1
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Liu Q, Liu H, Li C, Liu X, Liu G, Li Z. Citric acid treatment inhibits fading of sorghum (Sorghum bicolor) by modulating the accumulation of flavonoids. Food Chem 2024; 460:140612. [PMID: 39089034 DOI: 10.1016/j.foodchem.2024.140612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 07/14/2024] [Accepted: 07/22/2024] [Indexed: 08/03/2024]
Abstract
Sorghum seeds can discolor during storage. Treatment of seeds with citric acid improves sensory quality and antioxidant activity. This study compared the differences in phenotypic and antioxidant activity between citric acid-treated and water-treated sorghum seeds. The study used transcriptomics and metabolomics approaches to investigate the regulatory mechanisms. The ∆a, ∆b and ∆l values of citric acid-treated sorghum seeds significantly increased after 6 months of storage. The SOD, POD and CAT enzyme activities of the citric acid-treated group were 1.94, 1.91 and 2.45 times higher than those of the control, respectively. The joint transcriptome and metabolome analysis showed that the citric acid-induced changes were mainly focused on the flavonoid biosynthetic pathway. Citric acid treatment up-regulated CHS, ANR, MYB and bHLH genes and promoted flavonoid accumulation. In conclusion, citric acid treatment promotes flavonoid accumulation, delays sorghum seed discoloration, and enhances antioxidant activity and storage life.
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Affiliation(s)
- Qiyuan Liu
- College of Agriculture/Institute of Rice Industry Technology Research, Key Laboratory of Plant Resource, Guizhou University, Guiyang 550025, China; CRRC Guiyang Vehicle Co., LTD., 550025,China.
| | - Hongkai Liu
- Department of Food Science and Nutrition, College of Culture and Tourism, University of Jinan, Jinan 250002, China.
| | - ChenLan Li
- Green Pesticide National Key Laboratory, Fine Chemical Research and Development Center, Guizhou University, Guiyang 550025,China.
| | - XueXu Liu
- College of Agriculture/Institute of Rice Industry Technology Research, Key Laboratory of Plant Resource, Guizhou University, Guiyang 550025, China.
| | - Gang Liu
- CRRC Guiyang Vehicle Co., LTD., 550025,China
| | - Zhenhua Li
- College of Agriculture/Institute of Rice Industry Technology Research, Key Laboratory of Plant Resource, Guizhou University, Guiyang 550025, China.
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2
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Caine RS, Khan MS, Brench RA, Walker HJ, Croft HL. Inside-out: Synergising leaf biochemical traits with stomatal-regulated water fluxes to enhance transpiration modelling during abiotic stress. PLANT, CELL & ENVIRONMENT 2024. [PMID: 38533601 DOI: 10.1111/pce.14892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/17/2024] [Accepted: 03/08/2024] [Indexed: 03/28/2024]
Abstract
As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant-environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r2 = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal-based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing-based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields.
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Affiliation(s)
- Robert S Caine
- Plants, Photosynthesis and Soil, School of Biosciences, University of Sheffield, South Yorkshire, UK
- School of Biosciences, Institute for Sustainable Food, University of Sheffield, South Yorkshire, UK
| | - Muhammad S Khan
- Plants, Photosynthesis and Soil, School of Biosciences, University of Sheffield, South Yorkshire, UK
| | - Robert A Brench
- Plants, Photosynthesis and Soil, School of Biosciences, University of Sheffield, South Yorkshire, UK
| | - Heather J Walker
- Plants, Photosynthesis and Soil, School of Biosciences, University of Sheffield, South Yorkshire, UK
- School of Biosciences, Institute for Sustainable Food, University of Sheffield, South Yorkshire, UK
- biOMICS Mass Spectrometry Facility, School of Biosciences, University of Sheffield, South Yorkshire, UK
| | - Holly L Croft
- Plants, Photosynthesis and Soil, School of Biosciences, University of Sheffield, South Yorkshire, UK
- School of Biosciences, Institute for Sustainable Food, University of Sheffield, South Yorkshire, UK
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3
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Ding G, Shen L, Dai J, Jackson R, Liu S, Ali M, Sun L, Wen M, Xiao J, Deakin G, Jiang D, Wang XE, Zhou J. The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0128. [PMID: 38148766 PMCID: PMC10750832 DOI: 10.34133/plantphenomics.0128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/23/2023] [Indexed: 12/28/2023]
Abstract
Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.
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Affiliation(s)
- Guohui Ding
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Liyan Shen
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Jie Dai
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Robert Jackson
- Cambridge Crop Research,
National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
| | - Shuchen Liu
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Mujahid Ali
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Li Sun
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute,
Nanjing Agricultural University/JCIC-MCP, Nanjing, Jiangsu 210095, China
| | - Mingxing Wen
- Zhenjiang Institute of Agricultural Science, Jurong, Jiangsu 212400, China
| | - Jin Xiao
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute,
Nanjing Agricultural University/JCIC-MCP, Nanjing, Jiangsu 210095, China
| | - Greg Deakin
- Cambridge Crop Research,
National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
| | - Dong Jiang
- Regional Technique Innovation Center for Wheat Production, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Xiu-e Wang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute,
Nanjing Agricultural University/JCIC-MCP, Nanjing, Jiangsu 210095, China
| | - Ji Zhou
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
- Cambridge Crop Research,
National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
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4
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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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5
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Wang X, Zeng H, Lin L, Huang Y, Lin H, Que Y. Deep learning-empowered crop breeding: intelligent, efficient and promising. FRONTIERS IN PLANT SCIENCE 2023; 14:1260089. [PMID: 37860239 PMCID: PMC10583549 DOI: 10.3389/fpls.2023.1260089] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/13/2023] [Indexed: 10/21/2023]
Abstract
Crop breeding is one of the main approaches to increase crop yield and improve crop quality. However, the breeding process faces challenges such as complex data, difficulties in data acquisition, and low prediction accuracy, resulting in low breeding efficiency and long cycle. Deep learning-based crop breeding is a strategy that applies deep learning techniques to improve and optimize the breeding process, leading to accelerated crop improvement, enhanced breeding efficiency, and the development of higher-yielding, more adaptive, and disease-resistant varieties for agricultural production. This perspective briefly discusses the mechanisms, key applications, and impact of deep learning in crop breeding. We also highlight the current challenges associated with this topic and provide insights into its future application prospects.
