institute Zemun Polje, Belgrade, Serbia
| | - Kathleen Weigelt-Fischer
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Astrid Junker
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
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Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA. High-throughput phenotyping for crop improvement in the genomics era. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:60-72. [PMID: 31003612 DOI: 10.1016/j.plantsci.2019.01.007] [Citation(s) in RCA: 92] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 05/24/2023]
Abstract
Tremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this 'phenomics bottlenecks' by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement.
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Affiliation(s)
- Reyazul Rouf Mir
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India.
| | - Mathew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Mohd Anwar Khan
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
| | - Mohd Ashraf Bhat
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
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Bernotas G, Scorza LCT, Hansen MF, Hales IJ, Halliday KJ, Smith LN, Smith ML, McCormick AJ. A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth. Gigascience 2019; 8:giz056. [PMID: 31127811 PMCID: PMC6534809 DOI: 10.1093/gigascience/giz056] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 03/25/2019] [Accepted: 04/21/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping. Here we present PS-Plant, a low-cost and portable 3D plant phenotyping platform based on an imaging technique novel to plant phenotyping called photometric stereo (PS). RESULTS We calibrated PS-Plant to track the model plant Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement. Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis (1,768 images in total). A full protocol is provided, including all software components and an additional test data set. CONCLUSIONS PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small- and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap.
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Affiliation(s)
- Gytis Bernotas
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Livia C T Scorza
- SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, The King's Buildings, Edinburgh EH9 3BF, UK
| | - Mark F Hansen
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Ian J Hales
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Karen J Halliday
- SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, The King's Buildings, Edinburgh EH9 3BF, UK
| | - Lyndon N Smith
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Melvyn L Smith
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Alistair J McCormick
- SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, The King's Buildings, Edinburgh EH9 3BF, UK
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Abstract
Agricultural scientists face the dual challenge of breeding input-responsive, widely adoptable and climate-resilient varieties of crop plants and developing such varieties at a faster pace. Integrating the gains of genomics with modern-day phenomics will lead to increased breeding efficiency which in turn offers great promise to develop such varieties rapidly. Plant phenotyping techniques have impressively evolved during the last two decades. The low-cost, automated and semi-automated methods for data acquisition, storage and analysis are now available which allow precise quantitative analysis of plant structure and function; and genetic dissection of complex traits. Appropriate plant types can now be quickly developed that respond favorably to low input and resource-limited environments and address the challenges of subsistence agriculture. The present review focuses on the need of systematic, rapid, minimal invasive and low-cost plant phenotyping. It also discusses its evolution to modern day high throughput phenotyping (HTP), traits amenable to HTP, integration of HTP with genomics and the scope of utilizing these tools for crop improvement.
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Lien MR, Barker RJ, Ye Z, Westphall MH, Gao R, Singh A, Gilroy S, Townsend PA. A low-cost and open-source platform for automated imaging. PLANT METHODS 2019; 15:6. [PMID: 30705688 PMCID: PMC6348682 DOI: 10.1186/s13007-019-0392-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 01/21/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana. RESULTS A robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers. CONCLUSIONS HyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting.
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Affiliation(s)
- Max R. Lien
- Russell Labs, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706 USA
| | - Richard J. Barker
- Birge Hall, University of Wisconsin-Madison, 430 Lincoln Drive, Madison, WI 53706 USA
| | - Zhiwei Ye
- Russell Labs, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706 USA
| | - Matthew H. Westphall
- Russell Labs, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706 USA
| | - Ruohan Gao
- Russell Labs, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706 USA
| | - Aditya Singh
- Frazier Rogers Hall, 1741 Museum Road, Gainesville, FL 32611 USA
| | - Simon Gilroy
- Birge Hall, University of Wisconsin-Madison, 430 Lincoln Drive, Madison, WI 53706 USA
| | - Philip A. Townsend
- Russell Labs, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706 USA
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Costa C, Schurr U, Loreto F, Menesatti P, Carpentier S. Plant Phenotyping Research Trends, a Science Mapping Approach. FRONTIERS IN PLANT SCIENCE 2019; 9:1933. [PMID: 30666264 PMCID: PMC6330294 DOI: 10.3389/fpls.2018.01933] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 12/12/2018] [Indexed: 05/19/2023]
Abstract
Modern plant phenotyping, often using non-invasive technologies and digital technologies, is an emerging science and provides essential information on how genetics, epigenetics, environmental pressures, and crop management (farming) can guide selection toward productive plants suitable for their environment. Thus, phenotyping is at the forefront of future plant breeding. Bibliometric science mapping is a quantitative method that analyzes scientific publications throughout the terms present in their title, abstract, and keywords. The aim of this mapping exercise is to observe trends and identify research opportunities. This allows us to analyze the evolution of phenotyping research and to predict emerging topics of this discipline. A total of 1,827 scientific publications fitted our search method over the last 20 years. During the period 1997-2006, the total number of publications was only around 6.1%. The number of publications increased more steeply after 2010, boosted by the overcoming of technological bias and by a set of key developments at hard and software level (image analysis and data storage management, automation and robotics). Cluster analysis evidenced three main groups linked to genetics, physiology, and imaging. Mainly the model plant "Arabidopsis thaliana" and the crops "rice" and "triticum" species were investigated in the literature. The last two species were studied when addressing "plant breeding," and "genomic selection." However, currently the trend goes toward a higher diversity of phenotyped crops and research in the field. The application of plant phenotyping in the field is still under rapid development and this application has strong linkages with precision agriculture. EU co-authors were involved in 41.8% of the analyzed papers, followed by USA (15.4%), Australia (6.0%), and India (5.6%). Within the EU, coauthors were mainly affiliated in Germany (35.8%), France (23.7%), and United Kingdom (18.4%). Time seems right for new opportunities to incentivize research on more crops, in real field conditions, and to spread knowledge toward more countries, including emerging economies. Science mapping offers the possibility to get insights into a wide amount of bibliographic information, making them more manageable, attractive, and easy to serve science policy makers, stakeholders, and research managers.
