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Sharma P, Thilakarathna I, Fennell A. Hyperspectral imaging and artificial intelligence enhance remote phenotyping of grapevine rootstock influence on whole vine photosynthesis. FRONTIERS IN PLANT SCIENCE 2024; 15:1409821. [PMID: 39363918 PMCID: PMC11446806 DOI: 10.3389/fpls.2024.1409821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 08/12/2024] [Indexed: 10/05/2024]
Abstract
Rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhancing scion physiology. Photosynthetic parameters such as maximum rate of carboxylation of RuBP (Vcmax) and the maximum rate of electron transport driving RuBP regeneration (Jmax) have been identified as ideal targets for potential influence by rootstock and breeding. However, leaf specific direct measurement of these photosynthetic parameters is time consuming, limiting the information scope and the number of individuals that can be screened. This study aims to overcome these limitations by employing hyperspectral imaging combined with artificial intelligence (AI) to predict these key photosynthetic traits at the canopy level. Hyperspectral imaging captures detailed optical properties across a broad range of wavelengths (400 to 1000 nm), enabling use of all wavelengths in a comprehensive analysis of the entire vine's photosynthetic performance (Vcmax and Jmax). Artificial intelligence-based prediction models that blend the strength of deep learning and machine learning were developed using two growing seasons data measured post-solstice at 15 h, 14 h, 13 h and 12 h daylengths for Vitis hybrid 'Marquette' grafted to five commercial rootstocks and 'Marquette' grafted to 'Marquette'. Significant differences in photosynthetic efficiency (Vcmax and Jmax) were noted for both direct and indirect measurements for the six rootstocks, indicating that rootstock genotype and daylength have a significant influence on scion photosynthesis. Evaluation of multiple feature-extraction algorithms indicated the proposed Vitis base model incorporating a 1D-Convolutional neural Network (CNN) had the best prediction performance with a R2 of 0.60 for Vcmax and Jmax. Inclusion of weather and chlorophyll parameters slightly improved model performance for both photosynthetic parameters. Integrating AI with hyperspectral remote phenotyping provides potential for high-throughput whole vine assessment of photosynthetic performance and selection of rootstock genotypes that confer improved photosynthetic performance potential in the scion.
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Affiliation(s)
| | | | - Anne Fennell
- Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, United States
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McGrath JM, Siebers MH, Fu P, Long SP, Bernacchi CJ. To have value, comparisons of high-throughput phenotyping methods need statistical tests of bias and variance. FRONTIERS IN PLANT SCIENCE 2024; 14:1325221. [PMID: 38312358 PMCID: PMC10835710 DOI: 10.3389/fpls.2023.1325221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 12/20/2023] [Indexed: 02/06/2024]
Abstract
The gap between genomics and phenomics is narrowing. The rate at which it is narrowing, however, is being slowed by improper statistical comparison of methods. Quantification using Pearson's correlation coefficient (r) is commonly used to assess method quality, but it is an often misleading statistic for this purpose as it is unable to provide information about the relative quality of two methods. Using r can both erroneously discount methods that are inherently more precise and validate methods that are less accurate. These errors occur because of logical flaws inherent in the use of r when comparing methods, not as a problem of limited sample size or the unavoidable possibility of a type I error. A popular alternative to using r is to measure the limits of agreement (LOA). However both r and LOA fail to identify which instrument is more or less variable than the other and can lead to incorrect conclusions about method quality. An alternative approach, comparing variances of methods, requires repeated measurements of the same subject, but avoids incorrect conclusions. Variance comparison is arguably the most important component of method validation and, thus, when repeated measurements are possible, variance comparison provides considerable value to these studies. Statistical tests to compare variances presented here are well established, easy to interpret and ubiquitously available. The widespread use of r has potentially led to numerous incorrect conclusions about method quality, hampering development, and the approach described here would be useful to advance high throughput phenotyping methods but can also extend into any branch of science. The adoption of the statistical techniques outlined in this paper will help speed the adoption of new high throughput phenotyping techniques by indicating when one should reject a new method, outright replace an old method or conditionally use a new method.
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Affiliation(s)
- Justin M. McGrath
- Global Change and Photosynthesis Research Unit, USDA-Agricultural Research Service (ARS), Urbana, IL, United States
- Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL, United States
| | - Matthew H. Siebers
- Global Change and Photosynthesis Research Unit, USDA-Agricultural Research Service (ARS), Urbana, IL, United States
- Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL, United States
| | - Peng Fu
- Center for Advanced Agriculture and Sustainability, Harrisburg University of Science and Technology, Harrisburg, PA, United States
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, Urbana, IL, United States
| | - Stephen P. Long
- Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL, United States
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, Urbana, IL, United States
- Department of Crop Sciences, University of Illinois, Urbana-Champaign, Urbana, IL, United States
| | - Carl J. Bernacchi
- Global Change and Photosynthesis Research Unit, USDA-Agricultural Research Service (ARS), Urbana, IL, United States
- Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL, United States
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, Urbana, IL, United States
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Walker BJ, Driever SM, Kromdijk J, Lawson T, Busch FA. Tools for Measuring Photosynthesis at Different Scales. Methods Mol Biol 2024; 2790:1-26. [PMID: 38649563 DOI: 10.1007/978-1-0716-3790-6_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Measurements of in vivo photosynthesis are powerful tools that probe the largest fluxes of carbon and energy in an illuminated leaf, but often the specific techniques used are so varied and specialized that it is difficult for researchers outside the field to select and perform the most useful assays for their research questions. The goal of this chapter is to provide a broad overview of the current tools available for the study of photosynthesis, both in vivo and in vitro, so as to provide a foundation for selecting appropriate techniques, many of which are presented in detail in subsequent chapters. This chapter will also organize current methods into a comparative framework and provide examples of how they have been applied to research questions of broad agronomical, ecological, or biological importance. This chapter closes with an argument that the future of in vivo measurements of photosynthesis lies in the ability to use multiple methods simultaneously and discusses the benefits of this approach to currently open physiological questions. This chapter, combined with the relevant methods chapters, could serve as a laboratory course in methods in photosynthesis research or as part of a more comprehensive laboratory course in general plant physiology methods.
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Affiliation(s)
- Berkley J Walker
- Plant Research Laboratory, Michigan State University, East Lansing, MI, USA
- Department of Plant Biology, Michigan State University, East Lansing, MI, USA
| | - Steven M Driever
- Centre for Crop Systems Analysis, Wageningen University and Research, Wageningen, The Netherlands
| | - Johannes Kromdijk
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana, IL, USA
| | - Tracy Lawson
- School of Life Sciences, University of Essex, Colchester, UK
| | - Florian A Busch
- School of Biosciences and The Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK.