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Affiliation(s)
- Xiaoding Wang
- Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
| | - Haitao Zeng
- Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
| | - Limei Lin
- Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
| | - Yanze Huang
- School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
| | - Hui Lin
- Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
| | - Youxiong Que
- Key Laboratory of Sugarcane Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
- National Key Laboratory for Tropical Crop Breeding, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Hainan, China
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6
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Poorter H, Hummel GM, Nagel KA, Fiorani F, von Gillhaussen P, Virnich O, Schurr U, Postma JA, van de Zedde R, Wiese-Klinkenberg A. Pitfalls and potential of high-throughput plant phenotyping platforms. FRONTIERS IN PLANT SCIENCE 2023; 14:1233794. [PMID: 37680357 PMCID: PMC10481964 DOI: 10.3389/fpls.2023.1233794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/01/2023] [Indexed: 09/09/2023]
Abstract
Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.
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Affiliation(s)
- Hendrik Poorter
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Natural Sciences, Macquarie University, North Ryde, NSW, Australia
| | | | - Kerstin A. Nagel
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Fabio Fiorani
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Olivia Virnich
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Ulrich Schurr
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | - Anika Wiese-Klinkenberg
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Bioinformatics (IBG-4), Forschungszentrum Jülich GmbH, Jülich, Germany
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7
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Pasam RK, Kant S, Thoday-Kennedy E, Dimech A, Joshi S, Keeble-Gagnere G, Forrest K, Tibbits J, Hayden M. Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat. PLANTS (BASEL, SWITZERLAND) 2023; 12:2367. [PMID: 37375992 DOI: 10.3390/plants12122367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023]
Abstract
Soil salinity can impose substantial stress on plant growth and cause significant yield losses. Crop varieties tolerant to salinity stress are needed to sustain yields in saline soils. This requires effective genotyping and phenotyping of germplasm pools to identify novel genes and QTL conferring salt tolerance that can be utilised in crop breeding schemes. We investigated a globally diverse collection of 580 wheat accessions for their growth response to salinity using automated digital phenotyping performed under controlled environmental conditions. The results show that digitally collected plant traits, including digital shoot growth rate and digital senescence rate, can be used as proxy traits for selecting salinity-tolerant accessions. A haplotype-based genome-wide association study was conducted using 58,502 linkage disequilibrium-based haplotype blocks derived from 883,300 genome-wide SNPs and identified 95 QTL for salinity tolerance component traits, of which 54 were novel and 41 overlapped with previously reported QTL. Gene ontology analysis identified a suite of candidate genes for salinity tolerance, some of which are already known to play a role in stress tolerance in other plant species. This study identified wheat accessions that utilise different tolerance mechanisms and which can be used in future studies to investigate the genetic and genic basis of salinity tolerance. Our results suggest salinity tolerance has not arisen from or been bred into accessions from specific regions or groups. Rather, they suggest salinity tolerance is widespread, with small-effect genetic variants contributing to different levels of tolerance in diverse, locally adapted germplasm.
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Affiliation(s)
- Raj K Pasam
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Surya Kant
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | | | - Adam Dimech
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Sameer Joshi
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia
| | | | - Kerrie Forrest
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Josquin Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Matthew Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
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8
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Schmidt L, Jacobs J, Schmutzer T, Alqudah AM, Sannemann W, Pillen K, Maurer A. Identifying genomic regions determining shoot and root traits related to nitrogen uptake efficiency in a multiparent advanced generation intercross (MAGIC) winter wheat population in a high-throughput phenotyping facility. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 330:111656. [PMID: 36841338 DOI: 10.1016/j.plantsci.2023.111656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 01/17/2023] [Accepted: 02/18/2023] [Indexed: 06/18/2023]
Abstract
In the context of a continuously increasing human population that needs to be fed, with environmental protection in mind, nitrogen use efficiency (NUE) improvement is becoming very important. To understand the natural variation of traits linked to nitrogen uptake efficiency (UPE), one component of NUE, the multiparent advanced generation intercross (MAGIC) winter wheat population WM-800 was phenotyped under two contrasting nitrogen (N) levels in a high-throughput phenotyping facility for six weeks. Three biomass-related, three root-related, and two reflectance-related traits were measured weekly under each treatment. Subsequently, the population was genetically analysed using a total of 13,060 polymorphic haplotypes and singular SNPs for a genome-wide association study (GWAS). In total, we detected 543 quantitative trait loci (QTL) across all time points and traits, which were pooled into 42 stable QTL (sQTL; present in at least three of the six weeks). Besides Rht-B1 and Rht-D1, candidate genes playing a role in gibberellic acid-regulated growth and nitrate transporter genes from the NPF gene family, like NRT 1.1, were linked to sQTL. Two novel sQTL on chromosomes 5 A and 6D showed pleiotropic effects on several traits. The high number of N-specific sQTL indicates that selection for UPE is useful specifically under N-limited conditions.
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Affiliation(s)
- Laura Schmidt
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - John Jacobs
- BASF BBCC Innovation Center Gent, 9052 Gent, Belgium
| | - Thomas Schmutzer
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Ahmad M Alqudah
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany; Biological Science Program, Department of Biological and Environmental Sciences, College of Art and Science, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Wiebke Sannemann
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Klaus Pillen
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Andreas Maurer
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany.