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Affiliation(s)
- Corrado Costa
- Consiglio per la Ricerca in Agricoltura e l'analisi dell'economia Agraria–Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Rome, Italy
| | - Ulrich Schurr
- Forschungszentrum Jülich, IBG-2: Plant Sciences, Jülich, Germany
| | - Francesco Loreto
- Dipartimento di Scienze Bio-Agroalimentari, Consiglio Nazionale Delle Ricerche, Rome, Italy
| | - Paolo Menesatti
- Consiglio per la Ricerca in Agricoltura e l'analisi dell'economia Agraria–Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Rome, Italy
| | - Sebastien Carpentier
- Bioversity International, Genetic Resources, Leuven, Belgium
- KU Leuven, Leuven, Belgium
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van Bezouw RFHM, Keurentjes JJB, Harbinson J, Aarts MGM. Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:112-133. [PMID: 30548574 PMCID: PMC6850172 DOI: 10.1111/tpj.14190] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 11/27/2018] [Accepted: 11/28/2018] [Indexed: 05/18/2023]
Abstract
In recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High-throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis-related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio-engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype-to-gene identification pipeline.
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Affiliation(s)
- Roel F. H. M. van Bezouw
- Laboratory of GeneticsWageningen University and ResearchDroevendaalsesteeg 16708PBWageningenThe Netherlands
| | - Joost J. B. Keurentjes
- Laboratory of GeneticsWageningen University and ResearchDroevendaalsesteeg 16708PBWageningenThe Netherlands
| | - Jeremy Harbinson
- Horticulture and Product PhysiologyWageningen University and ResearchDroevendaalsesteeg 16708PBWageningenThe Netherlands
| | - Mark G. M. Aarts
- Laboratory of GeneticsWageningen University and ResearchDroevendaalsesteeg 16708PBWageningenThe Netherlands
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Morton MJL, Awlia M, Al‐Tamimi N, Saade S, Pailles Y, Negrão S, Tester M. Salt stress under the scalpel - dissecting the genetics of salt tolerance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:148-163. [PMID: 30548719 PMCID: PMC6850516 DOI: 10.1111/tpj.14189] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 11/28/2018] [Accepted: 11/30/2018] [Indexed: 05/08/2023]
Abstract
Salt stress limits the productivity of crops grown under saline conditions, leading to substantial losses of yield in saline soils and under brackish and saline irrigation. Salt tolerant crops could alleviate these losses while both increasing irrigation opportunities and reducing agricultural demands on dwindling freshwater resources. However, despite significant efforts, progress towards this goal has been limited, largely because of the genetic complexity of salt tolerance for agronomically important yield-related traits. Consequently, the focus is shifting to the study of traits that contribute to overall tolerance, thus breaking down salt tolerance into components that are more genetically tractable. Greater consideration of the plasticity of salt tolerance mechanisms throughout development and across environmental conditions furthers this dissection. The demand for more sophisticated and comprehensive methodologies is being met by parallel advances in high-throughput phenotyping and sequencing technologies that are enabling the multivariate characterisation of vast germplasm resources. Alongside steady improvements in statistical genetics models, forward genetics approaches for elucidating salt tolerance mechanisms are gaining momentum. Subsequent quantitative trait locus and gene validation has also become more accessible, most recently through advanced techniques in molecular biology and genomic analysis, facilitating the translation of findings to the field. Besides fuelling the improvement of established crop species, this progress also facilitates the domestication of naturally salt tolerant orphan crops. Taken together, these advances herald a promising era of discovery for research into the genetics of salt tolerance in plants.
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Affiliation(s)
- Mitchell J. L. Morton
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Mariam Awlia
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Nadia Al‐Tamimi
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Stephanie Saade
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Yveline Pailles
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Sónia Negrão
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Mark Tester
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
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50
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Vasseur F, Bresson J, Wang G, Schwab R, Weigel D. Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana. PLANT METHODS 2018; 14:63. [PMID: 30065776 PMCID: PMC6060534 DOI: 10.1186/s13007-018-0331-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 07/23/2018] [Indexed: 05/24/2023]
Abstract
BACKGROUND The model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera. RESULTS We developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H2 < 0.93), as well as estimated fruit number (H2 = 0.68). In addition, we validated the method for estimating fruit number with rev5, a mutant with increased flower abortion. CONCLUSIONS The method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large populations of a model species.
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Affiliation(s)
- François Vasseur
- Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
| | - Justine Bresson
- Center for Plant Molecular Biology (ZMBP), General Genetics, University of Tübingen, 72076 Tübingen, Germany
| | - George Wang
- Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
| | - Rebecca Schwab
- Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
| | - Detlef Weigel
- Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
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