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Mertens S, Verbraeken L, Sprenger H, De Meyer S, Demuynck K, Cannoot B, Merchie J, De Block J, Vogel JT, Bruce W, Nelissen H, Maere S, Inzé D, Wuyts N. Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform. PLANT METHODS 2023; 19:132. [PMID: 37996870 PMCID: PMC10668392 DOI: 10.1186/s13007-023-01102-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. RESULTS The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants' water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. CONCLUSION Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
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Affiliation(s)
- Stien Mertens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Lennart Verbraeken
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Heike Sprenger
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
- Food Safety Department , German Federal Institute for Risk Assessment, Max-Dohrn-Str. 8-10, 10589, Berlin, Germany
| | - Sam De Meyer
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
- Robovision, Technologiepark 80, 9052, Zwijnaarde, Belgium
| | - Kirin Demuynck
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Bernard Cannoot
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Julie Merchie
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
- Eenheid Plant, Instituut voor Landbouw, Visserij-en Voedingsonderzoek (ILVO), Caritasstraat 39, 9090, Melle, Belgium
| | - Jolien De Block
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | | | - Wesley Bruce
- BASF Corporation, 2 TW Alexander Drive, Durham, NC, 27709, USA
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Steven Maere
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Dirk Inzé
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium.
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium.
| | - Nathalie Wuyts
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
- Plant Production Systems, Cultivation Techniques and Varieties in Arable Farming, Agroscope, Route de Duillier 50, 1260, Nyon, Switzerland
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Li Y, Zhang P, Sheng W, Zhang Z, Rose RJ, Song Y. Securing maize reproductive success under drought stress by harnessing CO 2 fertilization for greater productivity. FRONTIERS IN PLANT SCIENCE 2023; 14:1221095. [PMID: 37860252 PMCID: PMC10582713 DOI: 10.3389/fpls.2023.1221095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/19/2023] [Indexed: 10/21/2023]
Abstract
Securing maize grain yield is crucial to meet food and energy needs for the future growing population, especially under frequent drought events and elevated CO2 (eCO2) due to climate change. To maximize the kernel setting rate under drought stress is a key strategy in battling against the negative impacts. Firstly, we summarize the major limitations to leaf source and kernel sink in maize under drought stress, and identified that loss in grain yield is mainly attributed to reduced kernel set. Reproductive drought tolerance can be realized by collective contribution with a greater assimilate import into ear, more available sugars for ovary and silk use, and higher capacity to remobilize assimilate reserve. As such, utilization of CO2 fertilization by improved photosynthesis and greater reserve remobilization is a key strategy for coping with drought stress under climate change condition. We propose that optimizing planting methods and mining natural genetic variation still need to be done continuously, meanwhile, by virtue of advanced genetic engineering and plant phenomics tools, the breeding program of higher photosynthetic efficiency maize varieties adapted to eCO2 can be accelerated. Consequently, stabilizing maize production under drought stress can be achieved by securing reproductive success by harnessing CO2 fertilization.
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Affiliation(s)
- Yangyang Li
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
| | - Pengpeng Zhang
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
| | - Wenjing Sheng
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
| | - Zixiang Zhang
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
| | - Ray J. Rose
- School of Environmental and Life Sciences, The University of Newcastle, Newcastle, NSW, Australia
| | - Youhong Song
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
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Ting TC, Souza ACM, Imel RK, Guadagno CR, Hoagland C, Yang Y, Wang DR. Quantifying physiological trait variation with automated hyperspectral imaging in rice. FRONTIERS IN PLANT SCIENCE 2023; 14:1229161. [PMID: 37799551 PMCID: PMC10548215 DOI: 10.3389/fpls.2023.1229161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023]
Abstract
Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.
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Affiliation(s)
- To-Chia Ting
- Agronomy Department, Purdue University, West Lafayette, IN, United States
| | - Augusto C. M. Souza
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Rachel K. Imel
- Agronomy Department, Purdue University, West Lafayette, IN, United States
| | | | - Chris Hoagland
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Yang Yang
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Diane R. Wang
- Agronomy Department, Purdue University, West Lafayette, IN, United States
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Song Q, Liu F, Bu H, Zhu XG. Quantifying Contributions of Different Factors to Canopy Photosynthesis in 2 Maize Varieties: Development of a Novel 3D Canopy Modeling Pipeline. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0075. [PMID: 37502446 PMCID: PMC10371248 DOI: 10.34133/plantphenomics.0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 07/01/2023] [Indexed: 07/29/2023]
Abstract
Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate (Ac) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in Ac between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in Ac (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with Ac and plant architecture traits were non-linearly related to Ac. This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.
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Affiliation(s)
- Qingfeng Song
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Fusang Liu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Hongyi Bu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
| | - Xin-Guang Zhu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
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Wong CYS. Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications. AOB PLANTS 2023; 15:plad039. [PMID: 37560760 PMCID: PMC10407989 DOI: 10.1093/aobpla/plad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/04/2023] [Indexed: 08/11/2023]
Abstract
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
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Wong CYS, Jones T, McHugh DP, Gilbert ME, Gepts P, Palkovic A, Buckley TN, Magney TS. TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology. PLANT METHODS 2023; 19:29. [PMID: 36978119 PMCID: PMC10044391 DOI: 10.1186/s13007-023-01001-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF). RESULTS We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions. CONCLUSIONS TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.
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Affiliation(s)
| | - Taylor Jones
- Department of Earth & Environment, Boston University, Boston, MA 02215 USA
| | - Devin P. McHugh
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Matthew E. Gilbert
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Paul Gepts
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Antonia Palkovic
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Thomas N. Buckley
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Troy S. Magney
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
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10
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Singh A, Karjagi C, Kaur S, Jeet G, Bhamare D, Gupta S, Kumar S, Das A, Gupta M, Chaudhary DP, Bhushan B, Jat BS, Kumar R, Dagla MC, Kumar M. Characterization of phi112, a Molecular Marker Tightly Linked to the o2 Gene of Maize, and Its Utilization in Multiplex PCR for Differentiating Normal Maize from QPM. Genes (Basel) 2023; 14:531. [PMID: 36833458 PMCID: PMC9957476 DOI: 10.3390/genes14020531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/14/2023] [Accepted: 02/17/2023] [Indexed: 02/25/2023] Open
Abstract
Quality Protein Maize (QPM) contains higher amounts of essential amino acids lysine and tryptophan. The QPM phenotype is based on regulating zein protein synthesis by opaque2 transcription factor. Many gene modifiers act to optimize the amino acid content and agronomic performance. An SSR marker, phi112, is present upstream of the opaque2 DNA gene. Its analysis has shown the presence of transcription factor activity. The functional associations of opaque2 have been determined. The putative transcription factor binding at phi112 marked DNA was identified through computational analysis. The present study is a step towards understanding the intricate network of molecular interactions that fine-tune the QPM genotype to influence maize protein quality. In addition, a multiplex PCR assay for differentiation of QPM from normal maize is shown, which can be used for Quality Control at various stages of the QPM value chain.