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9
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Sharma N, Banerjee BP, Hayden M, Kant S. An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse. PLANTS (BASEL, SWITZERLAND) 2023; 12:317. [PMID: 36679030 PMCID: PMC9866171 DOI: 10.3390/plants12020317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.
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Affiliation(s)
- Neelesh Sharma
- Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd, Horsham, VIC 3400, Australia
| | - Bikram Pratap Banerjee
- Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd, Horsham, VIC 3400, Australia
| | - Matthew Hayden
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Melbourne, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Melbourne, VIC 3083, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd, Horsham, VIC 3400, Australia
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Melbourne, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Melbourne, VIC 3083, Australia
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10
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Eckardt NA, Ainsworth EA, Bahuguna RN, Broadley MR, Busch W, Carpita NC, Castrillo G, Chory J, DeHaan LR, Duarte CM, Henry A, Jagadish SVK, Langdale JA, Leakey ADB, Liao JC, Lu KJ, McCann MC, McKay JK, Odeny DA, Jorge de Oliveira E, Platten JD, Rabbi I, Rim EY, Ronald PC, Salt DE, Shigenaga AM, Wang E, Wolfe M, Zhang X. Climate change challenges, plant science solutions. THE PLANT CELL 2023; 35:24-66. [PMID: 36222573 PMCID: PMC9806663 DOI: 10.1093/plcell/koac303] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community.
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Affiliation(s)
| | - Elizabeth A Ainsworth
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, Illinois 61801, USA
| | - Rajeev N Bahuguna
- Centre for Advanced Studies on Climate Change, Dr Rajendra Prasad Central Agricultural University, Samastipur 848125, Bihar, India
| | - Martin R Broadley
- School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, UK
- Rothamsted Research, West Common, Harpenden, Hertfordshire, AL5 2JQ, UK
| | - Wolfgang Busch
- Plant Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | - Nicholas C Carpita
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
| | - Gabriel Castrillo
- School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, UK
- Future Food Beacon of Excellence, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Joanne Chory
- Plant Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California 92037, USA
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | | | - Carlos M Duarte
- Red Sea Research Center (RSRC) and Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Amelia Henry
- International Rice Research Institute, Rice Breeding Innovations Platform, Los Baños, Laguna 4031, Philippines
| | - S V Krishna Jagadish
- Department of Plant and Soil Science, Texas Tech University, Lubbock, Texas 79410, USA
| | - Jane A Langdale
- Department of Biology, University of Oxford, Oxford, OX1 3RB, UK
| | - Andrew D B Leakey
- Department of Plant Biology, Department of Crop Sciences, and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Illinois 61801, USA
| | - James C Liao
- Institute of Biological Chemistry, Academia Sinica, Taipei 11528, Taiwan
| | - Kuan-Jen Lu
- Institute of Biological Chemistry, Academia Sinica, Taipei 11528, Taiwan
| | - Maureen C McCann
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
| | - John K McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, Colorado 80523, USA
| | - Damaris A Odeny
- The International Crops Research Institute for the Semi-Arid Tropics–Eastern and Southern Africa, Gigiri 39063-00623, Nairobi, Kenya
| | | | - J Damien Platten
- International Rice Research Institute, Rice Breeding Innovations Platform, Los Baños, Laguna 4031, Philippines
| | - Ismail Rabbi
- International Institute of Tropical Agriculture (IITA), PMB 5320 Ibadan, Oyo, Nigeria
| | - Ellen Youngsoo Rim
- Department of Plant Pathology and the Genome Center, University of California, Davis, California 95616, USA
| | - Pamela C Ronald
- Department of Plant Pathology and the Genome Center, University of California, Davis, California 95616, USA
- Innovative Genomics Institute, Berkeley, California 94704, USA
| | - David E Salt
- School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, UK
- Future Food Beacon of Excellence, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Alexandra M Shigenaga
- Department of Plant Pathology and the Genome Center, University of California, Davis, California 95616, USA
| | - Ertao Wang
- National Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Marnin Wolfe
- Auburn University, Dept. of Crop Soil and Environmental Sciences, College of Agriculture, Auburn, Alabama 36849, USA
| | - Xiaowei Zhang
- National Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
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11
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Zhang J, Wang X, Liu J, Zhang D, Lu Y, Zhou Y, Sun L, Hou S, Fan X, Shen S, Zhao J. Multispectral Drone Imagery and SRGAN for Rapid Phenotypic Mapping of Individual Chinese Cabbage Plants. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:0007. [PMID: 37266137 PMCID: PMC10230957 DOI: 10.34133/plantphenomics.0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 11/07/2022] [Indexed: 06/03/2023]
Abstract
The phenotypic parameters of crop plants can be evaluated accurately and quickly using an unmanned aerial vehicle (UAV) equipped with imaging equipment. In this study, hundreds of images of Chinese cabbage (Brassica rapa L. ssp. pekinensis) germplasm resources were collected with a low-cost UAV system and used to estimate cabbage width, length, and relative chlorophyll content (soil plant analysis development [SPAD] value). The super-resolution generative adversarial network (SRGAN) was used to improve the resolution of the original image, and the semantic segmentation network Unity Networking (UNet) was used to process images for the segmentation of each individual Chinese cabbage. Finally, the actual length and width were calculated on the basis of the pixel value of the individual cabbage and the ground sampling distance. The SPAD value of Chinese cabbage was also analyzed on the basis of an RGB image of a single cabbage after background removal. After comparison of various models, the model in which visible images were enhanced with SRGAN showed the best performance. With the validation set and the UNet model, the segmentation accuracy was 94.43%. For Chinese cabbage dimensions, the model was better at estimating length than width. The R2 of the visible-band model with images enhanced using SRGAN was greater than 0.84. For SPAD prediction, the R2 of the model with images enhanced with SRGAN was greater than 0.78. The root mean square errors of the 3 semantic segmentation network models were all less than 2.18. The results showed that the width, length, and SPAD value of Chinese cabbage predicted using UAV imaging were comparable to those obtained from manual measurements in the field. Overall, this research demonstrates not only that UAVs are useful for acquiring quantitative phenotypic data on Chinese cabbage but also that a regression model can provide reliable SPAD predictions. This approach offers a reliable and convenient phenotyping tool for the investigation of Chinese cabbage breeding traits.
