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Oskam L, Snoek BL, Pantazopoulou CK, van Veen H, Matton SEA, Dijkhuizen R, Pierik R. A low-cost open-source imaging platform reveals spatiotemporal insight into leaf elongation and movement. PLANT PHYSIOLOGY 2024; 195:1866-1879. [PMID: 38401532 PMCID: PMC11213255 DOI: 10.1093/plphys/kiae097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/08/2024] [Accepted: 01/25/2024] [Indexed: 02/26/2024]
Abstract
Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging setup, based on small Raspberry Pi computers that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semiautomated image analysis pipeline. Using this setup, we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.
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Affiliation(s)
- Lisa Oskam
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Basten L Snoek
- Theoretical Biology and Bioinformatics, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Chrysoula K Pantazopoulou
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Hans van Veen
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Sanne E A Matton
- Laboratory of Molecular Biology, Wageningen University and Research, Wageningen 6700 AA, The Netherlands
| | - Rens Dijkhuizen
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Ronald Pierik
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
- Laboratory of Molecular Biology, Wageningen University and Research, Wageningen 6700 AA, The Netherlands
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2
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Sénéchal F, Robinson S, Van Schaik E, Trévisan M, Saxena P, Reinhardt D, Fankhauser C. Pectin methylesterification state and cell wall mechanical properties contribute to neighbor proximity-induced hypocotyl growth in Arabidopsis. PLANT DIRECT 2024; 8:e584. [PMID: 38646567 PMCID: PMC11033045 DOI: 10.1002/pld3.584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/25/2024] [Accepted: 03/24/2024] [Indexed: 04/23/2024]
Abstract
Plants growing with neighbors compete for light and consequently increase the growth of their vegetative organs to enhance access to sunlight. This response, called shade avoidance syndrome (SAS), involves photoreceptors such as phytochromes as well as phytochrome interacting factors (PIFs), which regulate the expression of growth-mediating genes. Numerous cell wall-related genes belong to the putative targets of PIFs, and the importance of cell wall modifications for enabling growth was extensively shown in developmental models such as dark-grown hypocotyl. However, the contribution of the cell wall in the growth of de-etiolated seedlings regulated by shade cues remains poorly established. Through analyses of mechanical and biochemical properties of the cell wall coupled with transcriptomic analysis of cell wall-related genes from previously published data, we provide evidence suggesting that cell wall modifications are important for neighbor proximity-induced elongation. Further analysis using loss-of-function mutants impaired in the synthesis and remodeling of the main cell wall polymers corroborated this. We focused on the cgr2cgr3 double mutant that is defective in methylesterification of homogalacturonan (HG)-type pectins. By following hypocotyl growth kinetically and spatially and analyzing the mechanical and biochemical properties of cell walls, we found that methylesterification of HG-type pectins was required to enable global cell wall modifications underlying neighbor proximity-induced hypocotyl growth. Collectively, our work suggests that plant competition for light induces changes in the expression of numerous cell wall genes to enable modifications in biochemical and mechanical properties of cell walls that contribute to neighbor proximity-induced growth.
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Affiliation(s)
- Fabien Sénéchal
- Centre for Integrative Genomics, Faculty of Biology and Medicine, Génopode BuildingUniversity of LausanneLausanneSwitzerland
- Present address:
UMR INRAE 1158 BioEcoAgro, Plant Biology and InnovationUniversity of Picardie Jules VerneAmiensFrance
| | - Sarah Robinson
- Institute of Plant SciencesUniversity of BernBernSwitzerland
- Present address:
The Sainsbury LaboratoryUniversity of CambridgeCambridgeUK
| | - Evert Van Schaik
- Department of BiologyUniversity of FribourgFribourgSwitzerland
- Present address:
University of Applied Sciences LeidenLeidenNetherlands
| | - Martine Trévisan
- Centre for Integrative Genomics, Faculty of Biology and Medicine, Génopode BuildingUniversity of LausanneLausanneSwitzerland
| | - Prashant Saxena
- Centre for Integrative Genomics, Faculty of Biology and Medicine, Génopode BuildingUniversity of LausanneLausanneSwitzerland
- Present address:
James Watt School of EngineeringUniversity of GlasgowGlasgowUK
| | | | - Christian Fankhauser
- Centre for Integrative Genomics, Faculty of Biology and Medicine, Génopode BuildingUniversity of LausanneLausanneSwitzerland
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3
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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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4
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Harandi N, Vandenberghe B, Vankerschaver J, Depuydt S, Van Messem A. How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques. PLANT METHODS 2023; 19:60. [PMID: 37353846 DOI: 10.1186/s13007-023-01031-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/19/2023] [Indexed: 06/25/2023]
Abstract
Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.
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Affiliation(s)
- Negin Harandi
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | | | - Joris Vankerschaver
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | - Stephen Depuydt
- Erasmus Applied University of Sciences and Arts, Campus Kaai, Nijverheidskaai 170, Anderlecht, Belgium
| | - Arnout Van Messem
- Department of Mathematics, Université de Liège, Allée de la Découverte 12, Liège, Belgium.
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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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6
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Michaud O, Krahmer J, Galbier F, Lagier M, Galvão VC, Ince YÇ, Trevisan M, Knerova J, Dickinson P, Hibberd JM, Zeeman SC, Fankhauser C. Abscisic acid modulates neighbor proximity-induced leaf hyponasty in Arabidopsis. PLANT PHYSIOLOGY 2023; 191:542-557. [PMID: 36135791 PMCID: PMC9806605 DOI: 10.1093/plphys/kiac447] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 09/08/2022] [Indexed: 05/27/2023]
Abstract
Leaves of shade-avoiding plants such as Arabidopsis (Arabidopsis thaliana) change their growth pattern and position in response to low red to far-red ratios (LRFRs) encountered in dense plant communities. Under LRFR, transcription factors of the phytochrome-interacting factor (PIF) family are derepressed. PIFs induce auxin production, which is required for promoting leaf hyponasty, thereby favoring access to unfiltered sunlight. Abscisic acid (ABA) has also been implicated in the control of leaf hyponasty, with gene expression patterns suggesting that LRFR regulates the ABA response. Here, we show that LRFR leads to a rapid increase in ABA levels in leaves. Changes in ABA levels depend on PIFs, which regulate the expression of genes encoding isoforms of the enzyme catalyzing a rate-limiting step in ABA biosynthesis. Interestingly, ABA biosynthesis and signaling mutants have more erect leaves than wild-type Arabidopsis under white light but respond less to LRFR. Consistent with this, ABA application decreases leaf angle under white light; however, this response is inhibited under LRFR. Tissue-specific interference with ABA signaling indicates that an ABA response is required in different cell types for LRFR-induced hyponasty. Collectively, our data indicate that LRFR triggers rapid PIF-mediated ABA production. ABA plays a different role in controlling hyponasty under white light than under LRFR. Moreover, ABA exerts its activity in multiple cell types to control leaf position.
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Affiliation(s)
| | - Johanna Krahmer
- Faculty of Biology and Medicine, Centre for Integrative Genomics, University of Lausanne, Génopode Building, Lausanne CH-1015, Switzerland
| | - Florian Galbier
- Plant Biochemistry, Department of Biology, ETH Zürich, Universität-Str. 2, CH-8092 Zürich, Switzerland
| | | | | | | | - Martine Trevisan
- Faculty of Biology and Medicine, Centre for Integrative Genomics, University of Lausanne, Génopode Building, Lausanne CH-1015, Switzerland
| | - Jana Knerova
- Department of Plant Sciences, Downing Street, Cambridge, University of Cambridge, CB2 3EA, UK
| | - Patrick Dickinson
- Department of Plant Sciences, Downing Street, Cambridge, University of Cambridge, CB2 3EA, UK
| | - Julian M Hibberd
- Department of Plant Sciences, Downing Street, Cambridge, University of Cambridge, CB2 3EA, UK
| | - Samuel C Zeeman
- Plant Biochemistry, Department of Biology, ETH Zürich, Universität-Str. 2, CH-8092 Zürich, Switzerland
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7
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Morón-García O, Garzón-Martínez GA, Martínez-Martín MJP, Brook J, Corke FMK, Doonan JH, Camargo Rodríguez AV. Genetic architecture of variation in Arabidopsis thaliana rosettes. PLoS One 2022; 17:e0263985. [PMID: 35171969 PMCID: PMC8849614 DOI: 10.1371/journal.pone.0263985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 02/01/2022] [Indexed: 12/04/2022] Open
Abstract
Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.