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Affiliation(s)
- Alla Singh
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - Chikkappa Karjagi
- ICAR-Indian Institute of Maize Research, Pusa Campus, Delhi 110012, India
| | - Sehgeet Kaur
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141004, India
| | - Gagan Jeet
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141004, India
| | - Deepak Bhamare
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - Sonu Gupta
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - Sunil Kumar
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - Abhijit Das
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - Mamta Gupta
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - D. P. Chaudhary
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - Bharat Bhushan
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - B. S. Jat
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - Ramesh Kumar
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - M. C. Dagla
- ICAR-Indian Institute of Maize Research, P.A.U. Campus, Ludhiana 141004, India
| | - Manoj Kumar
- ICAR—Central Institute for Research on Cotton Technology, Mumbai 400019, India
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11
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Han P, Zhai Y, Liu W, Lin H, An Q, Zhang Q, Ding S, Zhang D, Pan Z, Nie X. Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton ( Gossypium hirsutum L.) under Different Nitrogen Fertilizer Application Based on Machine Learning Algorithms. PLANTS (BASEL, SWITZERLAND) 2023; 12:455. [PMID: 36771540 PMCID: PMC9919998 DOI: 10.3390/plants12030455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/16/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Hyperspectral technology has enabled rapid and efficient nitrogen monitoring in crops. However, most approaches involve direct monitoring of nitrogen content or physiological and biochemical indicators directly related to nitrogen, which cannot reflect the overall plant nutritional status. Two important photosynthetic traits, the fraction of absorbed photosynthetically active radiation (FAPAR) and the net photosynthetic rate (Pn), were previously shown to respond positively to nitrogen changes. Here, Pn and FAPAR were used for correlation analysis with hyperspectral data to establish a relationship between nitrogen status and hyperspectral characteristics through photosynthetic traits. Using principal component and band autocorrelation analyses of the original spectral reflectance, two band positions (350-450 and 600-750 nm) sensitive to nitrogen changes were obtained. The performances of four machine learning algorithm models based on six forms of hyperspectral transformations showed that the light gradient boosting machine (LightGBM) model based on the hyperspectral first derivative could better invert the Pn of function-leaves in cotton, and the random forest (RF) model based on hyperspectral first derivative could better invert the FAPAR of the cotton canopy. These results provide advanced metrics for non-destructive tracking of cotton nitrogen status, which can be used to diagnose nitrogen nutrition and cotton growth status in large farms.
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Affiliation(s)
- Peng Han
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Yaping Zhai
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Wenhong Liu
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Hairong Lin
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Qiushuang An
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Qi Zhang
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Shugen Ding
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Dawei Zhang
- Research Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
| | - Zhenyuan Pan
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Xinhui Nie
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
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12
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Wong CYS, Gilbert ME, Pierce MA, Parker TA, Palkovic A, Gepts P, Magney TS, Buckley TN. Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0021. [PMID: 37040284 PMCID: PMC10076057 DOI: 10.34133/plantphenomics.0021] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/12/2022] [Indexed: 06/19/2023]
Abstract
Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits (R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.
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13
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Puppala N, Nayak SN, Sanz-Saez A, Chen C, Devi MJ, Nivedita N, Bao Y, He G, Traore SM, Wright DA, Pandey MK, Sharma V. Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance. Front Genet 2023; 14:1121462. [PMID: 36968584 PMCID: PMC10030941 DOI: 10.3389/fgene.2023.1121462] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 02/06/2023] [Indexed: 03/29/2023] Open
Abstract
Climate change is significantly impacting agricultural production worldwide. Peanuts provide food and nutritional security to millions of people across the globe because of its high nutritive values. Drought and heat stress alone or in combination cause substantial yield losses to peanut production. The stress, in addition, adversely impact nutritional quality. Peanuts exposed to drought stress at reproductive stage are prone to aflatoxin contamination, which imposes a restriction on use of peanuts as health food and also adversely impact peanut trade. A comprehensive understanding of the impact of drought and heat stress at physiological and molecular levels may accelerate the development of stress tolerant productive peanut cultivars adapted to a given production system. Significant progress has been achieved towards the characterization of germplasm for drought and heat stress tolerance, unlocking the physiological and molecular basis of stress tolerance, identifying significant marker-trait associations as well major QTLs and candidate genes associated with drought tolerance, which after validation may be deployed to initiate marker-assisted breeding for abiotic stress adaptation in peanut. The proof of concept about the use of transgenic technology to add value to peanuts has been demonstrated. Advances in phenomics and artificial intelligence to accelerate the timely and cost-effective collection of phenotyping data in large germplasm/breeding populations have also been discussed. Greater focus is needed to accelerate research on heat stress tolerance in peanut. A suits of technological innovations are now available in the breeders toolbox to enhance productivity and nutritional quality of peanuts in harsh environments. A holistic breeding approach that considers drought and heat-tolerant traits to simultaneously address both stresses could be a successful strategy to produce climate-resilient peanut genotypes with improved nutritional quality.
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Affiliation(s)
- Naveen Puppala
- Agricultural Science Center at Clovis, New Mexico State University, Las Cruces, NM, United States
- *Correspondence: Naveen Puppala,
| | - Spurthi N. Nayak
- Department of Biotechnology, University of Agricultural Sciences, Dharwad, India
| | - Alvaro Sanz-Saez
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Charles Chen
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Mura Jyostna Devi
- USDA-ARS Vegetable Crops Research, Madison, WI, United States
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, United States
| | - Nivedita Nivedita
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, United States
| | - Yin Bao
- Biosystems Engineering Department, Auburn University, Auburn, AL, United States
| | - Guohao He
- Department of Plant and Soil Sciences, Tuskegee University, Tuskegee, AL, United States
| | - Sy M. Traore
- Department of Plant and Soil Sciences, Tuskegee University, Tuskegee, AL, United States
| | - David A. Wright
- Department of Biotechnology, Iowa State University, Ames, IA, United States
| | - Manish K. Pandey
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India
| | - Vinay Sharma
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India
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14
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Song G, Wang Q, Jin J. Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information. JOURNAL OF PLANT PHYSIOLOGY 2022; 279:153831. [PMID: 36252398 DOI: 10.1016/j.jplph.2022.153831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/09/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
Partial least squares regression (PLSR) is applied increasingly often to predict plant photosynthesis from reflectance spectra. While its applicability across different areas has been examined in previous studies, its stability across time has yet to be evaluated. In this study, we assessed a series of PLSR models built upon three different band selection approaches (iterative stepwise, genetic algorithm, and uninformative variable elimination), in combination with different spectral transforms (original and first-order derivative spectra), for their stabilities in predicting the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) from hyperspectral reflectance spectra at different temporal scales (seasonal and interannual). The results showed that both photosynthetic parameters can be estimated from leaf hyperspectral reflectance with moderate to good accuracy across different growing stages (R2 = 0.45-0.84) and years (R2 = 0.37-0.97). We further found that the iterative stepwise selection of informative bands when building PLSR models could greatly improve its predictive capacity compared with that of other PLSR models, especially those based on first-order derivative spectra. However, the selected bands of the models for both photosynthetic parameters were, unfortunately not consistent. Furthermore, we could not have identified any model with fixed spectra performed consistently across different seasonal stages and across different years. However, the blue spectral regions were popularly selected throughout the growing stages and in different years. The results demonstrate that leaf spectra-trait estimation using PLSR models varies with time and thus cast doubt over the use of a specific PLSR model to infer leaf traits across different temporal-spatial contexts. The development of a general applicable PLSR model is still in the works.