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Affiliation(s)
- Jun Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Xinxin Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- Mountain Area Research Institute, Hebei Agricultural University, 071001 Baoding, China
| | - Jingyan Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Dongfang Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Yin Lu
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Yuhong Zhou
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Lei Sun
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Shenglin Hou
- Hebei Academy of Agriculture and Forestry Sciences, 050000 Shijiazhuang, China
| | - Xiaofei Fan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Shuxing Shen
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Jianjun Zhao
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
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12
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Chattha MS, Ali Q, Haroon M, Afzal MJ, Javed T, Hussain S, Mahmood T, Solanki MK, Umar A, Abbas W, Nasar S, Schwartz-Lazaro LM, Zhou L. Enhancement of nitrogen use efficiency through agronomic and molecular based approaches in cotton. FRONTIERS IN PLANT SCIENCE 2022; 13:994306. [PMID: 36237509 PMCID: PMC9552886 DOI: 10.3389/fpls.2022.994306] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/22/2022] [Indexed: 05/22/2023]
Abstract
Cotton is a major fiber crop grown worldwide. Nitrogen (N) is an essential nutrient for cotton production and supports efficient crop production. It is a crucial nutrient that is required more than any other. Nitrogen management is a daunting task for plants; thus, various strategies, individually and collectively, have been adopted to improve its efficacy. The negative environmental impacts of excessive N application on cotton production have become harmful to consumers and growers. The 4R's of nutrient stewardship (right product, right rate, right time, and right place) is a newly developed agronomic practice that provides a solid foundation for achieving nitrogen use efficiency (NUE) in cotton production. Cropping systems are equally crucial for increasing production, profitability, environmental growth protection, and sustainability. This concept incorporates the right fertilizer source at the right rate, time, and place. In addition to agronomic practices, molecular approaches are equally important for improving cotton NUE. This could be achieved by increasing the efficacy of metabolic pathways at the cellular, organ, and structural levels and NUE-regulating enzymes and genes. This is a potential method to improve the role of N transporters in plants, resulting in better utilization and remobilization of N in cotton plants. Therefore, we suggest effective methods for accelerating NUE in cotton. This review aims to provide a detailed overview of agronomic and molecular approaches for improving NUE in cotton production, which benefits both the environment and growers.
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Affiliation(s)
- Muhammad Sohaib Chattha
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
- School of Plant, Environmental and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA, United States
| | - Qurban Ali
- Laboratory of Integrated Management of Crop Diseases and Pests, Department of Plant Pathology, College of Plant Protection, Ministry of Education, Nanjing Agricultural University, Nanjing, China
| | - Muhammad Haroon
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
| | | | - Talha Javed
- College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Sadam Hussain
- Department of Agronomy, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Tahir Mahmood
- Department of Plant Breeding & Genetics, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan
| | - Manoj K. Solanki
- Institute of Biology, Biotechnology and Environmental Protection, Faculty of Natural Sciences, University of Silesia in Katowice, Katowice, Poland
| | - Aisha Umar
- Institute of Botany, University of the Punjab, Lahore, Pakistan
| | - Waseem Abbas
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
| | - Shanza Nasar
- Department of Botany, University of Gujrat Hafiz Hayat Campus, Gujrat, Pakistan
| | - Lauren M. Schwartz-Lazaro
- School of Plant, Environmental and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA, United States
| | - Lei Zhou
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
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13
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Grzybowski MW, Zwiener M, Jin H, Wijewardane NK, Atefi A, Naldrett MJ, Alvarez S, Ge Y, Schnable JC. Variation in morpho-physiological and metabolic responses to low nitrogen stress across the sorghum association panel. BMC PLANT BIOLOGY 2022; 22:433. [PMID: 36076172 PMCID: PMC9461132 DOI: 10.1186/s12870-022-03823-2] [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: 06/24/2022] [Accepted: 09/05/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Access to biologically available nitrogen is a key constraint on plant growth in both natural and agricultural settings. Variation in tolerance to nitrogen deficit stress and productivity in nitrogen limited conditions exists both within and between plant species. However, our understanding of changes in different phenotypes under long term low nitrogen stress and their impact on important agronomic traits, such as yield, is still limited. RESULTS Here we quantified variation in the metabolic, physiological, and morphological responses of a sorghum association panel assembled to represent global genetic diversity to long term, nitrogen deficit stress and the relationship of these responses to grain yield under both conditions. Grain yield exhibits substantial genotype by environment interaction while many other morphological and physiological traits exhibited consistent responses to nitrogen stress across the population. Large scale nontargeted metabolic profiling for a subset of lines in both conditions identified a range of metabolic responses to long term nitrogen deficit stress. Several metabolites were associated with yield under high and low nitrogen conditions. CONCLUSION Our results highlight that grain yield in sorghum, unlike many morpho-physiological traits, exhibits substantial variability of genotype specific responses to long term low severity nitrogen deficit stress. Metabolic response to long term nitrogen stress shown higher proportion of variability explained by genotype specific responses than did morpho-pysiological traits and several metabolites were correlated with yield. This suggest, that it might be possible to build predictive models using metabolite abundance to estimate which sorghum genotypes will exhibit greater or lesser decreases in yield in response to nitrogen deficit, however further research needs to be done to evaluate such model.
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Affiliation(s)
- Marcin W Grzybowski
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA.
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA.