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Affiliation(s)
- Odín Morón-García
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - Gina A. Garzón-Martínez
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - M. J. Pilar Martínez-Martín
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - Jason Brook
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - Fiona M. K. Corke
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - John H. Doonan
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
- * E-mail: (AVCR); (JHD)
| | - Anyela V. Camargo Rodríguez
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
- * E-mail: (AVCR); (JHD)
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8
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Hilty J, Muller B, Pantin F, Leuzinger S. Plant growth: the What, the How, and the Why. THE NEW PHYTOLOGIST 2021; 232:25-41. [PMID: 34245021 DOI: 10.1111/nph.17610] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 06/19/2021] [Indexed: 05/28/2023]
Abstract
Growth is a widely used term in plant science and ecology, but it can have different meanings depending on the context and the spatiotemporal scale of analysis. At the meristem level, growth is associated with the production of cells and initiation of new organs. At the organ or plant scale and over short time periods, growth is often used synonymously with tissue expansion, while over longer time periods the increase in biomass is a common metric. At even larger temporal and spatial scales, growth is mostly described as net primary production. Here, we first address the question 'what is growth?'. We propose a general framework to distinguish between the different facets of growth, and the corresponding physiological processes, environmental drivers and mathematical formalisms. Based on these different definitions, we then review how plant growth can be measured and analysed at different organisational, spatial and temporal scales. We conclude by discussing why gaining a better understanding of the different facets of plant growth is essential to disentangle genetic and environmental effects on the phenotype, and to uncover the causalities around source or sink limitations of plant growth.
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Affiliation(s)
- Jonas Hilty
- School of Science, Auckland University of Technology, 46 Wakefield Street, Auckland, 1142, New Zealand
| | - Bertrand Muller
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, 34000, France
| | - Florent Pantin
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, 34000, France
| | - Sebastian Leuzinger
- School of Science, Auckland University of Technology, 46 Wakefield Street, Auckland, 1142, New Zealand
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9
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Jin S, Su Y, Zhang Y, Song S, Li Q, Liu Z, Ma Q, Ge Y, Liu L, Ding Y, Baret F, Guo Q. Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9895241. [PMID: 34557676 PMCID: PMC8441379 DOI: 10.34133/2021/9895241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 07/27/2021] [Indexed: 06/02/2023]
Abstract
Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment. Terrestrial laser scanning (TLS) is a well-suited tool to study structural rhythm under field conditions. Recent studies have used TLS to describe the structural rhythm of trees, but no consistent patterns have been drawn. Meanwhile, whether TLS can capture structural rhythm in crops is unclear. Here, we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS. The seasonal rhythm was studied using TLS data collected at four key growth periods, including jointing, bell-mouthed, heading, and maturity periods. Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions. Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels. (1) Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage. Leaf azimuth was stable after the jointing stage. (2) Some individual-level structural rhythms (e.g., azimuth and projected leaf area/PLA) were consistent with leaf-level structural rhythms. (3) The circadian rhythms of some traits (e.g., PLA) were not consistent under standard and cold stress conditions. (4) Environmental factors showed better correlations with leaf traits under cold stress than standard conditions. Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth. This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.
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Affiliation(s)
- Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanjun Su
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongguang Zhang
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Shilin Song
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qing Li
- National Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, 210095 Jiangsu, China
| | - Zhonghua Liu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qin Ma
- Department of Forestry, Mississippi State University, Mississippi State 39759, USA
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - LingLi Liu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Frédéric Baret
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
- Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Institut National de la Recherche Agronomique, Unité Mixte de Recherche 1114 Domaine Saint-Paul, Avignon Cedex 84914, France
| | - Qinghua Guo
- Department of Ecology, College of Environmental Sciences, and Key Laboratory of Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
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10
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Abstract
Use of 3D sensors in plant phenotyping has increased in the last few years. Various image acquisition, 3D representations, 3D model processing and analysis techniques exist to help the researchers. However, a review of approaches, algorithms, and techniques used for 3D plant physiognomic analysis is lacking. In this paper, we investigate the techniques and algorithms used at various stages of processing and analysing 3D models of plants, and identify their current limiting factors. This review will serve potential users as well as new researchers in this field. The focus is on exploring studies monitoring the plant growth of single plants or small scale canopies as opposed to large scale monitoring in the field.
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11
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Meyer RC, Weigelt-Fischer K, Knoch D, Heuermann M, Zhao Y, Altmann T. Temporal dynamics of QTL effects on vegetative growth in Arabidopsis thaliana. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:476-490. [PMID: 33080013 DOI: 10.1093/jxb/eraa490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
We assessed early vegetative growth in a population of 382 accessions of Arabidopsis thaliana using automated non-invasive high-throughput phenotyping. All accessions were imaged daily from 7 d to 18 d after sowing in three independent experiments and genotyped using the Affymetrix 250k SNP array. Projected leaf area (PLA) was derived from image analysis and used to calculate relative growth rates (RGRs). In addition, initial seed size was determined. The generated datasets were used jointly for a genome-wide association study that identified 238 marker-trait associations (MTAs) individually explaining up to 8% of the total phenotypic variation. Co-localization of MTAs occurred at 33 genomic positions. At 21 of these positions, sequential co-localization of MTAs for 2-9 consecutive days was observed. The detected MTAs for PLA and RGR could be grouped according to their temporal expression patterns, emphasizing that temporal variation of MTA action can be observed even during the vegetative growth phase, a period of continuous formation and enlargement of seemingly similar rosette leaves. This indicates that causal genes may be differentially expressed in successive periods. Analyses of the temporal dynamics of biological processes are needed to gain important insight into the molecular mechanisms of growth-controlling processes in plants.
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Affiliation(s)
- Rhonda C Meyer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Kathleen Weigelt-Fischer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Dominic Knoch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Marc Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Breeding Research, Research Group Quantitative Genetics, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
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12
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Li Z, Guo R, Li M, Chen Y, Li G. A review of computer vision technologies for plant phenotyping. COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020; 176:105672. [PMID: 0 DOI: 10.1016/j.compag.2020.105672] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
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13
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Korwin Krukowski P, Ellenberger J, Röhlen-Schmittgen S, Schubert A, Cardinale F. Phenotyping in Arabidopsis and Crops-Are We Addressing the Same Traits? A Case Study in Tomato. Genes (Basel) 2020; 11:E1011. [PMID: 32867311 PMCID: PMC7564427 DOI: 10.3390/genes11091011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 08/21/2020] [Accepted: 08/24/2020] [Indexed: 11/18/2022] Open
Abstract
The convenient model Arabidopsis thaliana has allowed tremendous advances in plant genetics and physiology, in spite of only being a weed. It has also unveiled the main molecular networks governing, among others, abiotic stress responses. Through the use of the latest genomic tools, Arabidopsis research is nowadays being translated to agronomically interesting crop models such as tomato, but at a lagging pace. Knowledge transfer has been hindered by invariable differences in plant architecture and behaviour, as well as the divergent direct objectives of research in Arabidopsis versus crops compromise transferability. In this sense, phenotype translation is still a very complex matter. Here, we point out the challenges of "translational phenotyping" in the case study of drought stress phenotyping in Arabidopsis and tomato. After briefly defining and describing drought stress and survival strategies, we compare drought stress protocols and phenotyping techniques most commonly used in the two species, and discuss their potential to gain insights, which are truly transferable between species. This review is intended to be a starting point for discussion about translational phenotyping approaches among plant scientists, and provides a useful compendium of methods and techniques used in modern phenotyping for this specific plant pair as a case study.
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Affiliation(s)
- Paolo Korwin Krukowski
- Plant Stress Lab, Department of Agriculture, Forestry and Food Sciences DISAFA-Turin University, 10095 Grugliasco, Italy; (A.S.); (F.C.)
| | - Jan Ellenberger
- INRES Horticultural Sciences, University of Bonn, 53121 Bonn, Germany;
| | | | - Andrea Schubert
- Plant Stress Lab, Department of Agriculture, Forestry and Food Sciences DISAFA-Turin University, 10095 Grugliasco, Italy; (A.S.); (F.C.)
| | - Francesca Cardinale
- Plant Stress Lab, Department of Agriculture, Forestry and Food Sciences DISAFA-Turin University, 10095 Grugliasco, Italy; (A.S.); (F.C.)
