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Moshelion M, Dietz KJ, Dodd IC, Muller B, Lunn JE. Guidelines for designing and interpreting drought experiments in controlled conditions. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:4671-4679. [PMID: 38957989 PMCID: PMC11350075 DOI: 10.1093/jxb/erae292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Accepted: 07/02/2024] [Indexed: 07/04/2024]
Affiliation(s)
- Menachem Moshelion
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel
| | - Karl-Josef Dietz
- Department of Biochemistry and Physiology, Faculty of Biology, Bielefeld University, Universitätsstraße 25, D-33615 Bielefeld, Germany
| | - Ian C Dodd
- Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster LA1 4YQ, UK
| | - Bertrand Muller
- INRAE-LEPSE, Institut Agro, Université Montpellier, UMR 759 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France
| | - John E Lunn
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany
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2
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Larue F, Rouan L, Pot D, Rami JF, Luquet D, Beurier G. Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits. FRONTIERS IN PLANT SCIENCE 2024; 15:1393965. [PMID: 39139722 PMCID: PMC11319263 DOI: 10.3389/fpls.2024.1393965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 07/04/2024] [Indexed: 08/15/2024]
Abstract
Introduction Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. Methods In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. Results The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. Discussion These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.
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Affiliation(s)
- Florian Larue
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Lauriane Rouan
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - David Pot
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Jean-François Rami
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Delphine Luquet
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Grégory Beurier
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
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3
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Ali B, Huguenin-Bizot B, Laurent M, Chaumont F, Maistriaux LC, Nicolas S, Duborjal H, Welcker C, Tardieu F, Mary-Huard T, Moreau L, Charcosset A, Runcie D, Rincent R. High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:175. [PMID: 38958724 DOI: 10.1007/s00122-024-04679-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 06/15/2024] [Indexed: 07/04/2024]
Abstract
KEY MESSAGE Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.
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Affiliation(s)
- Baber Ali
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Bertrand Huguenin-Bizot
- Laboratoire Reproduction Et Développement Des Plantes, CNRS, ENS de Lyon-46, Allée d'Italie, 69364, Lyon, France
| | - Maxime Laurent
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-La-Neuve, Belgium
| | - François Chaumont
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-La-Neuve, Belgium
| | - Laurie C Maistriaux
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-La-Neuve, Belgium
| | - Stéphane Nicolas
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Hervé Duborjal
- Limagrain, Limagrain Fields Seeds, Research Centre, 63720, Chappes, France
| | | | | | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Laurence Moreau
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Alain Charcosset
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Daniel Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, USA
| | - Renaud Rincent
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France.
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4
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Wilhelm de Almeida L, Pastenes C, Ojeda H, Torregrosa L, Pellegrino A. Water deficit differentially modulates leaf photosynthesis and transpiration of fungus-tolerant Muscadinia x Vitis hybrids. FRONTIERS IN PLANT SCIENCE 2024; 15:1405343. [PMID: 38817935 PMCID: PMC11137165 DOI: 10.3389/fpls.2024.1405343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 04/30/2024] [Indexed: 06/01/2024]
Abstract
Screening for drought performance among novel fungi-tolerant grapevine genotypes is a key point to consider in semiarid regions where water scarcity is a common problem during fruit ripening period. It is therefore important to evaluate the genotypes' responses at the level of carbon metabolism and water demand, under water deficit conditions. This study aimed to characterize leaf and plant water use efficiency (respectively named WUEi and WUEpl) of novel INRAE fungi-tolerant genotypes (including LowSugarBerry (LSB) genotypes), under mild and high-water deficit (WD) and to decipher the photosynthetic parameters leading to higher WUEi. For this purpose, experiments were conducted on potted plants during one season using a phenotyping platform. Two stabilized soil moisture capacity (SMC) conditions, corresponding to mild (SMC 0.6) and high (SMC 0.3) WD, were imposed from the onset of berry ripening until the physiological ripeness stage, which was defined as the point at which fruits reach their maximum solutes and water content. At the whole plant level, all genotypes increased WUEpl under high WD. The highest WUEpl was reached for 3176N, which displayed both a high rate of non-structural carbon accumulation in fruits due to high fruit-to-leaf ratio and low plant transpiration because of low total leaf area. However, when normalizing the fruit-to-leaf ratio among the genotypes, G14 reached the highest normalized WUEpl_n under high WD. At the leaf level, WUEi also increased under high WD, with the highest value attained for G14 and 3176N and the lowest value for Syrah. The higher WUEi values for all genotypes compared to Syrah were associated to higher levels of photosynthesis and changes in light-harvesting efficiency parameters (ΦCO2, qP and qN), while no clear trend was apparent when considering the photosynthetic biochemical parameters (Vcmax, Jmax). Finally, a positive correlation between leaf and plant WUE was observed regardless of genotypes. This study allowed us to classify grapevine genotypes based on their grapes primary metabolite accumulation and water consumption during the critical sugar-loading period. Additionally, the study highlighted the potential drought adaptation mechanism of the LSB genotypes.