- Department of Plant Molecular Ecophysiology, Institute of Plant Experimental Biology and Biotechnology, Faculty of Biology, University of Warsaw, Warsw, Poland.
| | - Mackenzie Zwiener
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
| | - Hongyu Jin
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
| | - Nuwan K Wijewardane
- Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, USA
| | - Abbas Atefi
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, USA
- California Strawberry Commission, San Luis Obispo, USA
| | - Michael J Naldrett
- Proteomics and Metabolomics Facility, Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, USA
| | - Sophie Alvarez
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA
- Proteomics and Metabolomics Facility, Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, USA
| | - Yufeng Ge
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA
- Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, USA
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, USA.
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, USA.
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14
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Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images. SUSTAINABILITY 2022. [DOI: 10.3390/su14159259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R2 = 0.80 and RMSE = 1.67 g kg−1 of the Xinluzao 45 optimal model, and R2 = 0.42 and RMSE = 3.13 g kg−1 of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved.
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15
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Sun C, Zhou J, Ma Y, Xu Y, Pan B, Zhang Z. A review of remote sensing for potato traits characterization in precision agriculture. FRONTIERS IN PLANT SCIENCE 2022; 13:871859. [PMID: 35923874 PMCID: PMC9339983 DOI: 10.3389/fpls.2022.871859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
Potato is one of the most significant food crops globally due to its essential role in the human diet. The growing demand for potato, coupled with severe environmental losses caused by extensive farming activities, implies the need for better crop protection and management practices. Precision agriculture is being well recognized as the solution as it deals with the management of spatial and temporal variability to improve agricultural returns and reduce environmental impact. As the initial step in precision agriculture, the traditional methods of crop and field characterization require a large input in labor, time, and cost. Recent developments in remote sensing technologies have facilitated the process of monitoring crops and quantifying field variations. Successful applications have been witnessed in the area of precision potato farming. Thus, this review reports the current knowledge on the applications of remote sensing technologies in precision potato trait characterization. We reviewed the commonly used imaging sensors and remote sensing platforms with the comparisons of their strengths and limitations and summarized the main applications of the remote sensing technologies in potato. As a result, this review could update potato agronomists and farmers with the latest approaches and research outcomes, as well as provide a selective list for those who have the intentions to apply remote sensing technologies to characterize potato traits for precision agriculture.
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Affiliation(s)
- Chen Sun
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
- Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Xi’an, China
| | - Jing Zhou
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Yuchi Ma
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Yijia Xu
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Bin Pan
- School of Statistics and Data Science, Nankai University, Tianjin, China
| | - Zhou Zhang
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
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Hussain B, Akpınar BA, Alaux M, Algharib AM, Sehgal D, Ali Z, Aradottir GI, Batley J, Bellec A, Bentley AR, Cagirici HB, Cattivelli L, Choulet F, Cockram J, Desiderio F, Devaux P, Dogramaci M, Dorado G, Dreisigacker S, Edwards D, El-Hassouni K, Eversole K, Fahima T, Figueroa M, Gálvez S, Gill KS, Govta L, Gul A, Hensel G, Hernandez P, Crespo-Herrera LA, Ibrahim A, Kilian B, Korzun V, Krugman T, Li Y, Liu S, Mahmoud AF, Morgounov A, Muslu T, Naseer F, Ordon F, Paux E, Perovic D, Reddy GVP, Reif JC, Reynolds M, Roychowdhury R, Rudd J, Sen TZ, Sukumaran S, Ozdemir BS, Tiwari VK, Ullah N, Unver T, Yazar S, Appels R, Budak H. Capturing Wheat Phenotypes at the Genome Level. FRONTIERS IN PLANT SCIENCE 2022; 13:851079. [PMID: 35860541 PMCID: PMC9289626 DOI: 10.3389/fpls.2022.851079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.
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Affiliation(s)
- Babar Hussain
- Department of Biological Sciences, Middle East Technical University, Ankara, Turkey
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore, Pakistan
| | | | - Michael Alaux
- Université Paris-Saclay, INRAE, URGI, Versailles, France
| | - Ahmed M. Algharib
- Department of Environment and Bio-Agriculture, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt
| | - Deepmala Sehgal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Zulfiqar Ali
- Institute of Plant Breeding and Biotechnology, MNS University of Agriculture, Multan, Pakistan
| | - Gudbjorg I. Aradottir
- Department of Pathology, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Arnaud Bellec
- French Plant Genomic Resource Center, INRAE-CNRGV, Castanet Tolosan, France
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Halise B. Cagirici
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Fred Choulet
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - James Cockram
- The John Bingham Laboratory, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Francesca Desiderio
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Pierre Devaux
- Research & Innovation, Florimond Desprez Group, Cappelle-en-Pévèle, France
| | - Munevver Dogramaci
- USDA, Agricultural Research Service, Edward T. Schafer Agricultural Research Center, Fargo, ND, United States
| | - Gabriel Dorado
- Department of Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain
| | | | - David Edwards
- University of Western Australia, Perth, WA, Australia
| | - Khaoula El-Hassouni
- State Plant Breeding Institute, The University of Hohenheim, Stuttgart, Germany
| | - Kellye Eversole
- International Wheat Genome Sequencing Consortium (IWGSC), Bethesda, MD, United States
| | - Tzion Fahima
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Melania Figueroa
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Canberra, ACT, Australia
| | - Sergio Gálvez
- Department of Languages and Computer Science, ETSI Informática, Campus de Teatinos, Universidad de Málaga, Andalucía Tech, Málaga, Spain
| | - Kulvinder S. Gill
- Department of Crop Science, Washington State University, Pullman, WA, United States
| | - Liubov Govta
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Alvina Gul
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Goetz Hensel
- Center of Plant Genome Engineering, Heinrich-Heine-Universität, Düsseldorf, Germany
- Division of Molecular Biology, Centre of Region Haná for Biotechnological and Agriculture Research, Czech Advanced Technology and Research Institute, Palacký University, Olomouc, Czechia
| | - Pilar Hernandez
- Institute for Sustainable Agriculture (IAS-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | | | - Amir Ibrahim
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | | | | | - Tamar Krugman
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Yinghui Li
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Shuyu Liu
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Amer F. Mahmoud
- Department of Plant Pathology, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Alexey Morgounov
- Food and Agriculture Organization of the United Nations, Riyadh, Saudi Arabia
| | - Tugdem Muslu
- Molecular Biology, Genetics and Bioengineering, Sabanci University, Istanbul, Turkey
| | - Faiza Naseer
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Frank Ordon
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Etienne Paux
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - Dragan Perovic
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Gadi V. P. Reddy
- USDA-Agricultural Research Service, Southern Insect Management Research Unit, Stoneville, MS, United States
| | - Jochen Christoph Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Rajib Roychowdhury
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Jackie Rudd
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Taner Z. Sen
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | | | | | | | - Naimat Ullah
- Institute of Biological Sciences (IBS), Gomal University, D. I. Khan, Pakistan
| | - Turgay Unver
- Ficus Biotechnology, Ostim Teknopark, Ankara, Turkey
| | - Selami Yazar
- General Directorate of Research, Ministry of Agriculture, Ankara, Turkey
| | | | - Hikmet Budak
- Montana BioAgriculture, Inc., Missoula, MT, United States
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17
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Al-Tamimi N, Langan P, Bernád V, Walsh J, Mangina E, Negrão S. Capturing crop adaptation to abiotic stress using image-based technologies. Open Biol 2022; 12:210353. [PMID: 35728624 PMCID: PMC9213114 DOI: 10.1098/rsob.210353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.