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14
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Olas JJ, Fichtner F, Apelt F. All roads lead to growth: imaging-based and biochemical methods to measure plant growth. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:11-21. [PMID: 31613967 PMCID: PMC6913701 DOI: 10.1093/jxb/erz406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 08/28/2019] [Indexed: 05/31/2023]
Abstract
Plant growth is a highly complex biological process that involves innumerable interconnected biochemical and signalling pathways. Many different techniques have been developed to measure growth, unravel the various processes that contribute to plant growth, and understand how a complex interaction between genotype and environment determines the growth phenotype. Despite this complexity, the term 'growth' is often simplified by researchers; depending on the method used for quantification, growth is viewed as an increase in plant or organ size, a change in cell architecture, or an increase in structural biomass. In this review, we summarise the cellular and molecular mechanisms underlying plant growth, highlight state-of-the-art imaging and non-imaging-based techniques to quantitatively measure growth, including a discussion of their advantages and drawbacks, and suggest a terminology for growth rates depending on the type of technique used.
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Affiliation(s)
- Justyna Jadwiga Olas
- University of Potsdam, Institute of Biochemistry and Biology, Karl-Liebknecht-Straße, Haus, Potsdam, Germany
| | - Franziska Fichtner
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg, Potsdam, Germany
| | - Federico Apelt
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg, Potsdam, Germany
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15
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Chaudhury A, Barron JL. 3D Phenotyping of Plants. 3D IMAGING, ANALYSIS AND APPLICATIONS 2020:699-732. [DOI: 10.1007/978-3-030-44070-1_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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16
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Ubbens J, Cieslak M, Prusinkiewicz P, Parkin I, Ebersbach J, Stavness I. Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:5801869. [PMID: 33313558 PMCID: PMC7706325 DOI: 10.34133/2020/5801869] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 12/15/2019] [Indexed: 05/05/2023]
Abstract
Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.
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Affiliation(s)
- Jordan Ubbens
- Department of Computer Science, University of Saskatchewan, Canada
| | - Mikolaj Cieslak
- Department of Computer Science, University of Calgary, Canada
| | | | - Isobel Parkin
- Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | | | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Canada
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17
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Chaudhury A, Ward C, Talasaz A, Ivanov AG, Brophy M, Grodzinski B, Huner NPA, Patel RV, Barron JL. Machine Vision System for 3D Plant Phenotyping. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:2009-2022. [PMID: 29993836 DOI: 10.1109/tcbb.2018.2824814] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications. Since 2D based approaches have their inherent limitations, 3D plant analysis is becoming state of the art for current phenotyping technologies. We present an automated system for analyzing plant growth in indoor conditions. A gantry robot system is used to perform scanning tasks in an automated manner throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's payload captures the surface point cloud data of the plant from multiple views. The plant is monitored from the vegetative to reproductive stages in light/dark cycles inside a controllable growth chamber. An efficient 3D reconstruction algorithm is used, by which multiple scans are aligned together to obtain a 3D mesh of the plant, followed by surface area and volume computations. The whole system, including the programmable growth chamber, robot, scanner, data transfer, and analysis is fully automated in such a way that a naive user can, in theory, start the system with a mouse click and get back the growth analysis results at the end of the lifetime of the plant with no intermediate intervention. As evidence of its functionality, we show and analyze quantitative results of the rhythmic growth patterns of the dicot Arabidopsis thaliana (L.), and the monocot barley (Hordeum vulgare L.) plants under their diurnal light/dark cycles.
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18
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Paulus S. Measuring crops in 3D: using geometry for plant phenotyping. PLANT METHODS 2019; 15:103. [PMID: 31497064 PMCID: PMC6719375 DOI: 10.1186/s13007-019-0490-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 08/27/2019] [Indexed: 05/22/2023]
Abstract
Using 3D sensing for plant phenotyping has risen within the last years. This review provides an overview on 3D traits for the demands of plant phenotyping considering different measuring techniques, derived traits and use-cases of biological applications. A comparison between a high resolution 3D measuring device and an established measuring tool, the leaf meter, is shown to categorize the possible measurement accuracy. Furthermore, different measuring techniques such as laser triangulation, structure from motion, time-of-flight, terrestrial laser scanning or structured light approaches enable the assessment of plant traits such as leaf width and length, plant size, volume and development on plant and organ level. The introduced traits were shown with respect to the measured plant types, the used measuring technique and the link to their biological use case. These were trait and growth analysis for measurements over time as well as more complex investigation on water budget, drought responses and QTL (quantitative trait loci) analysis. The used processing pipelines were generalized in a 3D point cloud processing workflow showing the single processing steps to derive plant parameters on plant level, on organ level using machine learning or over time using time series measurements. Finally the next step in plant sensing, the fusion of different sensor types namely 3D and spectral measurements is introduced by an example on sugar beet. This multi-dimensional plant model is the key to model the influence of geometry on radiometric measurements and to correct it. This publication depicts the state of the art for 3D measuring of plant traits as they were used in plant phenotyping regarding how the data is acquired, how this data is processed and what kind of traits is measured at the single plant, the miniplot, the experimental field and the open field scale. Future research will focus on highly resolved point clouds on the experimental and field scale as well as on the automated trait extraction of organ traits to track organ development at these scales.
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Affiliation(s)
- Stefan Paulus
- Institute of Sugar Beet Research, Holtenser Landstr. 77, 37079 Göttingen, Germany
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19
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Ward B, Brien C, Oakey H, Pearson A, Negrão S, Schilling RK, Taylor J, Jarvis D, Timmins A, Roy SJ, Tester M, Berger B, van den Hengel A. High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare). THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 98:555-570. [PMID: 30604470 PMCID: PMC6850118 DOI: 10.1111/tpj.14225] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Revised: 12/17/2018] [Accepted: 12/19/2018] [Indexed: 05/11/2023]
Abstract
To optimize shoot growth and structure of cereals, we need to understand the genetic components controlling initiation and elongation. While measuring total shoot growth at high throughput using 2D imaging has progressed, recovering the 3D shoot structure of small grain cereals at a large scale is still challenging. Here, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images. Plant shoot modelling over time was used to measure the initiation and elongation of leaves in a bi-parental barley mapping population under low and high soil salinity. We detected quantitative trait loci (QTL) related to shoot growth per se, using both simple 2D total shoot measurements and our approach of measuring individual leaves. In addition, we detected QTL specific to leaf elongation and not to total shoot size. Of particular importance was the detection of a QTL on chromosome 3H specific to the early responses of leaf elongation to salt stress, a locus that could not be detected without the computer vision tools developed in this study.
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Affiliation(s)
- Ben Ward
- Australian Center for Visual TechnologiesUniversity of AdelaideAdelaideSA5005Australia
| | - Chris Brien
- Australian Plant Phenomics FacilityThe Plant AcceleratorSchool of Agriculture Food & WineUniversity of AdelaideUrrbraeSA5064Australia
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Phenomics and Bioinformatics Research CentreSchool of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaide5001Australia
| | - Helena Oakey
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
| | - Allison Pearson
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- ARC Centre of Excellence in Plant Energy BiologyThe University of AdelaidePMB 1, Glen OsmondAdelaideSouth Australia5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Sónia Negrão
- Division of Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Saudi Arabia
| | - Rhiannon K. Schilling
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Julian Taylor
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
| | - David Jarvis
- Division of Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Saudi Arabia
| | - Andy Timmins
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Stuart J. Roy
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Mark Tester
- Division of Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Saudi Arabia
| | - Bettina Berger
- Australian Plant Phenomics FacilityThe Plant AcceleratorSchool of Agriculture Food & WineUniversity of AdelaideUrrbraeSA5064Australia
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
| | - Anton van den Hengel
- Australian Center for Visual TechnologiesUniversity of AdelaideAdelaideSA5005Australia
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20
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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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21
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Puttonen E, Lehtomäki M, Litkey P, Näsi R, Feng Z, Liang X, Wittke S, Pandžić M, Hakala T, Karjalainen M, Pfeifer N. A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series. FRONTIERS IN PLANT SCIENCE 2019; 10:486. [PMID: 31110511 PMCID: PMC6499199 DOI: 10.3389/fpls.2019.00486] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 03/29/2019] [Indexed: 05/28/2023]
Abstract
Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset.