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Affiliation(s)
- Guangman Song
- Graduate School of Science and Technology, Shizuoka University, Shizuoka, 422-8529, Japan
| | - Quan Wang
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan.
| | - Jia Jin
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan
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15
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Messina CD, Rotundo J, Hammer GL, Gho C, Reyes A, Fang Y, van Oosterom E, Borras L, Cooper M. Radiation use efficiency increased over a century of maize (Zea mays L.) breeding in the US corn belt. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5503-5513. [PMID: 35640591 DOI: 10.1093/jxb/erac212] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 05/23/2022] [Indexed: 05/26/2023]
Abstract
In the absence of stress, crop growth depends on the amount of light intercepted by the canopy and the conversion efficiency [radiation use efficiency (RUE)]. This study tested the hypothesis that long-term genetic gain for grain yield was partly due to improved RUE. The hypothesis was tested using 30 elite maize hybrids commercialized in the US corn belt between 1930 and 2017. Crops grown under irrigation showed that pre-flowering crop growth increased at a rate of 0.11 g m-2 year-1, while light interception remained constant. Therefore, RUE increased at a rate of 0.0049 g MJ-1 year-1, translating into an average of 3 g m-2 year-1 of grain yield over 100 years of maize breeding. Considering that the harvest index has not changed for crops grown at optimal density for the hybrid, the cumulative RUE increase over the history of commercial maize breeding in the USA can account for ~32% of the documented yield trend for maize grown in the central US corn belt. The remaining RUE gap between this study and theoretical maximum values suggests that a yield improvement of a similar magnitude could be achieved by further increasing RUE.
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Affiliation(s)
- Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Jose Rotundo
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Graeme L Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carla Gho
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Andres Reyes
- Corteva Agriscience, 18369 County Rd 96, Woodland, CA, USA
| | - Yinan Fang
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Erik van Oosterom
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Lucas Borras
- Corteva Agriscience, 8305 62nd Avenue, Johnston, IA 50131, USA
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
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16
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Khruschev SS, Plyusnina TY, Antal TK, Pogosyan SI, Riznichenko GY, Rubin AB. Machine learning methods for assessing photosynthetic activity: environmental monitoring applications. Biophys Rev 2022; 14:821-842. [PMID: 36124273 PMCID: PMC9481805 DOI: 10.1007/s12551-022-00982-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 10/15/2022] Open
Abstract
Monitoring of the photosynthetic activity of natural and artificial biocenoses is of crucial importance. Photosynthesis is the basis for the existence of life on Earth, and a decrease in primary photosynthetic production due to anthropogenic influences can have catastrophic consequences. Currently, great efforts are being made to create technologies that allow continuous monitoring of the state of the photosynthetic apparatus of terrestrial plants and microalgae. There are several sources of information suitable for assessing photosynthetic activity, including gas exchange and optical (reflectance and fluorescence) measurements. The advent of inexpensive optical sensors makes it possible to collect data locally (manually or using autonomous sea and land stations) and globally (using aircraft and satellite imaging). In this review, we consider machine learning methods proposed for determining the functional parameters of photosynthesis based on local and remote optical measurements (hyperspectral imaging, solar-induced chlorophyll fluorescence, local chlorophyll fluorescence imaging, and various techniques of fast and delayed chlorophyll fluorescence induction). These include classical and novel (such as Partial Least Squares) regression methods, unsupervised cluster analysis techniques, various classification methods (support vector machine, random forest, etc.) and artificial neural networks (multilayer perceptron, long short-term memory, etc.). Special aspects of time-series analysis are considered. Applicability of particular information sources and mathematical methods for assessment of water quality and prediction of algal blooms, for estimation of primary productivity of biocenoses, stress tolerance of agricultural plants, etc. is discussed.
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Affiliation(s)
- S. S. Khruschev
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| | - T. Yu. Plyusnina
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| | - T. K. Antal
- Laboratory of Integrated Environmental Research, Pskov State University, Pskov, 180000 Russia
| | - S. I. Pogosyan
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| | - G. Yu. Riznichenko
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| | - A. B. Rubin
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
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17
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Xu R, Li C. A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots. PLANT PHENOMICS 2022; 2022:9760269. [PMID: 36059604 PMCID: PMC9394113 DOI: 10.34133/2022/9760269] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/25/2022] [Indexed: 12/03/2022]
Abstract
Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans. The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems. This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors, manually pushed or motorized carts, gantries, and cable-driven systems. Then, a detailed review of autonomous ground phenotyping robots is provided with regard to the robot's main components, including mobile platforms, sensors, manipulators, computing units, and software. It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets. At the end of the review, this paper discusses current major challenges and future research directions.
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Affiliation(s)
- Rui Xu
- Bio-Sensing and Instrumentation Laboratory, College of Engineering, The University of Georgia, Athens, USA
| | - Changying Li
- Bio-Sensing and Instrumentation Laboratory, College of Engineering, The University of Georgia, Athens, USA
- Phenomics and Plant Robotics Center, The University of Georgia, Athens, USA
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18
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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19
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Montes CM, Fox C, Sanz-Sáez Á, Serbin SP, Kumagai E, Krause MD, Xavier A, Specht JE, Beavis WD, Bernacchi CJ, Diers BW, Ainsworth EA. High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population. Genetics 2022; 221:iyac065. [PMID: 35451475 PMCID: PMC9157091 DOI: 10.1093/genetics/iyac065] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 04/03/2022] [Indexed: 11/14/2022] Open
Abstract
Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.
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Affiliation(s)
| | - Carolyn Fox
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Álvaro Sanz-Sáez
- Department of Crop, Soil, and Environmental Sciences, Auburn, AL 36849, USA
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Etsushi Kumagai
- Institute of Agro-environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8604, Japan
| | - Matheus D Krause
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA 50011, USA
| | - Alencar Xavier
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
- Department of Biostatistics, Corteva Agrisciences, Johnston, IA 50131, USA
| | - James E Specht
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - William D Beavis
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA 50011, USA
| | - Carl J Bernacchi
- Global Change and Photosynthesis Research Unit, USDA ARS, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Brian W Diers
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Elizabeth A Ainsworth
- Global Change and Photosynthesis Research Unit, USDA ARS, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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20
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Fu P, Montes CM, Siebers MH, Gomez-Casanovas N, McGrath JM, Ainsworth EA, Bernacchi CJ. Advances in field-based high-throughput photosynthetic phenotyping. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3157-3172. [PMID: 35218184 PMCID: PMC9126737 DOI: 10.1093/jxb/erac077] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/23/2022] [Indexed: 05/22/2023]
Abstract
Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.
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Affiliation(s)
- Peng Fu
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Christopher M Montes
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Matthew H Siebers
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Nuria Gomez-Casanovas
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Justin M McGrath
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Elizabeth A Ainsworth
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Carl J Bernacchi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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21
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Burnett AC, Kromdijk J. Can we improve the chilling tolerance of maize photosynthesis through breeding? JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3138-3156. [PMID: 35143635 PMCID: PMC9126739 DOI: 10.1093/jxb/erac045] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 02/02/2022] [Indexed: 05/11/2023]
Abstract
Chilling tolerance is necessary for crops to thrive in temperate regions where cold snaps and lower baseline temperatures place limits on life processes; this is particularly true for crops of tropical origin such as maize. Photosynthesis is often adversely affected by chilling stress, yet the maintenance of photosynthesis is essential for healthy growth and development, and most crucially for yield. In this review, we describe the physiological basis for enhancing chilling tolerance of photosynthesis in maize by examining nine key responses to chilling stress. We synthesize current knowledge of genetic variation for photosynthetic chilling tolerance in maize with respect to each of these traits and summarize the extent to which genetic mapping and candidate genes have been used to understand the genomic regions underpinning chilling tolerance. Finally, we provide perspectives on the future of breeding for photosynthetic chilling tolerance in maize. We advocate for holistic and high-throughput approaches to screen for chilling tolerance of photosynthesis in research and breeding programmes in order to develop resilient crops for the future.