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Affiliation(s)
- Nadia Al-Tamimi
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Patrick Langan
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Villő Bernád
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Jason Walsh
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland,School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Eleni Mangina
- School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Sónia Negrão
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
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18
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Spectral Estimation of In Vivo Wheat Chlorophyll a/b Ratio under Contrasting Water Availabilities. REMOTE SENSING 2022. [DOI: 10.3390/rs14112585] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
To meet the ever-growing global population necessities, integrating climate-change-relevant plant traits into breeding programs is required. Developing new tools for fast and accurate estimation of chlorophyll parameters, chlorophyll a (Chl-a) content, chlorophyll b (Chl-b) content, and their ratio (Chl-a/b), can promote breeding programs of wheat with enhanced climate adaptability. Spectral reflectance of leaves is affected by changes in pigment concentration and can be used to estimate chlorophyll parameters. The current study identified and validated the top known spectral indices and developed new vegetation indices (VIs) for Chl-a and Chl-b content estimation and used them to non-destructively estimate Chl-a/b values and compare them to hyperspectral estimations. Three wild emmer introgression lines, with contrasting drought stress responsiveness dynamics, were selected. Well-watered and water-limited irrigation regimes were applied. The wheat leaves were spectrally measured with a handheld spectrometer to acquire their reflectance in the 330 to 790 nm range. Regression models based on calculated VIs as well as all hyperspectral curves were calibrated and validated against chlorophyll extracted values. The developed normalized difference spectral indices (NDSIs) resulted in high accuracy of Chl-a (NDSI415,614) and Chl-b (NDSI406,525) estimation, allowing for indirect non-destructive estimation of Chl-a/b with root mean square error (RMSE) values that could fit 6 to 10 times in the range of the measured values. They also performed similarly to the hyperspectral models. Altogether, we present here a new tool for a non-destructive estimation of Chl-a/b, which can serve as a basis for future breeding efforts of climate-resilient wheat as well as other crops.
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19
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Koh JC, Banerjee BP, Spangenberg G, Kant S. Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping. THE NEW PHYTOLOGIST 2022; 233:2659-2670. [PMID: 34997968 PMCID: PMC9305872 DOI: 10.1111/nph.17947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/16/2021] [Indexed: 05/14/2023]
Abstract
Hyperspectral vegetation indices (VIs) are widely deployed in agriculture remote sensing and plant phenotyping to estimate plant biophysical and biochemical traits. However, existing VIs consist mainly of simple two-band indices that limit the net performance and often do not generalise well for traits other than those for which they were originally designed. We present an automated hyperspectral vegetation index (AutoVI) system for the rapid generation of novel two- to six-band trait-specific indices in a streamlined process covering model selection, optimisation and evaluation, driven by the tree parzen estimator algorithm. Its performance was tested in generating novel indices to estimate chlorophyll and sugar contents in wheat. Results showed that AutoVI can rapidly generate complex novel VIs (at least a four-band index) that correlated strongly (R2 > 0.8) with measured chlorophyll and sugar contents in wheat. Automated hyperspectral vegetation index-derived indices were used as features in simple and stepwise multiple linear regressions for chlorophyll and sugar content estimation, and outperformed the results achieved with the existing 47 VIs and those provided using partial least squares regression. The AutoVI system can deliver novel trait-specific VIs readily adoptable to high-throughput plant phenotyping platforms and should appeal to plant scientists and breeders. A graphical user interface for the AutoVI is provided here.
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Affiliation(s)
- Joshua C.O. Koh
- Agriculture VictoriaGrains Innovation Park110 Natimuk RdHorshamVic.3400Australia
| | - Bikram P. Banerjee
- Agriculture VictoriaGrains Innovation Park110 Natimuk RdHorshamVic.3400Australia
| | - German Spangenberg
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVic.3083Australia
- School of Applied Systems BiologyLa Trobe UniversityBundooraVic.3083Australia
| | - Surya Kant
- Agriculture VictoriaGrains Innovation Park110 Natimuk RdHorshamVic.3400Australia
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVic.3083Australia
- School of Applied Systems BiologyLa Trobe UniversityBundooraVic.3083Australia
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20
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Nazari L, Khazaei A, Ropelewska E. Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning. Cereal Chem 2022. [DOI: 10.1002/cche.10542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Leyla Nazari
- Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO) Shiraz Iran
| | - Azim Khazaei
- Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO) Karaj Iran
| | - Ewa Ropelewska
- Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research Konstytucji 3 Maja 1/3, 96‐100 Skierniewice Poland
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21
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High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance. PLoS One 2021; 16:e0254908. [PMID: 34297757 PMCID: PMC8301646 DOI: 10.1371/journal.pone.0254908] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 07/06/2021] [Indexed: 01/11/2023] Open
Abstract
Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria’s high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.