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Affiliation(s)
- Luciana Wilhelm de Almeida
- UE Pech Rouge, Univ Montpellier, INRAE, Gruissan, France
- UMR LEPSE, Univ Montpellier, INRAE, CIRAD, Institut Agro Montpellier, Montpellier, France
| | - Claudio Pastenes
- Departamento de Producción Agrícola, Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago, Chile
| | - Hernán Ojeda
- UE Pech Rouge, Univ Montpellier, INRAE, Gruissan, France
| | - Laurent Torregrosa
- UE Pech Rouge, Univ Montpellier, INRAE, Gruissan, France
- UMR LEPSE, Univ Montpellier, INRAE, CIRAD, Institut Agro Montpellier, Montpellier, France
| | - Anne Pellegrino
- UMR LEPSE, Univ Montpellier, INRAE, CIRAD, Institut Agro Montpellier, Montpellier, France
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5
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Cagnola JI, D'Andrea KE, Rotili DH, Mercau JL, Ploschuk EL, Maddonni GA, Otegui ME, Casal JJ. Eco-physiology of maize crops under combined stresses. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:1856-1872. [PMID: 38113327 DOI: 10.1111/tpj.16595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/29/2023] [Accepted: 12/06/2023] [Indexed: 12/21/2023]
Abstract
The yield of maize (Zea mays L.) crops depends on their ability to intercept sunlight throughout the growing cycle, transform this energy into biomass and allocate it to the kernels. Abiotic stresses affect these eco-physiological determinants, reducing crop grain yield below the potential of each environment. Here we analyse the impact of combined abiotic stresses, such as water restriction and nitrogen deficiency or water restriction and elevated temperatures. Crop yield depends on the product of kernel yield per plant and the number of plants per unit soil area, but increasing plant population density imposes a crowding stress that reduces yield per plant, even within the range that maximises crop yield per unit soil area. Therefore, we also analyse the impact of abiotic stresses under different plant densities. We show that the magnitude of the detrimental effects of two combined stresses on field-grown plants can be lower, similar or higher than the sum of the individual stresses. These patterns depend on the timing and intensity of each one of the combined stresses and on the effects of one of the stresses on the status of the resource whose limitation causes the other. The analysis of the eco-physiological determinants of crop yield is useful to guide and prioritise the rapidly progressing studies aimed at understanding the molecular mechanisms underlying plant responses to combined stresses.
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Affiliation(s)
- Juan I Cagnola
- Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA), Facultad de Agronomía, Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Agronomía, Cátedra de Cultivos Industriales, Av. San Martín 4453, C1417DSE, Ciudad Autónoma de Buenos Aires, Argentina
| | - Karina E D'Andrea
- Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA), Facultad de Agronomía, Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Agronomía, Cátedra de Cerealicultura, Av. San Martín 4453, C1417DSE, Ciudad Autónoma de Buenos Aires, Argentina
| | - Diego H Rotili
- Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA), Facultad de Agronomía, Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Agronomía, Cátedra de Cerealicultura, Av. San Martín 4453, C1417DSE, Ciudad Autónoma de Buenos Aires, Argentina
| | - Jorge L Mercau
- INTA, Agencia de Extensión San Luis, San Luis, Argentina
| | - Edmundo L Ploschuk
- Universidad de Buenos Aires, Facultad de Agronomía, Cátedra de Cultivos Industriales, Av. San Martín 4453, C1417DSE, Ciudad Autónoma de Buenos Aires, Argentina
| | - Gustavo A Maddonni
- Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA), Facultad de Agronomía, Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Agronomía, Cátedra de Cerealicultura, Av. San Martín 4453, C1417DSE, Ciudad Autónoma de Buenos Aires, Argentina
| | - María E Otegui
- CONICET at INTA, Centro Regional Buenos Aires Norte, Estación Experimental INTA Pergamino, Pergamino, Argentina
- Universidad de Buenos Aires, Facultad de Agronomía, Cátedra de Producción Vegetal, Av. San Martín 4453, C1417DSE, Ciudad Autónoma de Buenos Aires, Argentina
| | - Jorge J Casal
- Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA), Facultad de Agronomía, Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Agronomía, Cátedra de Fisiología Vegetal, Av. San Martín 4453, C1417DSE, Ciudad Autónoma de Buenos Aires, Argentina
- Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, Argentina
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Moroyoqui‐Parra MA, Molero G, Reynolds MP, Gaju O, Murchie EH, Foulkes MJ. Interaction of planting system with radiation-use efficiency in wheat lines. CROP SCIENCE 2024; 64:314-332. [PMID: 38516200 PMCID: PMC10952436 DOI: 10.1002/csc2.21115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 09/22/2023] [Indexed: 03/23/2024]
Abstract
Radiation-use efficiency (RUE) is an important trait for raising biomass and yield potential in plant breeding. However, the effect of the planting system (PS) on genetic variation in RUE has not been previously investigated. Our objectives were to quantify genetic variation in RUE, biomass and grain yield in raised-bed and flat-basin planting systems, and associations with canopy-architecture traits (flag-leaf angle and curvature). Twelve spring wheat (Triticum aestivum L.) cultivars were evaluated under irrigated conditions for 3 years in North West Mexico using raised-bed and flat-basin planting systems. Canopy architecture traits were measured at booting and anthesis + 7 days. Grain yield (10.6%), biomass (7.6%), and pre-grain-filling RUE (9.7%) were higher in raised beds than flat basins, while a significant planting system × genotype interaction was found for grain yield. Genetic variation in pre-grain-filling RUE was associated with biomass and grain yield in beds and basins. In flat basins, higher pre-grain-filling RUE was correlated with a more upright flag-leaf angle but not in raised beds. In raised beds, cultivars with less upright flag-leaf angle had greater fractional light interception pre-anthesis. Taller semi-dwarf cultivars intercepted relatively more radiation in the beds than the flats before anthesis, consistent with the taller cultivars showing relatively greater increases in yield in beds compared to flats. Our results indicated that the evaluation of genotypes for RUE and biomass in wheat breeding should take into account planting systems to capture genotype × PS effects. In addition, the results demonstrate how flag-leaf angle has a different effect depending on the planting system.