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Affiliation(s)
- Angela C Burnett
- Department of Plant Sciences, University of CambridgeCambridge, UK
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22
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Robles-Zazueta CA, Pinto F, Molero G, Foulkes MJ, Reynolds MP, Murchie EH. Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck. FRONTIERS IN PLANT SCIENCE 2022; 13:828451. [PMID: 35481146 PMCID: PMC9036448 DOI: 10.3389/fpls.2022.828451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R 2 = 0.48, RMSE = 5.24 μmol m-2 s-1 and stomatal conductance: R 2 = 0.36, RMSE = 0.14 mol m-2 s-1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R 2 = 0.3, p < 0.05; R 2 = 0.29, p < 0.05) and at 7 days after anthesis (R 2 = 0.15, p < 0.05; R 2 = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.
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Affiliation(s)
- Carlos A. Robles-Zazueta
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - M. John Foulkes
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
| | - Matthew P. Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Erik H. Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
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23
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Zhi X, Massey-Reed SR, Wu A, Potgieter A, Borrell A, Hunt C, Jordan D, Zhao Y, Chapman S, Hammer G, George-Jaeggli B. Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:9768502. [PMID: 35498954 PMCID: PMC9013486 DOI: 10.34133/2022/9768502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/25/2022] [Indexed: 05/04/2023]
Abstract
Sorghum, a genetically diverse C4 cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation (V cmax), phosphoenolpyruvate (PEP) carboxylation (V pmax), and electron transport (J max), quantified using a C4 photosynthesis model, were evaluated in two field-grown training sets (n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V cmax (R 2 = 0.83), V pmax (R 2 = 0.93), J max (R 2 = 0.76), SLN (R 2 = 0.82), and LMA (R 2 = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V cmax, V pmax, J max, SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 (n = 875 plots including 650 genotypes) and 2020 (n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V cmax and two QTL for J max. Candidate genes within 200 kb of the V cmax QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se. J max QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.
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Affiliation(s)
- Xiaoyu Zhi
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Sean Reynolds Massey-Reed
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Alex Wu
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
| | - Andries Potgieter
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia
| | - Andrew Borrell
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Colleen Hunt
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
- Agri-Science Queensland, Department of Agriculture and Fisheries (DAF), Hermitage Research Facility, Warwick, QLD, Australia
| | - David Jordan
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Yan Zhao
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia
| | - Scott Chapman
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
- School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, Australia
| | - Graeme Hammer
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
| | - Barbara George-Jaeggli
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
- Agri-Science Queensland, Department of Agriculture and Fisheries (DAF), Hermitage Research Facility, Warwick, QLD, Australia
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24
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Buchaillot ML, Soba D, Shu T, Liu J, Aranjuelo I, Araus JL, Runion GB, Prior SA, Kefauver SC, Sanz-Saez A. Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models. PLANTA 2022; 255:93. [PMID: 35325309 PMCID: PMC8948130 DOI: 10.1007/s00425-022-03867-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
MAIN CONCLUSION By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.
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Affiliation(s)
- Ma Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - David Soba
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - Tianchu Shu
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA
| | - Juan Liu
- Industrial Crops Research Institute, Henan Academy of Agricultural Sciences, Henan, China
| | - Iker Aranjuelo
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - G Brett Runion
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Stephen A Prior
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Shawn C Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain.
| | - Alvaro Sanz-Saez
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA.
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25
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Tanner F, Tonn S, de Wit J, Van den Ackerveken G, Berger B, Plett D. Sensor-based phenotyping of above-ground plant-pathogen interactions. PLANT METHODS 2022; 18:35. [PMID: 35313920 PMCID: PMC8935837 DOI: 10.1186/s13007-022-00853-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/08/2022] [Indexed: 05/20/2023]
Abstract
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.
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Affiliation(s)
- Florian Tanner
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Sebastian Tonn
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Jos de Wit
- Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
| | - Guido Van den Ackerveken
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Bettina Berger
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Darren Plett
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
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26
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Md Saleh R, Kulig B, Arefi A, Hensel O, Sturm B. Prediction of total carotenoids, color and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Rosalizan Md Saleh
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
- Industrial Crops Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang, Selangor Malaysia
| | - Boris Kulig
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Arman Arefi
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Oliver Hensel
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
| | - Barbara Sturm
- Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhofstrasse. 1a 37213 Witzenhausen Germany
- Leibniz Institute for Agricultural Engineering and Bioeconomy(ATB) Max‐Eyth‐Allee 100 14469 Potsdam Germany
- Humboldt Universität zu Berlin Albrecht Daniel Thaer Institute for Agricultural and Horticultural Sciences 10115 Berlin Germany
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27
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Jin J, Wang Q, Song G. Selecting informative bands for partial least squares regressions improves their goodness-of-fits to estimate leaf photosynthetic parameters from hyperspectral data. PHOTOSYNTHESIS RESEARCH 2022; 151:71-82. [PMID: 34491493 DOI: 10.1007/s11120-021-00873-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 08/24/2021] [Indexed: 06/13/2023]
Abstract
The plant photosynthetic capacity determines the photosynthetic rates of the terrestrial biosphere. Timely approaches to obtain the spatiotemporal variations of the photosynthetic parameters are urgently needed to grasp the gas exchange rhythms of the terrestrial biosphere. While partial least squares regression (PLSR) is a promising way to predict the photosynthetic parameters maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) rapidly and non-destructively from hyperspectral data, the approach, however, faces a high risk of overfitting and remains a high hurdle for applications. In this study, we propose to incorporate proper band selection techniques for PLSR analysis to refine the goodness-of-fit (GoF) in estimating Vcmax and Jmax. Different band selection procedures coupled with different hyperspectral forms (reflectance, apparent absorption, as well as derivatives) were examined. Our results demonstrate that the GoFs of PLSR models could be greatly improved by combining proper band selection methods (especially the iterative stepwise elimination approach) rather than using full bands as commonly done with PLSR. The results also show that the 1st order derivative spectra had a balance between accuracy (R2 = 0.80 for Vcmax, and 0.94 for Jmax) and denoising (when a Gaussian noise was added to each leaf reflectance spectrum at each wavelength with a standard deviation of 1%) on retrieving photosynthetic parameters from hyperspectral data. Our results clearly illustrate the advantage of using the band selection approach for PLSR dimensionality reduction and model optimization, highlighting the superiority of using derivative spectra for Vcmax and Jmax estimations, which should provide valuable insights for retrieving photosynthetic parameters from hyperspectral remotely sensed data.
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Affiliation(s)
- Jia Jin
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan
- Institute of Geography and Oceanography, Nanning Normal University, Nanning, 530001, China
| | - Quan Wang
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan.