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22
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Kohzuma K, Tamaki M, Hikosaka K. Corrected photochemical reflectance index (PRI) is an effective tool for detecting environmental stresses in agricultural crops under light conditions. JOURNAL OF PLANT RESEARCH 2021; 134:683-694. [PMID: 34081252 DOI: 10.1007/s10265-021-01316-1] [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: 11/09/2020] [Accepted: 05/25/2021] [Indexed: 05/25/2023]
Abstract
High-throughput detection of plant environmental stresses is required for minimizing the reduction in crop yield. Environmental stresses in plants have primarily been validated by the measurements of photosynthesis with gas exchange and chlorophyll fluorescence, which involve complicated procedures. Remote sensing technologies that monitor leaf reflectance in intact plants enable real-time visualization of plant responses to environmental fluctuations. The photochemical reflectance index (PRI), one of the vegetation indices of spectral leaf reflectance, is related to changes in xanthophyll pigment composition. Xanthophyll dynamics are strongly correlated with plant stress because they contribute to the thermal dissipation of excess energy. However, an accurate assessment of plant stress based on PRI requires correction by baseline PRI (PRIo) in the dark, which is difficult to obtain in the field. In this study, we propose a method to correct the PRI using NPQT, which can be measured under light. By this method, we evaluated responses of excess light energy stress under drought in wild watermelon (Citrullus lanatus L.), a xerophyte. Demonstration on the farm, the stress behaviors were observed in maize (Zea mays L.). Furthermore, the stress status of plants and their recovery following re-watering were captured as visual information. These results suggest that the PRI is an excellent indicator of environmental stress and recovery in plants and could be used as a high-throughput stress detection tool in agriculture.
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Affiliation(s)
- Kaori Kohzuma
- Graduate School of Life Sciences, Tohoku University, Sendai, Miyagi, 980-8578, Japan.
| | - Maro Tamaki
- Okinawa Prefectural Agricultural Research Center, Itoman, Okinawa, 901-0336, Japan
| | - Kouki Hikosaka
- Graduate School of Life Sciences, Tohoku University, Sendai, Miyagi, 980-8578, Japan
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23
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Thoday-Kennedy E, Joshi S, Daetwyler HD, Hayden M, Hudson D, Spangenberg G, Kant S. Digital Phenotyping to Delineate Salinity Response in Safflower Genotypes. FRONTIERS IN PLANT SCIENCE 2021; 12:662498. [PMID: 34220887 PMCID: PMC8242588 DOI: 10.3389/fpls.2021.662498] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/24/2021] [Indexed: 05/27/2023]
Abstract
Salinity is a major contributing factor to the degradation of arable land, and reductions in crop growth and yield. To overcome these limitations, the breeding of crop varieties with improved salt tolerance is needed. This requires effective and high-throughput phenotyping to optimize germplasm enhancement. Safflower (Carthamus tinctorius L.), is an underappreciated but highly versatile oilseed crop, capable of growing in saline and arid environments. To develop an effective and rapid phenotyping protocol to differentiate salt responses in safflower genotypes, experiments were conducted in the automated imaging facility at Plant Phenomics Victoria, Horsham, focussing on digital phenotyping at early vegetative growth. The initial experiment, at 0, 125, 250, and 350 mM sodium chloride (NaCl), showed that 250 mM NaCl was optimum to differentiate salt sensitive and tolerant genotypes. Phenotyping of a diverse set of 200 safflower genotypes using the developed protocol defined four classes of salt tolerance or sensitivity, based on biomass and ion accumulation. Salt tolerance in safflower was dependent on the exclusion of Na+ from shoot tissue and the maintenance of K+ uptake. Salinity response identified in glasshouse experiments showed some consistency with the performance of representatively selected genotypes tested under sodic field conditions. Overall, our results suggest that digital phenotyping can be an effective high-throughput approach in identifying candidate genotypes for salt tolerance in safflower.
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Affiliation(s)
| | - Sameer Joshi
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC, Australia
| | - Hans D. Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Matthew Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - David Hudson
- GO Resources Pty Ltd., Brunswick, VIC, Australia
| | - German Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC, Australia
- Centre for Agricultural Innovation, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Melbourne, VIC, Australia
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24
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Chaudhary S, Kalkal M. Rice Transcriptome Analysis Reveals Nitrogen Starvation Modulates Differential Alternative Splicing and Transcript Usage in Various Metabolism-Related Genes. Life (Basel) 2021; 11:285. [PMID: 33801769 PMCID: PMC8066416 DOI: 10.3390/life11040285] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 12/13/2022] Open
Abstract
Nitrogen (N) is crucial for plant growth and development; however, excessive use of N fertilizers cause many problems including environmental damage, degradation of soil fertility, and high cost to the farmers. Therefore, immediate implementation is required to develop N efficient crop varieties. Rice being low nitrogen use efficiency (NUE) and a high demand staple food across the world has become a favorite crop to study the NUE trait. In the current study, we used the publicly available transcriptome data generated from the root and shoot tissues of two rice genotypes IR-64 and Nagina-22 (N-22) under optimum N supply (N+) and chronic N-starvation (N-). A stringent pipeline was applied to detect differentially expressed genes (DEGs), alternatively spliced (DAS) genes, differentially expressed transcripts (DETs) and differential transcript usage (DTU) transcripts in both the varieties and tissues under N+ and N- conditions. The DAS genes and DTU transcripts identified in the study were found to be involved in several metabolic and biosynthesis processes. We suggest alternative splicing (AS) plays an important role in fine-tuning the regulation of metabolic pathways related genes in genotype, tissue, and condition-dependent manner. The current study will help in understanding the transcriptional dynamics of NUE traits in the future.