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Affiliation(s)
- Eetu Puttonen
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland
- Department of Remote Sensing and Photogrammetry, Centre of Excellence in Laser Scanning Research, National Land Survey of Finland, Helsinki, Finland
| | - Matti Lehtomäki
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland
| | - Paula Litkey
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland
| | - Roope Näsi
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland
| | - Ziyi Feng
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland
| | - Xinlian Liang
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland
| | - Samantha Wittke
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland
- Department of Built Environment, Aalto University, Espoo, Finland
| | - Miloš Pandžić
- University of Novi Sad, BioSense Institute, Novi Sad, Serbia
| | - Teemu Hakala
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland
- Department of Remote Sensing and Photogrammetry, Centre of Excellence in Laser Scanning Research, National Land Survey of Finland, Helsinki, Finland
| | - Mika Karjalainen
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland
- Department of Remote Sensing and Photogrammetry, Centre of Excellence in Laser Scanning Research, National Land Survey of Finland, Helsinki, Finland
| | - Norbert Pfeifer
- Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria
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22
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Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M. Plant Phenomics, From Sensors to Knowledge. Curr Biol 2018; 27:R770-R783. [PMID: 28787611 DOI: 10.1016/j.cub.2017.05.055] [Citation(s) in RCA: 226] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.
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Affiliation(s)
- François Tardieu
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, F34060, Montpellier, France.
| | - Llorenç Cabrera-Bosquet
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, F34060, Montpellier, France
| | - Tony Pridmore
- School of Computer Science, University of Nottingham, NG8 1BB, UK
| | - Malcolm Bennett
- Plant & Crop Sciences, School of Biosciences, University of Nottingham, LE12 3RD, UK.
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23
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Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges. AGRONOMY-BASEL 2018. [DOI: 10.3390/agronomy8040057] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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24
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Hu Y, Wang L, Xiang L, Wu Q, Jiang H. Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect. SENSORS 2018. [PMID: 29518958 PMCID: PMC5876734 DOI: 10.3390/s18030806] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Non-destructive plant growth measurement is essential for plant growth and health research. As a 3D sensor, Kinect v2 has huge potentials in agriculture applications, benefited from its low price and strong robustness. The paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables. The system used a turntable to acquire multi-view point clouds of the measured plant. Then a series of suitable algorithms were applied to obtain a fine 3D reconstruction for the plant, while measuring the key growth parameters including relative/absolute height, total/projected leaf area and volume. In experiment, 63 pots of lettuce in different growth stages were measured. The result shows that the Kinect-measured height and projected area have fine linear relationship with reference measurements. While the measured total area and volume both follow power law distributions with reference data. All these data have shown good fitting goodness (R2 = 0.9457–0.9914). In the study of biomass correlations, the Kinect-measured volume was found to have a good power law relationship (R2 = 0.9281) with fresh weight. In addition, the system practicality was validated by performance and robustness analysis.
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Affiliation(s)
- Yang Hu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Le Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Lirong Xiang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Qian Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Huanyu Jiang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
- Key Laboratory of On Site Processing Equipment for Agricultural Products, Ministry of Agriculture, Beijing 100125, China.
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Herrero-Huerta M, Lindenbergh R, Gard W. Leaf Movements of Indoor Plants Monitored by Terrestrial LiDAR. FRONTIERS IN PLANT SCIENCE 2018; 9:189. [PMID: 29527217 PMCID: PMC5829619 DOI: 10.3389/fpls.2018.00189] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 01/31/2018] [Indexed: 05/28/2023]
Abstract
Plant leaf movement is induced by some combination of different external and internal stimuli. Detailed geometric characterization of such movement is expected to improve understanding of these mechanisms. A metric high-quality, non-invasive and innovative sensor system to analyze plant movement is Terrestrial LiDAR (TLiDAR). This technique has an active sensor and is, therefore, independent of light conditions, able to obtain accurate high spatial and temporal resolution point clouds. In this study, a movement parameterization approach of leaf plants based on TLiDAR is introduced. For this purpose, two Calathea roseopicta plants were scanned in an indoor environment during 2 full-days, 1 day in natural light conditions and the other in darkness. The methodology to estimate leaf movement is based on segmenting individual leaves using an octree-based 3D-grid and monitoring the changes in their orientation by Principal Component Analysis. Additionally, canopy variations of the plant as a whole were characterized by a convex-hull approach. As a result, 9 leaves in plant 1 and 11 leaves in plant 2 were automatically detected with a global accuracy of 93.57 and 87.34%, respectively, compared to a manual detection. Regarding plant 1, in natural light conditions, the displacement average of the leaves between 7.00 a.m. and 12.30 p.m. was 3.67 cm as estimated using so-called deviation maps. The maximum displacement was 7.92 cm. In addition, the orientation changes of each leaf within a day were analyzed. The maximum variation in the vertical angle was 69.6° from 12.30 to 6.00 p.m. In darkness, the displacements were smaller and showed a different orientation pattern. The canopy volume of plant 1 changed more in the morning (4.42 dm3) than in the afternoon (2.57 dm3). The results of plant 2 largely confirmed the results of the first plant and were added to check the robustness of the methodology. The results show how to quantify leaf orientation variation and leaf movements along a day at mm accuracy in different light conditions. This confirms the feasibility of the proposed methodology to robustly analyse leaf movements.
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Affiliation(s)
- Mónica Herrero-Huerta
- Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands
- TIDOP Research Group, Higher Polytechnic School of Avila, University of Salamanca, Avila, Spain
- Agronomy Department, Purdue University, West Lafayette, IN, United States
| | - Roderik Lindenbergh
- Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands
| | - Wolfgang Gard
- Department of Structural and Building Engineering, Delft University of Technology, Delft, Netherlands
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Syngelaki M, Hardner M, Oberthuer P, Bley T, Schneider D, Lenk F. A new method for non-invasive biomass determination based on stereo photogrammetry. Bioprocess Biosyst Eng 2017; 41:369-380. [PMID: 29230535 DOI: 10.1007/s00449-017-1871-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 11/24/2017] [Indexed: 12/14/2022]
Abstract
A novel, non-destructive method for the biomass estimation of biological samples on culture dishes was developed. To achieve this, a photogrammetric system, which consists of a digital single-lens reflex camera (DSLR), an illuminated platform where the culture dishes are positioned and an Arduino board which controls the capturing process, was constructed. The camera was mounted on a holder which set the camera at different title angles and the platform rotated, to capture images from different directions. A software, based on stereo photogrammetry, was developed for the three-dimensional (3D) reconstruction of the samples. The proof-of-concept was demonstrated in a series of experiments with plant tissue cultures and specifically with calli cultures of Salvia fruticosa and Ocimum basilicum. For a period of 14 days images of these cultures were acquired and 3D-reconstructions and volumetric data were obtained. The volumetric data correlated well with the experimental measurements and made the calculation of the specific growth rate, µ max, possible. The µ max value for S. fruticosa samples was 0.14 day-1 and for O. basilicum 0.16 day-1. The developed method demonstrated the high potential of this photogrammetric approach in the biological sciences.
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Affiliation(s)
- Maria Syngelaki
- Chair of Bioprocess Engineering, Faculty of Mechanical Science and Engineering, Institute of Natural Materials Technology, Technische Universität Dresden, Dresden, Germany
| | - Matthias Hardner
- Chair of Photogrammetry, Department of Geosciences, Faculty of Environmental Sciences, Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Dresden, Germany
| | - Patrick Oberthuer
- Chair of Bioprocess Engineering, Faculty of Mechanical Science and Engineering, Institute of Natural Materials Technology, Technische Universität Dresden, Dresden, Germany
| | - Thomas Bley
- Chair of Bioprocess Engineering, Faculty of Mechanical Science and Engineering, Institute of Natural Materials Technology, Technische Universität Dresden, Dresden, Germany
| | - Danilo Schneider
- Chair of Photogrammetry, Department of Geosciences, Faculty of Environmental Sciences, Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Dresden, Germany
| | - Felix Lenk
- Chair of Bioprocess Engineering, Faculty of Mechanical Science and Engineering, Institute of Natural Materials Technology, Technische Universität Dresden, Dresden, Germany.