- Research Institute of Green Science and Technology, Shizuoka University, Shizuoka, 422-8529, Japan.
| | - Guangman Song
- Graduate School of Science and Technology, Shizuoka University, Shizuoka, 422-8529, Japan
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28
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Melandri G, Thorp KR, Broeckling C, Thompson AL, Hinze L, Pauli D. Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance. FRONTIERS IN PLANT SCIENCE 2021; 12:751868. [PMID: 34745185 PMCID: PMC8569624 DOI: 10.3389/fpls.2021.751868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 09/30/2021] [Indexed: 06/13/2023]
Abstract
The study of phenotypes that reveal mechanisms of adaptation to drought and heat stress is crucial for the development of climate resilient crops in the face of climate uncertainty. The leaf metabolome effectively summarizes stress-driven perturbations of the plant physiological status and represents an intermediate phenotype that bridges the plant genome and phenome. The objective of this study was to analyze the effect of water deficit and heat stress on the leaf metabolome of 22 genetically diverse accessions of upland cotton grown in the Arizona low desert over two consecutive years. Results revealed that membrane lipid remodeling was the main leaf mechanism of adaptation to drought. The magnitude of metabolic adaptations to drought, which had an impact on fiber traits, was found to be quantitatively and qualitatively associated with different stress severity levels during the two years of the field trial. Leaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions. Multivariate statistical models using hyperspectral data accurately estimated (R 2 > 0.7 in ∼34% of the metabolites) and predicted (Q 2 > 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status.
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Affiliation(s)
- Giovanni Melandri
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Kelly R. Thorp
- United States Department of Agriculture-Agricultural Research Service, Arid Land Agricultural Research Center, Maricopa, AZ, United States
| | - Corey Broeckling
- Analytical Resources Core: Bioanalysis and Omics Center, Colorado State University, Fort Collins, CO, United States
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
| | - Alison L. Thompson
- United States Department of Agriculture-Agricultural Research Service, Arid Land Agricultural Research Center, Maricopa, AZ, United States
| | - Lori Hinze
- United States Department of Agriculture-Agricultural Research Service, Southern Plains Agricultural Research Center, College Station, TX, United States
| | - Duke Pauli
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
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29
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Liu F, Song Q, Zhao J, Mao L, Bu H, Hu Y, Zhu XG. Canopy occupation volume as an indicator of canopy photosynthetic capacity. THE NEW PHYTOLOGIST 2021; 232:941-956. [PMID: 34245568 DOI: 10.1111/nph.17611] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/03/2021] [Indexed: 06/13/2023]
Abstract
Leaf angle and leaf area index together influence canopy light interception and canopy photosynthesis. However, so far, there is no effective method to identify the optimal combination of these two parameters for canopy photosynthesis. In this study, first a robust high-throughput method for accurate segmentation of maize organs based on 3D point clouds data was developed, then the segmented plant organs were used to generate new 3D point clouds for the canopy of altered architectures. With this, we simulated the synergistic effect of leaf area and leaf angle on canopy photosynthesis. The results show that, compared to the traditional parameters describing the canopy photosynthesis including leaf area index, facet angle and canopy coverage, a new parameter - the canopy occupation volume (COV) - can better explain the variations of canopy photosynthetic capacity. Specifically, COV can explain > 79% variations of canopy photosynthesis generated by changing leaf angle and > 84% variations of canopy photosynthesis generated by changing leaf area. As COV can be calculated in a high-throughput manner based on the canopy point clouds, it can be used to evaluate canopy architecture in breeding and agronomic research.
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Affiliation(s)
- Fusang Liu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qingfeng Song
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jinke Zhao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Linxiong Mao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hongyi Bu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Yong Hu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Xin-Guang Zhu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
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30
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Burnett AC, Serbin SP, Davidson KJ, Ely KS, Rogers A. Detection of the metabolic response to drought stress using hyperspectral reflectance. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:6474-6489. [PMID: 34235536 DOI: 10.1093/jxb/erab255] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/02/2021] [Indexed: 06/13/2023]
Abstract
Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.
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Affiliation(s)
- Angela C Burnett
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kenneth J Davidson
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kim S Ely
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Alistair Rogers
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
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31
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Li W, Zhou X, Yu K, Zhang Z, Liu Y, Hu N, Liu Y, Yao C, Yang X, Wang Z, Zhang Y. Spectroscopic Estimation of N Concentration in Wheat Organs for Assessing N Remobilization Under Different Irrigation Regimes. FRONTIERS IN PLANT SCIENCE 2021; 12:657578. [PMID: 33897747 PMCID: PMC8062884 DOI: 10.3389/fpls.2021.657578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 03/15/2021] [Indexed: 06/12/2023]
Abstract
Nitrogen (N) remobilization is a critical process that provides substantial N to winter wheat grains for improving yield productivity. Here, the remobilization of N from anthesis to maturity in two wheat cultivars under three irrigation regimes was measured and its relationship to organ N concentration was examined. Based on spectral data of organ powder samples, partial least squares regression (PLSR) models were calibrated to estimate N concentration (N mass) and validated against laboratory-based measurements. Although spectral reflectance could accurately estimate N mass, the PLSR-based N mass-spectra predictive model was found to be organ-specific, organs at the top canopy (chaff and top three leaves) received the best predictions (R 2 > 0.88). In addition, N remobilization efficiency (NRE) in the top two leaves and top third internode was highly correlated with its corresponding N concentration change (ΔN mass) with an R 2 of 0.90. ΔN mass of the top first internode (TIN1) explained 78% variation of the whole-plant NRE. This study provides a proof of concept for estimating N concentration and assessing N remobilization using hyperspectral data of individual organs, which offers a non-chemical and low-cost approach to screen germplasms for an optimal NRE in drought-resistance breeding.
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Affiliation(s)
- Wei Li
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Xiaonan Zhou
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Kang Yu
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Zhen Zhang
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Yang Liu
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Naiyue Hu
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Ying Liu
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Chunsheng Yao
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Xiaoguang Yang
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Zhimin Wang
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- Engineering Technology Research Center for Agriculture in Low Plain Areas, Cangzhou, China
| | - Yinghua Zhang
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- Engineering Technology Research Center for Agriculture in Low Plain Areas, Cangzhou, China
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Moore CE, Meacham-Hensold K, Lemonnier P, Slattery RA, Benjamin C, Bernacchi CJ, Lawson T, Cavanagh AP. The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:2822-2844. [PMID: 33619527 PMCID: PMC8023210 DOI: 10.1093/jxb/erab090] [Citation(s) in RCA: 104] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 02/19/2021] [Indexed: 05/03/2023]
Abstract
As global land surface temperature continues to rise and heatwave events increase in frequency, duration, and/or intensity, our key food and fuel cropping systems will likely face increased heat-related stress. A large volume of literature exists on exploring measured and modelled impacts of rising temperature on crop photosynthesis, from enzymatic responses within the leaf up to larger ecosystem-scale responses that reflect seasonal and interannual crop responses to heat. This review discusses (i) how crop photosynthesis changes with temperature at the enzymatic scale within the leaf; (ii) how stomata and plant transport systems are affected by temperature; (iii) what features make a plant susceptible or tolerant to elevated temperature and heat stress; and (iv) how these temperature and heat effects compound at the ecosystem scale to affect crop yields. Throughout the review, we identify current advancements and future research trajectories that are needed to make our cropping systems more resilient to rising temperature and heat stress, which are both projected to occur due to current global fossil fuel emissions.