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Affiliation(s)
- Saurabh Chaudhary
- Cardiff School of Biosciences, Cardiff University, Cardiff CF10 3AX, UK
| | - Meenu Kalkal
- Parasite-Host Biology, National Institute of Malaria Research, Dwarka, New Delhi 110077, India;
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25
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Kim HS, Yoo JH, Park SH, Kim JS, Chung Y, Kim JH, Kim HS. Measurement of Environmentally Influenced Variations in Anthocyanin Accumulations in Brassica rapa subsp. Chinensis (Bok Choy) Using Hyperspectral Imaging. FRONTIERS IN PLANT SCIENCE 2021; 12:693854. [PMID: 34489997 PMCID: PMC8416915 DOI: 10.3389/fpls.2021.693854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/22/2021] [Indexed: 05/02/2023]
Abstract
Dietary supplements of anthocyanin-rich vegetables have been known to increase potential health benefits for humans. The optimization of environmental conditions to increase the level of anthocyanin accumulations in vegetables during the cultivation periods is particularly important in terms of the improvement of agricultural values in the indoor farm using artificial light and climate controlling systems. This study reports on the measurement of variations in anthocyanin accumulations in leaf tissues of four different cultivars in Brassica rapa var. chinensis (bok choy) grown under the different environmental conditions of the indoor farm using hyperspectral imaging. Anthocyanin accumulations estimated by hyperspectral imaging were compared with the measured anthocyanin accumulation obtained by destructive analysis. Between hyperspectral imaging and destructive analysis values, no significant differences in anthocyanin accumulation were observed across four bok choy cultivars grown under the anthocyanin stimulation environmental condition, whereas the estimated anthocyanin accumulations displayed cultivar-dependent significant differences, suggesting that hyperspectral imaging can be employed to measure variations in anthocyanin accumulations of different bok choy cultivars. Increased accumulation of anthocyanin under the stimulation condition for anthocyanin accumulation was observed in "purple magic" and "red stem" by both hyperspectral imaging and destructive analysis. In the different growth stages, no significant differences in anthocyanin accumulation were found in each cultivar by both hyperspectral imaging and destructive analysis. These results suggest that hyperspectral imaging can provide comparable analytic capability with destructive analysis to measure variations in anthocyanin accumulation that occurred under the different light and temperature conditions of the indoor farm. Leaf image analysis measuring the percentage of purple color area in the total leaf area displayed successful classification of anthocyanin accumulation in four bok choy cultivars in comparison to hyperspectral imaging and destructive analysis, but it also showed limitation to reflect the level of color saturation caused by anthocyanin accumulation under different environmental conditions in "red stem," "white stem," and "green stem." Finally, our hyperspectral imaging system was modified to be applied onto the high-throughput plant phenotyping system, and its test to analyze the variation of anthocyanin accumulation in four cultivars showed comparable results with the result of the destructive analysis.
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Affiliation(s)
- Hyo-suk Kim
- Sensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul, South Korea
- Department of Electronics and Communications Engineering, Kwangwoon University, Seoul, South Korea
| | - Ji Hye Yoo
- Smart Farm Convergence Research Center, Korea Institute of Science and Technology (KIST), Gangneung, South Korea
| | - Soo Hyun Park
- Smart Farm Convergence Research Center, Korea Institute of Science and Technology (KIST), Gangneung, South Korea
| | - Jun-Sik Kim
- Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology (KIST), Seoul, South Korea
| | - Youngchul Chung
- Department of Electronics and Communications Engineering, Kwangwoon University, Seoul, South Korea
| | - Jae Hun Kim
- Sensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul, South Korea
- Jae Hun Kim
| | - Hyoung Seok Kim
- Smart Farm Convergence Research Center, Korea Institute of Science and Technology (KIST), Gangneung, South Korea
- *Correspondence: Hyoung Seok Kim
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26
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Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives. FORESTS 2020. [DOI: 10.3390/f11111190] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The breeding of forest trees is only a few decades old, and is a much more complicated, longer, and expensive endeavor than the breeding of agricultural crops. One breeding cycle for forest trees can take 20–30 years. Recent advances in genomics and molecular biology have revolutionized traditional plant breeding based on visual phenotype assessment: the development of different types of molecular markers has made genotype selection possible. Marker-assisted breeding can significantly accelerate the breeding process, but this method has not been shown to be effective for selection of complex traits on forest trees. This new method of genomic selection is based on the analysis of all effects of quantitative trait loci (QTLs) using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual. This approach is expected to be much more efficient for forest tree improvement than traditional breeding. Here, we review the current state of the art in the application of genomic selection in forest tree breeding and discuss different methods of genotyping and phenotyping. We also compare the accuracies of genomic prediction models and highlight the importance of a prior cost-benefit analysis before implementing genomic selection. Perspectives for the further development of this approach in forest breeding are also discussed: expanding the range of species and the list of valuable traits, the application of high-throughput phenotyping methods, and the possibility of using epigenetic variance to improve of forest trees.
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27
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Xu G, Takahashi H. Improving nitrogen use efficiency: from cells to plant systems. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:4359-4364. [PMID: 32710784 DOI: 10.1093/jxb/eraa309] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Affiliation(s)
- Guohua Xu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- China MOA Key Laboratory of Plant Nutrition and Fertilization in Lower-Middle Reaches of the Yangtze River, Nanjing, China
| | - Hideki Takahashi
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, USA
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