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Affiliation(s)
- Marcela A. Moroyoqui‐Parra
- Division of Plant and Crop Science, School of BiosciencesUniversity of NottinghamLeicestershireUK
- Global Wheat ProgramInternational Maize and Wheat Improvement Center (CIMMYT)TexcocoMexico
| | - Gemma Molero
- Global Wheat ProgramInternational Maize and Wheat Improvement Center (CIMMYT)TexcocoMexico
- KWS Momont RechercheMons‐en‐PeveleFrance
| | - Matthew P. Reynolds
- Global Wheat ProgramInternational Maize and Wheat Improvement Center (CIMMYT)TexcocoMexico
| | - Oorbessy Gaju
- Lincoln Institute for Agri‐Food and TechnologyUniversity of LincolnLincolnUK
| | - Erik H. Murchie
- Division of Plant and Crop Science, School of BiosciencesUniversity of NottinghamLeicestershireUK
| | - Michael John Foulkes
- Division of Plant and Crop Science, School of BiosciencesUniversity of NottinghamLeicestershireUK
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7
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Daviet B, Fournier C, Cabrera-Bosquet L, Simonneau T, Cafier M, Romieu C. Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images. PLANT METHODS 2023; 19:146. [PMID: 38098093 PMCID: PMC10720176 DOI: 10.1186/s13007-023-01125-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. RESULTS First, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. CONCLUSIONS We successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images acquired in laboratory conditions. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one cultivar, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.
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Affiliation(s)
- Benoit Daviet
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | | | - Maxence Cafier
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Charles Romieu
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
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8
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Djabali Y, Rincent R, Martin ML, Blein-Nicolas M. Plasticity QTLs specifically contribute to the genotype × water availability interaction in maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:228. [PMID: 37855950 DOI: 10.1007/s00122-023-04458-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/31/2023] [Indexed: 10/20/2023]
Abstract
KEY MESSAGE Multi-trial genome wide association study of plasticity indices allow to detect QTLs specifically involved in the genotype x water availability interaction. Concerns regarding high maize yield losses due to increasing occurrences of drought events are growing, and breeders are still looking for molecular markers for drought tolerance. However, the genetic determinism of traits in response to drought is highly complex and identification of causal regions is a tremendous task. Here, we exploit the phenotypic data obtained from four trials carried out on a phenotyping platform, where a diversity panel of 254 maize hybrids was grown under well-watered and water deficit conditions, to investigate the genetic bases of the drought response in maize. To dissociate drought effect from other environmental factors, we performed multi-trial genome-wide association study on well-watered and water deficit phenotypic means, and on phenotypic plasticity indices computed from measurements made for six ecophysiological traits. We identify 102 QTLs and 40 plasticity QTLs. Most of them were new compared to those obtained from a previous study on the same dataset. Our results show that plasticity QTLs cover genetic regions not identified by QTLs. Furthermore, for all ecophysiological traits, except one, plasticity QTLs are specifically involved in the genotype by water availability interaction, for which they explain between 60 and 100% of the variance. Altogether, QTLs and plasticity QTLs captured more than 75% of the genotype by water availability interaction variance, and allowed to find new genetic regions. Overall, our results demonstrate the importance of considering phenotypic plasticity to decipher the genetic architecture of trait response to stress.
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Affiliation(s)
- Yacine Djabali
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190, Gif-sur-Yvette, France
- Université de Paris Cité, Institute of Plant Sciences Paris-Saclay (IPS2), 91190, Gif-sur-Yvette, France
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Marie-Laure Martin
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190, Gif-sur-Yvette, France.
- Université de Paris Cité, Institute of Plant Sciences Paris-Saclay (IPS2), 91190, Gif-sur-Yvette, France.