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Affiliation(s)
- Caitlin E Moore
- School of Agriculture and Environment, The University of Western Australia, Crawley, Australia
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, USA
| | - Katherine Meacham-Hensold
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, USA
| | | | - Rebecca A Slattery
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, USA
| | - Claire Benjamin
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, USA
| | - Carl J Bernacchi
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, USA
- Global Change and Photosynthesis Research Unit, United States Department of Agriculture–Agricultural Research Service, Urbana, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, USA
| | - Tracy Lawson
- School of Life Sciences, University of Essex, Colchester, UK
| | - Amanda P Cavanagh
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, USA
- School of Life Sciences, University of Essex, Colchester, UK
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Khan HA, Nakamura Y, Furbank RT, Evans JR. Effect of leaf temperature on the estimation of photosynthetic and other traits of wheat leaves from hyperspectral reflectance. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:1271-1281. [PMID: 33252664 DOI: 10.1093/jxb/eraa514] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 10/30/2020] [Indexed: 05/21/2023]
Abstract
A growing number of leaf traits can be estimated from hyperspectral reflectance data. These include structural and compositional traits, such as leaf mass per area (LMA) and nitrogen and chlorophyll content, but also physiological traits such a Rubisco carboxylation activity, electron transport rate, and respiration rate. Since physiological traits vary with leaf temperature, how does this impact on predictions made from reflectance measurements? We investigated this with two wheat varieties, by repeatedly measuring each leaf through a sequence of temperatures imposed by varying the air temperature in a growth room. Leaf temperatures ranging from 20 °C to 35 °C did not alter the estimated Rubisco capacity normalized to 25 °C (Vcmax25), or chlorophyll or nitrogen contents per unit leaf area. Models estimating LMA and Vcmax25/N were both slightly influenced by leaf temperature: estimated LMA increased by 0.27% °C-1 and Vcmax25/N increased by 0.46% °C-1. A model estimating Rubisco activity closely followed variation associated with leaf temperature. Reflectance spectra change with leaf temperature and therefore contain a temperature signal.
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Affiliation(s)
- Hammad A Khan
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, The Australian National University, Canberra, ACT, Australia
- Department of Primary Industries and Regional Development (DPIRD), Northam, WA, Australia
| | - Yukiko Nakamura
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, The Australian National University, Canberra, ACT, Australia
- Graduate School of Life Sciences, Tohoku University, Japan
| | - Robert T Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, The Australian National University, Canberra, ACT, Australia
- CSIRO Agriculture & Food, Canberra, ACT, Australia
| | - John R Evans
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, The Australian National University, Canberra, ACT, Australia
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Fu P, Meacham-Hensold K, Siebers MH, Bernacchi CJ. The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: benefits for field phenotyping. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:1295-1306. [PMID: 33340310 PMCID: PMC7904154 DOI: 10.1093/jxb/eraa537] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 12/02/2020] [Indexed: 05/08/2023]
Abstract
Improving photosynthesis is considered a promising way to increase crop yield to feed a growing population. Realizing this goal requires non-destructive techniques to quantify photosynthetic variation among crop cultivars. Despite existing remote sensing-based approaches, it remains a question whether solar-induced fluorescence (SIF) can facilitate screening crop cultivars of improved photosynthetic capacity in plant breeding trials. Here we tested a hypothesis that SIF yield rather than SIF had a better relationship with the maximum electron transport rate (Jmax). Time-synchronized hyperspectral images and irradiance spectra of sunlight under clear-sky conditions were combined to estimate SIF and SIF yield, which were then correlated with ground-truth Vcmax and Jmax. With observations binned over time (i.e. group 1: 6, 7, and 12 July 2017; group 2: 31 July and 18 August 2017; and group 3: 24 and 25 July 2018), SIF yield showed a stronger negative relationship, compared with SIF, with photosynthetic variables. Using SIF yield for Jmax (Vcmax) predictions, the regression analysis exhibited an R2 of 0.62 (0.71) and root mean square error (RMSE) of 11.88 (46.86) μmol m-2 s-1 for group 1, an R2 of 0.85 (0.72) and RMSE of 13.51 (49.32) μmol m-2 s-1 for group 2, and an R2 of 0.92 (0.87) and RMSE of 15.23 (30.29) μmol m-2 s-1 for group 3. The combined use of hyperspectral images and irradiance measurements provides an alternative yet promising approach to characterization of photosynthetic parameters at plot level.
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Affiliation(s)
- Peng Fu
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Katherine Meacham-Hensold
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Matthew H Siebers
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- USDA-ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
| | - Carl J Bernacchi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- USDA-ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
- Correspondence:
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Emerging approaches to measure photosynthesis from the leaf to the ecosystem. Emerg Top Life Sci 2021; 5:261-274. [PMID: 33527993 PMCID: PMC8166339 DOI: 10.1042/etls20200292] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/12/2021] [Accepted: 01/14/2021] [Indexed: 12/03/2022]
Abstract
Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorological methods have also become more standardized tools and have an advanced understanding of ecosystem functioning under a changing environment and through long time series data coupled with community data sharing. Second, we review proximal and remote sensing approaches to measure photosynthesis, including hyperspectral reflectance- and fluorescence-based techniques. These techniques have long been used with aircraft and orbiting satellites, but lower-cost sensors and improved statistical analyses are allowing these techniques to become applicable at smaller scales to quantify changes in the underlying biochemistry of photosynthesis. Within the past decade measurements of chlorophyll fluorescence from earth-orbiting satellites have measured Solar Induced Fluorescence (SIF) enabling estimates of global ecosystem productivity. Finally, we highlight that stronger interactions of scientists across disciplines will benefit our capacity to accurately estimate productivity at regional and global scales. Applying the multiple techniques outlined in this review at scales from the leaf to the globe are likely to advance understanding of plant functioning from the organelle to the ecosystem.
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Li D, Quan C, Song Z, Li X, Yu G, Li C, Muhammad A. High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field. Front Bioeng Biotechnol 2021; 8:623705. [PMID: 33520974 PMCID: PMC7838587 DOI: 10.3389/fbioe.2020.623705] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/15/2020] [Indexed: 11/13/2022] Open
Abstract
Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.