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Michaud O, Fiorucci AS, Xenarios I, Fankhauser C. Local auxin production underlies a spatially restricted neighbor-detection response in Arabidopsis. Proc Natl Acad Sci U S A 2017; 114:7444-7449. [PMID: 28652343 PMCID: PMC5514730 DOI: 10.1073/pnas.1702276114] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Competition for light triggers numerous developmental adaptations known as the "shade-avoidance syndrome" (SAS). Important molecular events underlying specific SAS responses have been identified. However, in natural environments light is often heterogeneous, and it is currently unknown how shading affecting part of a plant leads to local responses. To study this question, we analyzed upwards leaf movement (hyponasty), a rapid adaptation to neighbor proximity, in Arabidopsis We show that manipulation of the light environment at the leaf tip triggers a hyponastic response that is restricted to the treated leaf. This response is mediated by auxin synthesized in the blade and transported to the petiole. Our results suggest that a strong auxin response in the vasculature of the treated leaf and auxin signaling in the epidermis mediate leaf elevation. Moreover, the analysis of an auxin-signaling mutant reveals signaling bifurcation in the control of petiole elongation versus hyponasty. Our work identifies a mechanism for a local shade response that may pertain to other plant adaptations to heterogeneous environments.
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Affiliation(s)
- Olivier Michaud
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Anne-Sophie Fiorucci
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Ioannis Xenarios
- Swiss Institute of Bioinformatics, University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Christian Fankhauser
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, CH-1015 Lausanne, Switzerland;
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Xiong X, Yu L, Yang W, Liu M, Jiang N, Wu D, Chen G, Xiong L, Liu K, Liu Q. A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage. PLANT METHODS 2017; 13:7. [PMID: 28163771 PMCID: PMC5282657 DOI: 10.1186/s13007-017-0157-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 01/13/2017] [Indexed: 05/08/2023]
Abstract
BACKGROUND The fitness of the rape leaf is closely related to its biomass and photosynthesis. The study of leaf traits is significant for improving rape leaf production and optimizing crop management. Canopy structure and individual leaf traits are the major indicators of quality during the rape seedling stage. Differences in canopy structure reflect the influence of environmental factors such as water, sunlight and nutrient supply. The traits of individual rape leaves traits indicate the growth period of the rape as well as its canopy shape. RESULTS We established a high-throughput stereo-imaging system for the reconstruction of the three-dimensional canopy structure of rape seedlings from which leaf area and plant height can be extracted. To evaluate the measurement accuracy of leaf area and plant height, 66 rape seedlings were randomly selected for automatic and destructive measurements. Compared with the manual measurements, the mean absolute percentage error of automatic leaf area and plant height measurements was 3.68 and 6.18%, respectively, and the squares of the correlation coefficients (R2) were 0.984 and 0.845, respectively. Compared with the two-dimensional projective imaging method, the leaf area extracted using stereo-imaging was more accurate. In addition, a semi-automatic image analysis pipeline was developed to extract 19 individual leaf shape traits, including 11 scale-invariant traits, 3 inner cavity related traits, and 5 margin-related traits, from the images acquired by the stereo-imaging system. We used these quantified traits to classify rapes according to three different leaf shapes: mosaic-leaf, semi-mosaic-leaf, and round-leaf. Based on testing of 801 seedling rape samples, we found that the leave-one-out cross validation classification accuracy was 94.4, 95.6, and 94.8% for stepwise discriminant analysis, the support vector machine method and the random forest method, respectively. CONCLUSIONS In this study, a nondestructive and high-throughput stereo-imaging system was developed to quantify canopy three-dimensional structure and individual leaf shape traits with improved accuracy, with implications for rape phenotyping, functional genomics, and breeding.
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Affiliation(s)
- Xiong Xiong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan, 430074 People’s Republic of China
| | - Lejun Yu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan, 430074 People’s Republic of China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070 People’s Republic of China
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070 People’s Republic of China
| | - Meng Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070 People’s Republic of China
| | - Ni Jiang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan, 430074 People’s Republic of China
| | - Di Wu
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070 People’s Republic of China
| | - Guoxing Chen
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, Huazhong Agricultural University, Wuhan, 430070 People’s Republic of China
| | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070 People’s Republic of China
| | - Kede Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070 People’s Republic of China
| | - Qian Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan, 430074 People’s Republic of China
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Rose JC, Kicherer A, Wieland M, Klingbeil L, Töpfer R, Kuhlmann H. Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions. SENSORS (BASEL, SWITZERLAND) 2016; 16:E2136. [PMID: 27983669 PMCID: PMC5191116 DOI: 10.3390/s16122136] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 12/08/2016] [Accepted: 12/08/2016] [Indexed: 11/25/2022]
Abstract
In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.
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Affiliation(s)
- Johann Christian Rose
- Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
| | - Anna Kicherer
- Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany.
| | - Markus Wieland
- Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
| | - Lasse Klingbeil
- Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
| | - Reinhard Töpfer
- Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany.
| | - Heiner Kuhlmann
- Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
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30
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Derbidge R, Baumgartner S, Heusser P. Mistletoe Berry Outline Mapping with a Path Curve Function and Recording the Circadian Rhythm of Their Phenotypic Shape Change. FRONTIERS IN PLANT SCIENCE 2016; 7:1749. [PMID: 27933073 PMCID: PMC5122707 DOI: 10.3389/fpls.2016.01749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 11/07/2016] [Indexed: 06/06/2023]
Abstract
This paper presents a discovery: the change of the outline shape of mistletoe (Viscum album ssp. album) berries in vivo and in situ during ripening. It was found that a plant organ that is usually considered to merely increase in size actually changes shape in a specific rhythmic fashion. We introduce a new approach to chronobiological research on a macro-phenotypic scale to trace changes over long periods of time (with a resolution from hours to months) by using a dynamic form-determining parameter called Lambda (λ). λ is known in projective geometry as a measure for pertinent features of the outline shapes of egg-like forms, so called path curves. Ascertained circadian changes of form were analyzed for their correlation with environmental factors such as light, temperature, and other weather influences. Certain weather conditions such as sky cover, i.e., sunshine minutes per hour, have an impact on the amplitude of the daily change in form. The present paper suggests a possible supplement to established methods in chronobiology, as in this case the dynamic of form-change becomes a measurable feature, displaying a convincing accordance between mathematical rule and plant shape.
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Affiliation(s)
- Renatus Derbidge
- Institute of Integrative Medicine, University of Witten/HerdeckeWitten, Germany
- Research Institute at the Goetheanum, Science SectionDornach, Switzerland
| | - Stephan Baumgartner
- Institute of Integrative Medicine, University of Witten/HerdeckeWitten, Germany
- Hiscia Institute, Society for Cancer ResearchArlesheim, Switzerland
| | - Peter Heusser
- Institute of Integrative Medicine, University of Witten/HerdeckeWitten, Germany
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31
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Afonnikov DA, Genaev MA, Doroshkov AV, Komyshev EG, Pshenichnikova TA. Methods of high-throughput plant phenotyping for large-scale breeding and genetic experiments. RUSS J GENET+ 2016. [DOI: 10.1134/s1022795416070024] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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32
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Dellen B, Scharr H, Torras C. Growth Signatures of Rosette Plants from Time-Lapse Video. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:1470-8. [PMID: 26684463 DOI: 10.1109/tcbb.2015.2404810] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Plant growth is a dynamic process, and the precise course of events during early plant development is of major interest for plant research. In this work, we investigate the growth of rosette plants by processing time-lapse videos of growing plants, where we use Nicotiana tabacum (tobacco) as a model plant. In each frame of the video sequences, potential leaves are detected using a leaf-shape model. These detections are prone to errors due to the complex shape of plants and their changing appearance in the image, depending on leaf movement, leaf growth, and illumination conditions. To cope with this problem, we employ a novel graph-based tracking algorithm which can bridge gaps in the sequence by linking leaf detections across a range of neighboring frames. We use the overlap of fitted leaf models as a pairwise similarity measure, and forbid graph edges that would link leaf detections within a single frame. We tested the method on a set of tobacco-plant growth sequences, and could track the first leaves of the plant, including partially or temporarily occluded ones, along complete sequences, demonstrating the applicability of the method to automatic plant growth analysis. All seedlings displayed approximately the same growth behavior, and a characteristic growth signature was found.