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France.
| | - Mélisande Blein-Nicolas
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190, Gif-Sur-Yvette, France.
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Bouidghaghen J, Moreau L, Beauchêne K, Chapuis R, Mangel N, Cabrera-Bosquet L, Welcker C, Bogard M, Tardieu F. Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field. Nat Commun 2023; 14:6603. [PMID: 37857601 PMCID: PMC10587076 DOI: 10.1038/s41467-023-42298-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023] Open
Abstract
Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.
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Affiliation(s)
- Jugurta Bouidghaghen
- LEPSE, Univ Montpellier, INRAE, Montpellier, France
- ARVALIS, Chemin de la côte vieille, Baziège, France
| | - Laurence Moreau
- GQE-Le Moulon, INRAE, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Katia Beauchêne
- ARVALIS, 45 Voie Romaine, Ouzouer-Le-Marché, Beauce La Romaine, France
| | | | - Nathalie Mangel
- ARVALIS, Station de recherche et d'expérimentation, Boigneville, France
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10
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Van Laere J, Merckx R, Hood-Nowotny R, Dercon G. Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates. FRONTIERS IN PLANT SCIENCE 2023; 14:1222558. [PMID: 37900736 PMCID: PMC10611503 DOI: 10.3389/fpls.2023.1222558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 09/04/2023] [Indexed: 10/31/2023]
Abstract
Cassava (Manihot esculenta Crantz) is an important root crop, which despite its drought tolerance suffers considerable yield losses under water deficit. One strategy to increase crop yields under water deficit is improving the crop's transpiration efficiency, which could be achieved by variety selection and potassium application. We assessed carbon isotope composition in bulk leaf material and extracted carbohydrates (soluble sugar, starch, and cellulose) of selected leaves one month after inducing water deficit to estimate transpiration efficiency and storage root biomass under varying conditions in a greenhouse experiment. A local and improved variety were grown in sand, supplied with nutrient solution with two potassium levels (1.44 vs. 0.04 mM K+) and were subjected to water deficit five months after planting. Potassium application and selection of the improved variety both increased transpiration efficiency of the roots with 58% and 85% respectively. Only in the improved variety were 13C ratios affected by potassium application (up to - 1.8‰ in δ13C of soluble sugar) and water deficit (up to + 0.6‰ in δ13C of starch and soluble sugar). These data revealed a shift in substrate away from transitory starch for cellulose synthesis in young leaves of the improved variety under potassium deficit. Bulk δ13C of leaves that had fully developed prior to water deficit were the best proxies for storage root biomass (r = - 0.62, r = - 0.70) and transpiration efficiency (r = - 0.68, r = - 0.58) for the local and improved variety respectively, making laborious extractions redundant. Results obtained from the youngest fully developed leaf, commonly used as a diagnostic leaf, were complicated by remobilized assimilates in the improved variety, making them less suitable for carbon isotope analysis. This study highlights the potential of carbon isotope composition to assess transpiration efficiency and yield, depending on the chosen sampling strategy as well as to unravel carbon allocation processes.
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Affiliation(s)
- Jonas Van Laere
- Soil and Water Management & Crop Nutrition Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
- Division of Soil and Water Management, Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
- Institute of Soil Research, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Roel Merckx
- Division of Soil and Water Management, Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
| | - Rebecca Hood-Nowotny
- Institute of Soil Research, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Gerd Dercon
- Soil and Water Management & Crop Nutrition Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
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11
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Zhang P, Huang J, Ma Y, Wang X, Kang M, Song Y. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0091. [PMID: 37780969 PMCID: PMC10538623 DOI: 10.34133/plantphenomics.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023]
Abstract
Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.
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Affiliation(s)
- Pengpeng Zhang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Jingyao Huang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100094, China
| | - Xiujuan Wang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Mengzhen Kang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
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12
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Harandi N, Vandenberghe B, Vankerschaver J, Depuydt S, Van Messem A. How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques. PLANT METHODS 2023; 19:60. [PMID: 37353846 DOI: 10.1186/s13007-023-01031-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/19/2023] [Indexed: 06/25/2023]
Abstract
Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.
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Affiliation(s)
- Negin Harandi
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | | | - Joris Vankerschaver
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | - Stephen Depuydt
- Erasmus Applied University of Sciences and Arts, Campus Kaai, Nijverheidskaai 170, Anderlecht, Belgium
| | - Arnout Van Messem
- Department of Mathematics, Université de Liège, Allée de la Découverte 12, Liège, Belgium.
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13
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Ye D, Wu L, Li X, Atoba TO, Wu W, Weng H. A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping. PLANTS (BASEL, SWITZERLAND) 2023; 12:1698. [PMID: 37111921 PMCID: PMC10146287 DOI: 10.3390/plants12081698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/08/2023] [Accepted: 04/16/2023] [Indexed: 06/19/2023]
Abstract
Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.