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Affiliation(s)
- Daoliang Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Chaoqun Quan
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhaoyang Song
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Xiang Li
- Department of Psychology, College of Education, Hubei University, Wuhan, China
| | - Guanghui Yu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Cheng Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Akhter Muhammad
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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Salter WT, Li S, Dracatos PM, Barbour MM. Identification of quantitative trait loci for dynamic and steady-state photosynthetic traits in a barley mapping population. AOB PLANTS 2020; 12:plaa063. [PMID: 33408849 PMCID: PMC7759950 DOI: 10.1093/aobpla/plaa063] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 11/18/2020] [Indexed: 05/29/2023]
Abstract
Enhancing the photosynthetic induction response to fluctuating light has been suggested as a key target for improvement in crop breeding programmes, with the potential to substantially increase whole-canopy carbon assimilation and contribute to crop yield potential. Rubisco activation may be the main physiological process that will allow us to achieve such a goal. In this study, we assessed the phenotype of Rubisco activation rate in a doubled haploid (DH) barley mapping population [131 lines from a Yerong/Franklin (Y/F) cross] after a switch from moderate to saturating light. Rates of Rubisco activation were found to be highly variable across the mapping population, with a median activation rate of 0.1 min-1 in the slowest genotype and 0.74 min-1 in the fastest genotype. A unique quantitative trait locus (QTL) for Rubisco activation rate was identified on chromosome 7H. This is the first report on the identification of a QTL for Rubisco activation rate in planta and the discovery opens the door to marker-assisted breeding to improve whole-canopy photosynthesis of barley. This also suggests that genetic factors other than the previously characterized Rubisco activase (RCA) isoforms on chromosome 4H control Rubisco activity. Further strength is given to this finding as this QTL co-localized with QTLs identified for steady-state photosynthesis and stomatal conductance. Several other distinct QTLs were identified for these steady-state traits, with a common overlapping QTL on chromosome 2H, and distinct QTLs for photosynthesis and stomatal conductance identified on chromosomes 4H and 5H, respectively. Future work should aim to validate these QTLs under field conditions so that they can be used to aid plant breeding efforts.
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Affiliation(s)
- William T Salter
- School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Brownlow Hill, NSW, Australia
| | - Si Li
- School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Brownlow Hill, NSW, Australia
| | - Peter M Dracatos
- Plant Breeding Institute, The University of Sydney, Cobbitty, NSW, Australia
| | - Margaret M Barbour
- School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Brownlow Hill, NSW, Australia
- School of Science, University of Waikato, Hillcrest, Hamilton, New Zealand
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Cotrozzi L, Peron R, Tuinstra MR, Mickelbart MV, Couture JJ. Spectral Phenotyping of Physiological and Anatomical Leaf Traits Related with Maize Water Status. PLANT PHYSIOLOGY 2020; 184:1363-1377. [PMID: 32907885 PMCID: PMC7608158 DOI: 10.1104/pp.20.00577] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 08/31/2020] [Indexed: 05/04/2023]
Abstract
Advancements in phenotyping techniques capable of rapidly and nondestructively detecting impacts of drought on crops are necessary to meet the 21st-century challenge of food security. Here, we describe the use of hyperspectral reflectance to predict variation in physiological and anatomical leaf traits related with water status under varying water availability in six maize (Zea mays) hybrids that differ in yield stability under drought. We also assessed relationships among traits and collections of traits with yield stability. Measurements were collected in both greenhouse and field environments, with plants exposed to different levels of water stress or to natural water availability, respectively. Leaf spectral measurements were paired with a number of physiological and anatomical reference measurements, and predictive spectral models were constructed using a partial least-squares regression approach. All traits were relatively well predicted by spectroscopic models, with external validation (i.e. by applying partial least-squares regression coefficients on a dataset distinct from the one used for calibration) goodness-of-fit (R 2 ) ranging from 0.37 to 0.89 and normalized error ranging from 12% to 21%. Correlations between reference and predicted data were statistically similar for both greenhouse and field data. Our findings highlight the capability of vegetation spectroscopy to rapidly and nondestructively identify a number of foliar functional traits affected by drought that can be used as indicators of plant water status. Although we did not detect trait coordination with yield stability in the hybrids used in this study, expanding the range of functional traits estimated by hyperspectral data can help improve trait-based breeding approaches.
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Affiliation(s)
- Lorenzo Cotrozzi
- Department of Entomology, Purdue University, West Lafayette, Indiana 47907
- Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana 47907
| | - Raquel Peron
- Department of Entomology, Purdue University, West Lafayette, Indiana 47907
- Interdiciplinary Life Science Education Program, Purdue University, West Lafayette, Indiana 47907
| | | | - Michael V Mickelbart
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, Indiana 47907
- Purdue Center for Plant Biology, Purdue University, West Lafayette, Indiana 47907
| | - John J Couture
- Department of Entomology, Purdue University, West Lafayette, Indiana 47907
- Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana 47907
- Purdue Center for Plant Biology, Purdue University, West Lafayette, Indiana 47907
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Crandall SG, Gold KM, Jiménez-Gasco MDM, Filgueiras CC, Willett DS. A multi-omics approach to solving problems in plant disease ecology. PLoS One 2020; 15:e0237975. [PMID: 32960892 PMCID: PMC7508392 DOI: 10.1371/journal.pone.0237975] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 08/04/2020] [Indexed: 12/11/2022] Open
Abstract
The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions.
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Affiliation(s)
- Sharifa G. Crandall
- Department of Plant Pathology and Environmental Microbiology, The Pennsylvania State University, University Park, PA, United States of America
| | - Kaitlin M. Gold
- Plant Pathology & Plant Microbe Biology Section, Cornell AgriTech, Cornell University, Geneva, NY, United States of America
| | - María del Mar Jiménez-Gasco
- Department of Plant Pathology and Environmental Microbiology, The Pennsylvania State University, University Park, PA, United States of America
| | - Camila C. Filgueiras
- Applied Chemical Ecology Technology, Cornell AgriTech, Cornell University, Geneva, NY, United States of America
| | - Denis S. Willett
- Applied Chemical Ecology Technology, Cornell AgriTech, Cornell University, Geneva, NY, United States of America
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von Caemmerer S. Rubisco carboxylase/oxygenase: From the enzyme to the globe: A gas exchange perspective. JOURNAL OF PLANT PHYSIOLOGY 2020; 252:153240. [PMID: 32707452 DOI: 10.1016/j.jplph.2020.153240] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 07/12/2020] [Indexed: 05/28/2023]
Abstract
Rubisco is the primary carboxylase of the photosynthetic process, the most abundant enzyme in the biosphere, and also one of the best-characterized enzymes. Rubisco also functions as an oxygenase, a discovery made 50 years ago by Bill Ogren. Carboxylation of ribulose bisphosphate (RuBP) is the first step of the photosynthetic carbon reduction cycle and leads to the assimilation of CO2, whereas the oxygenase activity necessitates the recycling of phosphoglycolate through the photorespiratory carbon oxidation cycle with concomitant loss of CO2. Since the discovery of Rubisco's dual function, the biochemical properties of Rubisco have underpinned the mechanistic mathematical models of photosynthetic CO2 fixation which link Rubisco kinetic properties to gas exchange of leaves. This has allowed assessments of global CO2 exchange and predictions of how Rubisco has and will shape the environmental responses of crop and global photosynthesis in future climates. Rubisco's biochemical properties, including its slow catalytic turnover and poor affinity for CO2, constrain crop growth and therefore improving its activity and regulation and minimising photorespiration are key targets for crop improvement.
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Affiliation(s)
- Susanne von Caemmerer
- Australian Research Council Centre of Excellence for Translational Photosynthesis, Division of Plant Science, Research School of Biology, The Australian National University, Acton, Australian Capital Territory, 2601, Australia.
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41
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Evans JR, Lawson T. From green to gold: agricultural revolution for food security. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:2211-2215. [PMID: 32251509 DOI: 10.1093/jxb/eraa110] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Affiliation(s)
- John R Evans
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, The Australian National University, Canberra, ACT, Australia
| | - Tracy Lawson
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, UK
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