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33
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Minervini M, Scharr H, Tsaftaris SA. The significance of image compression in plant phenotyping applications. FUNCTIONAL PLANT BIOLOGY : FPB 2015; 42:971-988. [PMID: 32480737 DOI: 10.1071/fp15033] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 07/01/2015] [Indexed: 06/11/2023]
Abstract
We are currently witnessing an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected using complex automated procedures and are then post-processed to extract phenotyping-related information. In this article, we show that the image compression used in such procedures may compromise phenotyping results and this needs to be taken into account. We use three illuminating proof-of-concept experiments that demonstrate that compression (especially in the most common lossy JPEG form) affects measurements of plant traits and the errors introduced can be high. We also systematically explore how compression affects measurement fidelity, quantified as effects on image quality, as well as errors in extracted plant visual traits. To do so, we evaluate a variety of image-based phenotyping scenarios, including size and colour of shoots, leaf and root growth. To show that even visual impressions can be used to assess compression effects, we use root system images as examples. Overall, we find that compression has a considerable effect on several types of analyses (albeit visual or quantitative) and that proper care is necessary to ensure that this choice does not affect biological findings. In order to avoid or at least minimise introduced measurement errors, for each scenario, we derive recommendations and provide guidelines on how to identify suitable compression options in practice. We also find that certain compression choices can offer beneficial returns in terms of reducing the amount of data storage without compromising phenotyping results. This may enable even higher throughput experiments in the future.
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Affiliation(s)
- Massimo Minervini
- Pattern Recognition and Image Analysis, IMT Institute for Advanced Studies, Lucca, Piazza S. Francesco, 19, 55100 Lucca, Italy
| | - Hanno Scharr
- Institute of Bio- and Geosciences: Plant Sciences, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
| | - Sotirios A Tsaftaris
- Pattern Recognition and Image Analysis, IMT Institute for Advanced Studies, Lucca, Piazza S. Francesco, 19, 55100 Lucca, Italy
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34
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Vadez V, Kholová J, Hummel G, Zhokhavets U, Gupta SK, Hash CT. LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget. JOURNAL OF EXPERIMENTAL BOTANY 2015; 66:5581-93. [PMID: 26034130 PMCID: PMC4585418 DOI: 10.1093/jxb/erv251] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
In this paper, we describe the thought process and initial data behind the development of an imaging platform (LeasyScan) combined with lysimetric capacity, to assess canopy traits affecting water use (leaf area, leaf area index, transpiration). LeasyScan is based on a novel 3D scanning technique to capture leaf area development continuously, a scanner-to-plant concept to increase imaging throughput and analytical scales to combine gravimetric transpiration measurements. The paper presents how the technology functions, how data are visualised via a web-based interface and how data extraction and analysis is interfaced through 'R' libraries. Close agreement between scanned and observed leaf area data of individual plants in different crops was found (R(2) between 0.86 and 0.94). Similar agreement was found when comparing scanned and observed area of plants cultivated at densities reflecting field conditions (R(2) between 0.80 and 0.96). An example in monitoring plant transpiration by the analytical scales is presented. The last section illustrates some of the early ongoing applications of the platform to target key phenotypes: (i) the comparison of the leaf area development pattern of fine mapping recombinants of pearl millet; (ii) the leaf area development pattern of pearl millet breeding material targeted to different agro-ecological zones; (iii) the assessment of the transpiration response to high VPD in sorghum and pearl millet. This new platform has the potential to phenotype for traits controlling plant water use at a high rate and precision, of critical importance for drought adaptation, and creates an opportunity to harness their genetics for the breeding of improved varieties.
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Affiliation(s)
- Vincent Vadez
- ICRISAT-Crop Physiology Laboratory, Greater Hyderabad, Patancheru 502324, Telangana, India
| | - Jana Kholová
- ICRISAT-Crop Physiology Laboratory, Greater Hyderabad, Patancheru 502324, Telangana, India
| | - Grégoire Hummel
- Phenospex, Jan Campertstraat 11 / NL-6416 SG Heerlen, The Netherlands
| | | | - S K Gupta
- ICRISAT-Crop Physiology Laboratory, Greater Hyderabad, Patancheru 502324, Telangana, India
| | - C Tom Hash
- ICRISAT, Sahelian Center, Pearl Millet Breeding, BP 12404, Niamey, Niger
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35
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Vanhaeren H, Gonzalez N, Inzé D. A Journey Through a Leaf: Phenomics Analysis of Leaf Growth in Arabidopsis thaliana. THE ARABIDOPSIS BOOK 2015; 13:e0181. [PMID: 26217168 PMCID: PMC4513694 DOI: 10.1199/tab.0181] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
In Arabidopsis, leaves contribute to the largest part of the aboveground biomass. In these organs, light is captured and converted into chemical energy, which plants use to grow and complete their life cycle. Leaves emerge as a small pool of cells at the vegetative shoot apical meristem and develop into planar, complex organs through different interconnected cellular events. Over the last decade, numerous phenotyping techniques have been developed to visualize and quantify leaf size and growth, leading to the identification of numerous genes that contribute to the final size of leaves. In this review, we will start at the Arabidopsis rosette level and gradually zoom in from a macroscopic view on leaf growth to a microscopic and molecular view. Along this journey, we describe different techniques that have been key to identify important events during leaf development and discuss approaches that will further help unraveling the complex cellular and molecular mechanisms that underlie leaf growth.
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Affiliation(s)
- Hannes Vanhaeren
- Department of Plant Systems Biology, VIB, B-9052 Gent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Gent, Belgium
| | - Nathalie Gonzalez
- Department of Plant Systems Biology, VIB, B-9052 Gent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Gent, Belgium
| | - Dirk Inzé
- Department of Plant Systems Biology, VIB, B-9052 Gent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Gent, Belgium
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36
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Apelt F, Breuer D, Nikoloski Z, Stitt M, Kragler F. Phytotyping(4D) : a light-field imaging system for non-invasive and accurate monitoring of spatio-temporal plant growth. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2015; 82:693-706. [PMID: 25801304 DOI: 10.1111/tpj.12833] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 03/04/2015] [Accepted: 03/13/2015] [Indexed: 05/08/2023]
Abstract
Integrative studies of plant growth require spatially and temporally resolved information from high-throughput imaging systems. However, analysis and interpretation of conventional two-dimensional images is complicated by the three-dimensional nature of shoot architecture and by changes in leaf position over time, termed hyponasty. To solve this problem, Phytotyping(4D) uses a light-field camera that simultaneously provides a focus image and a depth image, which contains distance information about the object surface. Our automated pipeline segments the focus images, integrates depth information to reconstruct the three-dimensional architecture, and analyses time series to provide information about the relative expansion rate, the timing of leaf appearance, hyponastic movement, and shape for individual leaves and the whole rosette. Phytotyping(4D) was calibrated and validated using discs of known sizes, and plants tilted at various orientations. Information from this analysis was integrated into the pipeline to allow error assessment during routine operation. To illustrate the utility of Phytotyping(4D) , we compare diurnal changes in Arabidopsis thaliana wild-type Col-0 and the starchless pgm mutant. Compared to Col-0, pgm showed very low relative expansion rate in the second half of the night, a transiently increased relative expansion rate at the onset of light period, and smaller hyponastic movement including delayed movement after dusk, both at the level of the rosette and individual leaves. Our study introduces light-field camera systems as a tool to accurately measure morphological and growth-related features in plants.
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Affiliation(s)
- Federico Apelt
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
- University of Potsdam, Am Neuen Palais 10, 14469, Potsdam, Germany
| | - David Breuer
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
- University of Potsdam, Am Neuen Palais 10, 14469, Potsdam, Germany
| | - Zoran Nikoloski
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Mark Stitt
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Friedrich Kragler
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
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37
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Rose JC, Paulus S, Kuhlmann H. Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level. SENSORS (BASEL, SWITZERLAND) 2015; 15:9651-65. [PMID: 25919368 PMCID: PMC4481991 DOI: 10.3390/s150509651] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Revised: 04/14/2015] [Accepted: 04/20/2015] [Indexed: 11/16/2022]
Abstract
Accessing a plant's 3D geometry has become of significant importance for phenotyping during the last few years. Close-up laser scanning is an established method to acquire 3D plant shapes in real time with high detail, but it is stationary and has high investment costs. 3D reconstruction from images using structure from motion (SfM) and multi-view stereo (MVS) is a flexible cost-effective method, but requires post-processing procedures. The aim of this study is to evaluate the potential measuring accuracy of an SfM- and MVS-based photogrammetric method for the task of organ-level plant phenotyping. For this, reference data are provided by a high-accuracy close-up laser scanner. Using both methods, point clouds of several tomato plants were reconstructed at six following days. The parameters leaf area, main stem height and convex hull of the complete plant were extracted from the 3D point clouds and compared to the reference data regarding accuracy and correlation. These parameters were chosen regarding the demands of current phenotyping scenarios. The study shows that the photogrammetric approach is highly suitable for the presented monitoring scenario, yielding high correlations to the reference measurements. This cost-effective 3D reconstruction method depicts an alternative to an expensive laser scanner in the studied scenarios with potential for automated procedures.