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Affiliation(s)
- Dapeng Ye
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Libin Wu
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Xiaobin Li
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Tolulope Opeyemi Atoba
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Wenhao Wu
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Haiyong Weng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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14
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Field‐based robotic leaf angle detection and characterization of maize plants using stereo vision and deep convolutional neural networks. J FIELD ROBOT 2023. [DOI: 10.1002/rob.22166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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15
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Daviet B, Fernandez R, Cabrera-Bosquet L, Pradal C, Fournier C. PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time. PLANT METHODS 2022; 18:130. [PMID: 36482291 PMCID: PMC9730636 DOI: 10.1186/s13007-022-00961-4] [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: 07/20/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
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Affiliation(s)
- Benoit Daviet
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Romain Fernandez
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France
| | | | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France.
- Inria & LIRMM, CNRS, Univ Montpellier, Montpellier, France.
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16
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Wild Wheat Rhizosphere-Associated Plant Growth-Promoting Bacteria Exudates: Effect on Root Development in Modern Wheat and Composition. Int J Mol Sci 2022; 23:ijms232315248. [PMID: 36499572 PMCID: PMC9740669 DOI: 10.3390/ijms232315248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/15/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
Diazotrophic bacteria isolated from the rhizosphere of a wild wheat ancestor, grown from its refuge area in the Fertile Crescent, were found to be efficient Plant Growth-Promoting Rhizobacteria (PGPR), upon interaction with an elite wheat cultivar. In nitrogen-starved plants, they increased the amount of nitrogen in the seed crop (per plant) by about twofold. A bacterial growth medium was developed to investigate the effects of bacterial exudates on root development in the elite cultivar, and to analyze the exo-metabolomes and exo-proteomes. Altered root development was observed, with distinct responses depending on the strain, for instance, with respect to root hair development. A first conclusion from these results is that the ability of wheat to establish effective beneficial interactions with PGPRs does not appear to have undergone systematic deep reprogramming during domestication. Exo-metabolome analysis revealed a complex set of secondary metabolites, including nutrient ion chelators, cyclopeptides that could act as phytohormone mimetics, and quorum sensing molecules having inter-kingdom signaling properties. The exo-proteome-comprised strain-specific enzymes, and structural proteins belonging to outer-membrane vesicles, are likely to sequester metabolites in their lumen. Thus, the methodological processes we have developed to collect and analyze bacterial exudates have revealed that PGPRs constitutively exude a highly complex set of metabolites; this is likely to allow numerous mechanisms to simultaneously contribute to plant growth promotion, and thereby to also broaden the spectra of plant genotypes (species and accessions/cultivars) with which beneficial interactions can occur.
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17
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Li Y, Tao F, Hao Y, Tong J, Xiao Y, He Z, Reynolds M. Wheat traits and the associated loci conferring radiation use efficiency. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 112:565-582. [PMID: 36004546 DOI: 10.1111/tpj.15954] [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: 05/02/2022] [Revised: 08/16/2022] [Accepted: 08/20/2022] [Indexed: 06/15/2023]
Abstract
Wheat (Triticum aestivum L.) radiation use efficiency (RUE) must be raised through crop breeding to further increase the yield potential, as the harvest index is now close to its theoretical limit. Field experiments including 209 wheat cultivars which have been widely cultivated in China since the 1940s were conducted in two growing seasons (2018-2019 and 2019-2020) to evaluate the variations of phenological, physiological, plant architectural, and yield-related traits and their contributions to RUE and to identify limiting factors for wheat yield potential. The average annual genetic gain in grain yield was 0.60% (or 45.32 kg ha-1 year-1 ; R2 = 0.44, P < 0.01), mainly attributed to the gain in RUE (r = 0.85, P < 0.01). The net photosynthetic rates were positively and closely correlated with grain RUE and grain yield, suggesting source as a limiting factor to future yield gains. Thirty-four cultivars were identified, exhibiting not only high RUE, but also traits contributing to high RUE and 11 other critical traits - of known genetic basis - as potential parents for breeding to improve yield and RUE. Our findings reveal wheat traits and the associated loci conferring RUE, which are valuable for facilitating marker-assisted breeding to improve wheat RUE and yield potential.
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Affiliation(s)
- Yibo Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Fulu Tao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Natural Resources Institute Finland (Luke), Helsinki, Finland
| | - Yuanfeng Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jingyang Tong
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yonggui Xiao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhonghu He
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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18
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Affortit P, Effa-Effa B, Ndoye MS, Moukouanga D, Luchaire N, Cabrera-Bosquet L, Perálvarez M, Pilloni R, Welcker C, Champion A, Gantet P, Diedhiou AG, Manneh B, Aroca R, Vadez V, Laplaze L, Cubry P, Grondin A. Physiological and genetic control of transpiration efficiency in African rice, Oryza glaberrima Steud. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5279-5293. [PMID: 35429274 DOI: 10.1093/jxb/erac156] [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: 11/28/2021] [Accepted: 04/13/2022] [Indexed: 06/14/2023]
Abstract
Improving crop water use efficiency, the amount of carbon assimilated as biomass per unit of water used by a plant, is of major importance as water for agriculture becomes scarcer. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand remain unknown. These traits were measured in 2019 in a panel of 147 African rice (Oryza glaberrima) genotypes known to be potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly contrasted genotypes in terms of biomass accumulation and transpiration confirmed these associations and suggested that root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency, and its residuals, with links to genes involved in water transport and cell wall patterning. Our data suggest that root-shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.