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Affiliation(s)
- Johann Christian Rose
- Department of Geodesy, Institute of Geodesy and Geoinformation, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
| | - Stefan Paulus
- Department of Geodesy, Institute of Geodesy and Geoinformation, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
| | - Heiner Kuhlmann
- Department of Geodesy, Institute of Geodesy and Geoinformation, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
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38
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Fahlgren N, Gehan MA, Baxter I. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. CURRENT OPINION IN PLANT BIOLOGY 2015; 24:93-9. [PMID: 25733069 DOI: 10.1016/j.pbi.2015.02.006] [Citation(s) in RCA: 306] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2015] [Revised: 02/13/2015] [Accepted: 02/13/2015] [Indexed: 05/18/2023]
Abstract
Anticipated population growth, shifting demographics, and environmental variability over the next century are expected to threaten global food security. In the face of these challenges, crop yield for food and fuel must be maintained and improved using fewer input resources. In recent years, genetic tools for profiling crop germplasm has benefited from rapid advances in DNA sequencing, and now similar advances are needed to improve the throughput of plant phenotyping. We highlight recent developments in high-throughput plant phenotyping using robotic-assisted imaging platforms and computer vision-assisted analysis tools.
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Affiliation(s)
- Noah Fahlgren
- Donald Danforth Plant Science Center, United States.
| | - Malia A Gehan
- Donald Danforth Plant Science Center, United States.
| | - Ivan Baxter
- USDA-ARS, Donald Danforth Plant Science Center, United States
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39
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Nozue K, Tat AV, Kumar Devisetty U, Robinson M, Mumbach MR, Ichihashi Y, Lekkala S, Maloof JN. Shade avoidance components and pathways in adult plants revealed by phenotypic profiling. PLoS Genet 2015; 11:e1004953. [PMID: 25874869 PMCID: PMC4398415 DOI: 10.1371/journal.pgen.1004953] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Accepted: 12/11/2014] [Indexed: 01/01/2023] Open
Abstract
Shade from neighboring plants limits light for photosynthesis; as a consequence, plants have a variety of strategies to avoid canopy shade and compete with their neighbors for light. Collectively the response to foliar shade is called the shade avoidance syndrome (SAS). The SAS includes elongation of a variety of organs, acceleration of flowering time, and additional physiological responses, which are seen throughout the plant life cycle. However, current mechanistic knowledge is mainly limited to shade-induced elongation of seedlings. Here we use phenotypic profiling of seedling, leaf, and flowering time traits to untangle complex SAS networks. We used over-representation analysis (ORA) of shade-responsive genes, combined with previous annotation, to logically select 59 known and candidate novel mutants for phenotyping. Our analysis reveals shared and separate pathways for each shade avoidance response. In particular, auxin pathway components were required for shade avoidance responses in hypocotyl, petiole, and flowering time, whereas jasmonic acid pathway components were only required for petiole and flowering time responses. Our phenotypic profiling allowed discovery of seventeen novel shade avoidance mutants. Our results demonstrate that logical selection of mutants increased success of phenotypic profiling to dissect complex traits and discover novel components.
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Affiliation(s)
- Kazunari Nozue
- Department of Plant Biology, University of California, Davis, Davis, California, United States of America
| | - An V. Tat
- Department of Plant Biology, University of California, Davis, Davis, California, United States of America
| | - Upendra Kumar Devisetty
- Department of Plant Biology, University of California, Davis, Davis, California, United States of America
| | - Matthew Robinson
- Department of Plant Biology, University of California, Davis, Davis, California, United States of America
| | - Maxwell R. Mumbach
- Department of Plant Biology, University of California, Davis, Davis, California, United States of America
| | - Yasunori Ichihashi
- Department of Plant Biology, University of California, Davis, Davis, California, United States of America
| | - Saradadevi Lekkala
- Department of Plant Biology, University of California, Davis, Davis, California, United States of America
| | - Julin N. Maloof
- Department of Plant Biology, University of California, Davis, Davis, California, United States of America
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40
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Müller-Linow M, Pinto-Espinosa F, Scharr H, Rascher U. The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool. PLANT METHODS 2015; 11:11. [PMID: 25774205 PMCID: PMC4359433 DOI: 10.1186/s13007-015-0052-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Accepted: 01/29/2015] [Indexed: 05/02/2023]
Abstract
BACKGROUND Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure to characterize these structural properties is the leaf angle distribution, which in turn requires knowledge on the 3-dimensional single leaf surface. Despite a large number of 3-d sensors and methods only a few systems are applicable for fast and routine measurements in plants and natural canopies. A suitable approach is stereo imaging, which combines depth and color information that allows for easy segmentation of green leaf material and the extraction of plant traits, such as leaf angle distribution. RESULTS We developed a software package, which provides tools for the quantification of leaf surface properties within natural canopies via 3-d reconstruction from stereo images. Our approach includes a semi-automatic selection process of single leaves and different modes of surface characterization via polygon smoothing or surface model fitting. Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations. We include a case study to demonstrate the functionality of our software. 48 images of small sugar beet populations (4 varieties) have been analyzed on the base of their leaf angle distribution in order to investigate seasonal, genotypic and fertilization effects on leaf angle distributions. We could show that leaf angle distributions change during the course of the season with all varieties having a comparable development. Additionally, different varieties had different leaf angle orientation that could be separated in principle component analysis. In contrast nitrogen treatment had no effect on leaf angles. CONCLUSIONS We show that a stereo imaging setup together with the appropriate image processing tools is capable of retrieving the geometric leaf surface properties of plants and canopies. Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management.
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Affiliation(s)
- Mark Müller-Linow
- Institute of Bio and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str, Jülich, 52425 Germany
| | - Francisco Pinto-Espinosa
- Institute of Bio and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str, Jülich, 52425 Germany
| | - Hanno Scharr
- Institute of Bio and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str, Jülich, 52425 Germany
| | - Uwe Rascher
- Institute of Bio and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str, Jülich, 52425 Germany
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41
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Dornbusch T, Michaud O, Xenarios I, Fankhauser C. Differentially phased leaf growth and movements in Arabidopsis depend on coordinated circadian and light regulation. THE PLANT CELL 2014; 26:3911-21. [PMID: 25281688 PMCID: PMC4247567 DOI: 10.1105/tpc.114.129031] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Revised: 09/04/2014] [Accepted: 09/19/2014] [Indexed: 05/18/2023]
Abstract
In contrast to vastly studied hypocotyl growth, little is known about diel regulation of leaf growth and its coordination with movements such as changes in leaf elevation angle (hyponasty). We developed a 3D live-leaf growth analysis system enabling simultaneous monitoring of growth and movements. Leaf growth is maximal several hours after dawn, requires light, and is regulated by daylength, suggesting coupling between growth and metabolism. We identify both blade and petiole positioning as important components of leaf movements in Arabidopsis thaliana and reveal a temporal delay between growth and movements. In hypocotyls, the combination of circadian expression of PHYTOCHROME INTERACTING FACTOR4 (PIF4) and PIF5 and their light-regulated protein stability drives rhythmic hypocotyl elongation with peak growth at dawn. We find that PIF4 and PIF5 are not essential to sustain rhythmic leaf growth but influence their amplitude. Furthermore, EARLY FLOWERING3, a member of the evening complex (EC), is required to maintain the correct phase between growth and movement. Our study shows that the mechanisms underlying rhythmic hypocotyl and leaf growth differ. Moreover, we reveal the temporal relationship between leaf elongation and movements and demonstrate the importance of the EC for the coordination of these phenotypic traits.