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Affiliation(s)
- Pablo Affortit
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | - Branly Effa-Effa
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- CENAREST, Libreville, Gabon
| | - Mame Sokhatil Ndoye
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- CERAAS, Thiès, Senegal
| | | | - Nathalie Luchaire
- LEPSE, Université de Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | - Raphaël Pilloni
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | - Claude Welcker
- LEPSE, Université de Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Antony Champion
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | - Pascal Gantet
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | | | | | | | - Vincent Vadez
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- CERAAS, Thiès, Senegal
- LMI LAPSE, Dakar, Senegal
- ICRISAT, Patancheru, India
| | - Laurent Laplaze
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- LMI LAPSE, Dakar, Senegal
| | - Philippe Cubry
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | - Alexandre Grondin
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- CERAAS, Thiès, Senegal
- LMI LAPSE, Dakar, Senegal
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19
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Forero MG, Murcia HF, Méndez D, Betancourt-Lozano J. LiDAR Platform for Acquisition of 3D Plant Phenotyping Database. PLANTS 2022; 11:plants11172199. [PMID: 36079580 PMCID: PMC9459957 DOI: 10.3390/plants11172199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/26/2022] [Accepted: 08/10/2022] [Indexed: 11/16/2022]
Abstract
Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.
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20
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Welcker C, Spencer NA, Turc O, Granato I, Chapuis R, Madur D, Beauchene K, Gouesnard B, Draye X, Palaffre C, Lorgeou J, Melkior S, Guillaume C, Presterl T, Murigneux A, Wisser RJ, Millet EJ, van Eeuwijk F, Charcosset A, Tardieu F. Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions. Nat Commun 2022; 13:3225. [PMID: 35680899 PMCID: PMC9184527 DOI: 10.1038/s41467-022-30872-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 05/23/2022] [Indexed: 11/08/2022] Open
Abstract
Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha-1 year-1) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits.
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Affiliation(s)
- Claude Welcker
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | - Olivier Turc
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Italo Granato
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Romain Chapuis
- DIASCOPE, Université de Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Delphine Madur
- GQE-Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | - Brigitte Gouesnard
- AGAP institut Univ. Montpellier, INRAE, CIRAD, Institut Agro, Montpellier, France
| | - Xavier Draye
- Catholic Univ. Louvain, Earth & Life Institute, Louvain la Neuve, Belgium
| | | | | | | | | | | | | | - Randall J Wisser
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | - Alain Charcosset
- GQE-Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - François Tardieu
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France.
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21
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Eyland D, Luchaire N, Cabrera‐Bosquet L, Parent B, Janssens SB, Swennen R, Welcker C, Tardieu F, Carpentier SC. High-throughput phenotyping reveals differential transpiration behaviour within the banana wild relatives highlighting diversity in drought tolerance. PLANT, CELL & ENVIRONMENT 2022; 45:1647-1663. [PMID: 35297073 PMCID: PMC9310827 DOI: 10.1111/pce.14310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/22/2022] [Indexed: 06/14/2023]
Abstract
Crop wild relatives, the closely related species of crops, may harbour potentially important sources of new allelic diversity for (a)biotic tolerance or resistance. However, to date, wild diversity is only poorly characterized and evaluated. Banana has a large wild diversity but only a narrow proportion is currently used in breeding programmes. The main objective of this study was to evaluate genotype-dependent transpiration responses in relation to the environment. By applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential. We characterized and evaluated six (sub)species and discerned four phenotypic clusters. Significant differences were observed in leaf area, cumulative transpiration and transpiration efficiency. We confirmed a general stomatal-driven 'isohydric' drought avoidance behaviour, but discovered genotypic differences in the onset and intensity of stomatal closure. We pinpointed crucial genotype-specific soil water potentials when drought avoidance mechanisms were initiated and when stress kicked in. Differences between (sub)species were dependent on environmental conditions, illustrating the need for high-throughput dynamic phenotyping, modelling and validation. We conclude that the banana wild relatives contain useful drought tolerance traits, emphasising the importance of their conservation and potential for use in breeding programmes.