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Affiliation(s)
- Tino Dornbusch
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Olivier Michaud
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Ioannis Xenarios
- SIB-Swiss Institute of Bioinformatics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Christian Fankhauser
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
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42
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Sozzani R, Busch W, Spalding EP, Benfey PN. Advanced imaging techniques for the study of plant growth and development. TRENDS IN PLANT SCIENCE 2014; 19:304-10. [PMID: 24434036 PMCID: PMC4008707 DOI: 10.1016/j.tplants.2013.12.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Revised: 11/29/2013] [Accepted: 12/11/2013] [Indexed: 05/07/2023]
Abstract
A variety of imaging methodologies are being used to collect data for quantitative studies of plant growth and development from living plants. Multi-level data, from macroscopic to molecular, and from weeks to seconds, can be acquired. Furthermore, advances in parallelized and automated image acquisition enable the throughput to capture images from large populations of plants under specific growth conditions. Image-processing capabilities allow for 3D or 4D reconstruction of image data and automated quantification of biological features. These advances facilitate the integration of imaging data with genome-wide molecular data to enable systems-level modeling.
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Affiliation(s)
- Rosangela Sozzani
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC 27695, USA
| | - Wolfgang Busch
- Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences, 1030 Vienna, Austria
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, Madison, WI 53706 USA
| | - Philip N Benfey
- Department of Biology, Duke Center for Systems Biology, and Howard Hughes Medical Institute, Duke University, Durham, NC 27708, USA.
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43
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Hersch M, Lorrain S, de Wit M, Trevisan M, Ljung K, Bergmann S, Fankhauser C. Light intensity modulates the regulatory network of the shade avoidance response in Arabidopsis. Proc Natl Acad Sci U S A 2014; 111:6515-20. [PMID: 24733935 PMCID: PMC4035961 DOI: 10.1073/pnas.1320355111] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Plants such as Arabidopsis thaliana respond to foliar shade and neighbors who may become competitors for light resources by elongation growth to secure access to unfiltered sunlight. Challenges faced during this shade avoidance response (SAR) are different under a light-absorbing canopy and during neighbor detection where light remains abundant. In both situations, elongation growth depends on auxin and transcription factors of the phytochrome interacting factor (PIF) class. Using a computational modeling approach to study the SAR regulatory network, we identify and experimentally validate a previously unidentified role for long hypocotyl in far red 1, a negative regulator of the PIFs. Moreover, we find that during neighbor detection, growth is promoted primarily by the production of auxin. In contrast, in true shade, the system operates with less auxin but with an increased sensitivity to the hormonal signal. Our data suggest that this latter signal is less robust, which may reflect a cost-to-robustness tradeoff, a system trait long recognized by engineers and forming the basis of information theory.
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Affiliation(s)
- Micha Hersch
- Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
- Department of Medical Genetics and
| | - Séverine Lorrain
- Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
- Centre for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, CH-1015 Lausanne, Switzerland; and
| | - Mieke de Wit
- Centre for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, CH-1015 Lausanne, Switzerland; and
| | - Martine Trevisan
- Centre for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, CH-1015 Lausanne, Switzerland; and
| | - Karin Ljung
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden
| | - Sven Bergmann
- Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
- Department of Medical Genetics and
| | - Christian Fankhauser
- Centre for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, CH-1015 Lausanne, Switzerland; and
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44
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Dhondt S, Wuyts N, Inzé D. Cell to whole-plant phenotyping: the best is yet to come. TRENDS IN PLANT SCIENCE 2013; 18:428-39. [PMID: 23706697 DOI: 10.1016/j.tplants.2013.04.008] [Citation(s) in RCA: 155] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2013] [Revised: 04/18/2013] [Accepted: 04/22/2013] [Indexed: 05/18/2023]
Abstract
Imaging and image processing have revolutionized plant phenotyping and are now a major tool for phenotypic trait measurement. Here we review plant phenotyping systems by examining three important characteristics: throughput, dimensionality, and resolution. First, whole-plant phenotyping systems are highlighted together with advances in automation that enable significant throughput increases. Organ and cellular level phenotyping and its tools, often operating at a lower throughput, are then discussed as a means to obtain high-dimensional phenotypic data at elevated spatial and temporal resolution. The significance of recent developments in sensor technologies that give access to plant morphology and physiology-related traits is shown. Overall, attention is focused on spatial and temporal resolution because these are crucial aspects of imaging procedures in plant phenotyping systems.
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Affiliation(s)
- Stijn Dhondt
- Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium
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45
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Lièvre M, Wuyts N, Cookson SJ, Bresson J, Dapp M, Vasseur F, Massonnet C, Tisné S, Bettembourg M, Balsera C, Bédiée A, Bouvery F, Dauzat M, Rolland G, Vile D, Granier C. Phenotyping the kinematics of leaf development in flowering plants: recommendations and pitfalls. WILEY INTERDISCIPLINARY REVIEWS-DEVELOPMENTAL BIOLOGY 2013; 2:809-21. [PMID: 24123939 DOI: 10.1002/wdev.119] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Leaves of flowering plants are produced from the shoot apical meristem at regular intervals and they grow according to a developmental program that is determined by both genetic and environmental factors. Detailed frameworks for multiscale dynamic analyses of leaf growth have been developed in order to identify and interpret phenotypic differences caused by either genetic or environmental variations. They revealed that leaf growth dynamics are non-linearly and nonhomogeneously distributed over the lamina, in the leaf tissues and cells. The analysis of the variability in leaf growth, and its underlying processes, has recently gained momentum with the development of automated phenotyping platforms that use various technologies to record growth at different scales and at high throughput. These modern tools are likely to accelerate the characterization of gene function and the processes that underlie the control of shoot development. Combined with powerful statistical analyses, trends have emerged that may have been overlooked in low throughput analyses. However, in many examples, the increase in throughput allowed by automated platforms has led to a decrease in the spatial and/or temporal resolution of growth analyses. Concrete examples presented here indicate that simplification of the dynamic leaf system, without consideration of its spatial and temporal context, can lead to important misinterpretations of the growth phenotype.
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Affiliation(s)
- Maryline Lièvre
- Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, Montpellier, France
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46
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Spalding EP, Miller ND. Image analysis is driving a renaissance in growth measurement. CURRENT OPINION IN PLANT BIOLOGY 2013; 16:100-4. [PMID: 23352714 DOI: 10.1016/j.pbi.2013.01.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Revised: 01/02/2013] [Accepted: 01/03/2013] [Indexed: 05/21/2023]
Abstract
The domain of machine vision, in which digital images are acquired automatically in a highly structured environment for the purpose of computationally measuring features in the scene, is applicable to the measurement of plant growth. This article reviews the quickly growing collection of reports in which digital image-processing has been used to measure plant growth, with emphasis on the methodology and adaptations required for high-throughput studies of populations.
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Affiliation(s)
- Edgar P Spalding
- University of Wisconsin-Madison, Department of Botany, 430 Lincoln Drive, Madison, WI 53706, United States.
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47
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Pieruschka R, Poorter H. Phenotyping plants: genes, phenes and machines. FUNCTIONAL PLANT BIOLOGY : FPB 2012; 39:813-820. [PMID: 32480832 DOI: 10.1071/fpv39n11_in] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
No matter how fascinating the discoveries in the field of molecular biology are, in the end it is the phenotype that matters. In this paper we pay attention to various aspects of plant phenotyping. The challenges to unravel the relationship between genotype and phenotype are discussed, as well as the case where 'plants do not have a phenotype'. More emphasis has to be placed on automation to match the increased output in the molecular sciences with analysis of relevant traits under laboratory, greenhouse and field conditions. Currently, non-destructive measurements with cameras are becoming widely used to assess plant structural properties, but a wider range of non-invasive approaches and evaluation tools has to be developed to combine physiologically meaningful data with structural information of plants. Another field requiring major progress is the handling and processing of data. A better e-infrastructure will enable easier establishment of links between phenotypic traits and genetic data. In the final part of this paper we briefly introduce the range of contributions that form the core of a special issue of this journal on plant phenotyping.
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Affiliation(s)
| | - Hendrik Poorter
- IBG-2 Plant Sciences, Forschungszentrum Jülich, D-52425, Germany
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