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Affiliation(s)
- David Eyland
- Laboratory of Tropical Crop Improvement, Division of Crop BiotechnicsKU LeuvenLeuvenBelgium
| | - Nathalie Luchaire
- Département Environnement et AgronomieLEPSE, Univ Montpellier, INRAE, Institut AgroMontpellierFrance
| | - Llorenç Cabrera‐Bosquet
- Département Environnement et AgronomieLEPSE, Univ Montpellier, INRAE, Institut AgroMontpellierFrance
| | - Boris Parent
- Département Environnement et AgronomieLEPSE, Univ Montpellier, INRAE, Institut AgroMontpellierFrance
| | - Steven B. Janssens
- Department ResearchMeise Botanic GardenMeiseBelgium
- Department of BiologyKU LeuvenLeuvenBelgium
| | - Rony Swennen
- Laboratory of Tropical Crop Improvement, Division of Crop BiotechnicsKU LeuvenLeuvenBelgium
- Banana and Plantain Crop ImprovementInternational Institute of Tropical AgricultureKampalaUganda
| | - Claude Welcker
- Département Environnement et AgronomieLEPSE, Univ Montpellier, INRAE, Institut AgroMontpellierFrance
| | - François Tardieu
- Département Environnement et AgronomieLEPSE, Univ Montpellier, INRAE, Institut AgroMontpellierFrance
| | - Sebastien C. Carpentier
- Laboratory of Tropical Crop Improvement, Division of Crop BiotechnicsKU LeuvenLeuvenBelgium
- Biodiversity for Food and AgricultureBioversity InternationalLeuvenBelgium
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22
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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23
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Light Interception, Photosynthetic Performance, and Yield of Oil Palm Interspecific OxG Hybrid (Elaeis oleifera (Kunth) Cortés x Elaeis guineensis Jacq.) under Three Planting Densities. PLANTS 2022; 11:plants11091166. [PMID: 35567167 PMCID: PMC9101212 DOI: 10.3390/plants11091166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 12/02/2022]
Abstract
Environmental conditions are crucial for crops’ growth, development, and productivity. One of the most important physiological factors associated with the production of crops is the use of solar radiation for the photosynthesis process, which determines the amount of assimilates available for crop growth and yield. Three age classes (4, 6, and 14 years) and three planting densities (143, 128, and 115 palms ha−1) were evaluated in a commercial interspecific Elaeis Oleifera x Elaeis guineensis hybrid Coari x La Mé. The light interception patterns and the photosynthetic performance were determined. Measurements were taken of the leaf area, the number of leaves, and incident and photosynthetically transmitted active radiation. Also, photosynthetic rates, light, and yield were measured. The canopy extinction coefficient (Kc) was estimated using the Monsi and Saeki model. Under the evaluated conditions, the average Kc value for 4-year-old palms was 0.44; for the 6-year-old group of palms, the average value was 0.40, and 0.32 for the 14-year-old palms, with coefficients of determination (R2) greater than 0.8. A pattern associated with the age of the crop was observed, where the Kc decreased in groups of adult palms. The results showed increased Kc as the planting density decreased. No statistically significant differences were observed between planting densities or ages in the light and CO2 curves regarding photosynthesis. The leaf level in which the measurement was made influenced photosynthesis. Thus, the highest values of the photosynthesis parameters were observed in leaf 17. The crop yield tended to stabilize 8 years after planting under 143 and 128 palms per hectare, but 14 years after planting, the Fresh fruit bunch (FFB) production was still growing under 115 palms per hectare. The results showed that, up to year 14 after planting, the highest cumulative yield was achieved with 115 palms per hectare. This was partly caused by a sharp decline in production observed under 128 palms per hectare, which could indicate that in the long production cycle of the OxG hybrids, the 115-palms-per-hectare planting density would result in higher cumulative FFB production. Furthermore, the results showed that the optimum planting density for the hybrids of the present study would be 120 palms ha−1, corresponding to a planting distance of 9.8 m between plants.
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24
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Robles-Zazueta CA, Pinto F, Molero G, Foulkes MJ, Reynolds MP, Murchie EH. Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck. FRONTIERS IN PLANT SCIENCE 2022; 13:828451. [PMID: 35481146 PMCID: PMC9036448 DOI: 10.3389/fpls.2022.828451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R 2 = 0.48, RMSE = 5.24 μmol m-2 s-1 and stomatal conductance: R 2 = 0.36, RMSE = 0.14 mol m-2 s-1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R 2 = 0.3, p < 0.05; R 2 = 0.29, p < 0.05) and at 7 days after anthesis (R 2 = 0.15, p < 0.05; R 2 = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.
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Affiliation(s)
- Carlos A. Robles-Zazueta
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - M. John Foulkes
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
| | - Matthew P. Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Erik H. Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
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25
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Pérez-Valencia DM, Rodríguez-Álvarez MX, Boer MP, Kronenberg L, Hund A, Cabrera-Bosquet L, Millet EJ, Eeuwijk FAV. A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data. Sci Rep 2022; 12:3177. [PMID: 35210494 PMCID: PMC8873425 DOI: 10.1038/s41598-022-06935-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 01/20/2022] [Indexed: 12/19/2022] Open
Abstract
High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.
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Affiliation(s)
- Diana M Pérez-Valencia
- BCAM-Basque Center for Applied Mathematics, Mazarredo 14, 48009, Bilbao, Spain.
- Departamento de Matemáticas, Universidad del País Vasco UPV/EHU, 48940, Leioa, Spain.
| | - María Xosé Rodríguez-Álvarez
- BCAM-Basque Center for Applied Mathematics, Mazarredo 14, 48009, Bilbao, Spain
- IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain
- Department of Statistics and Operations Research, Universidade de Vigo, 36310, Vigo, Spain
| | - Martin P Boer
- Biometris, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands
| | - Lukas Kronenberg
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, 8092, Zürich, Switzerland
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zürich, 8092, Zürich, Switzerland
| | - Andreas Hund
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, 8092, Zürich, Switzerland
| | | | - Emilie J Millet
- Biometris, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands
- LEPSE, Univ Montpellier, INRAE, Institut Agro, 34060, Montpellier, France
| | - Fred A van Eeuwijk
- Biometris, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands
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26
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Morphological and Physiological Screening to Predict Lettuce Biomass Production in Controlled Environment Agriculture. REMOTE SENSING 2022. [DOI: 10.3390/rs14020316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Fast growth and rapid turnover is an important crop trait in controlled environment agriculture (CEA) due to its high cost. An ideal screening approach for fast-growing cultivars should detect desirable phenotypes non-invasively at an early growth stage, based on morphological and/or physiological traits. Hence, we established a rapid screening protocol based on a simple chlorophyll fluorescence imaging (CFI) technique to quantify the projected canopy size (PCS) of plants, combined with electron transport rate (ETR) measurements using a chlorophyll fluorometer. Eleven lettuce cultivars (Lactuca sativa), selected based on morphological differences, were grown in a greenhouse and imaged twice a week. Shoot dry weight (DW) of green cultivars at harvest 51 days after germination (DAG) was correlated with PCS at 13 DAG (R2 = 0.74), when the first true leaves had just appeared and the PCS was <8.5 cm2. However, early PCS of high anthocyanin (red) cultivars was not predictive of DW. Because light absorption by anthocyanins reduces the amount of photons available for photosynthesis, anthocyanins lower light use efficiency (LUE; DW/total incident light on canopy over the cropping cycle) and reduce growth. Additionally, the total incident light on the canopy throughout the cropping cycle explained 90% and 55% of variability in DW within green and red cultivars, respectively. Estimated leaf level ETR at a photosynthetic photon flux density (PPFD) of 200 or 1000 µmol m−2 s−1 were not correlated with DW in either green or red cultivars. In conclusion, early PCS quantification is a useful tool for the selection of fast-growing green lettuce phenotypes. However, this approach may not work in cultivars with high anthocyanin content because anthocyanins direct excitation energy away from photosynthesis and growth, weakening the correlation between incident light and growth.
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27
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Kuromori T, Fujita M, Takahashi F, Yamaguchi‐Shinozaki K, Shinozaki K. Inter-tissue and inter-organ signaling in drought stress response and phenotyping of drought tolerance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 109:342-358. [PMID: 34863007 PMCID: PMC9300012 DOI: 10.1111/tpj.15619] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 05/10/2023]
Abstract
Plant response to drought stress includes systems for intracellular regulation of gene expression and signaling, as well as inter-tissue and inter-organ signaling, which helps entire plants acquire stress resistance. Plants sense water-deficit conditions both via the stomata of leaves and roots, and transfer water-deficit signals from roots to shoots via inter-organ signaling. Abscisic acid is an important phytohormone involved in the drought stress response and adaptation, and is synthesized mainly in vascular tissues and guard cells of leaves. In leaves, stress-induced abscisic acid is distributed to various tissues by transporters, which activates stomatal closure and expression of stress-related genes to acquire drought stress resistance. Moreover, the stepwise stress response at the whole-plant level is important for proper understanding of the physiological response to drought conditions. Drought stress is sensed by multiple types of sensors as molecular patterns of abiotic stress signals, which are transmitted via separate parallel signaling networks to induce downstream responses, including stomatal closure and synthesis of stress-related proteins and metabolites. Peptide molecules play important roles in the inter-organ signaling of dehydration from roots to shoots, as well as signaling of osmotic changes and reactive oxygen species/Ca2+ . In this review, we have summarized recent advances in research on complex plant drought stress responses, focusing on inter-tissue signaling in leaves and inter-organ signaling from roots to shoots. We have discussed the mechanisms via which drought stress adaptations and resistance are acquired at the whole-plant level, and have proposed the importance of quantitative phenotyping for measuring plant growth under drought conditions.
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Affiliation(s)
- Takashi Kuromori
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science2‐1 HirosawaWakoSaitama351‐0198Japan
| | - Miki Fujita
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science3‐1‐1 KoyadaiTsukubaIbaraki305‐0074Japan
| | - Fuminori Takahashi
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science3‐1‐1 KoyadaiTsukubaIbaraki305‐0074Japan
- Department of Biological Science and TechnologyGraduate School of Advanced EngineeringTokyo University of Science6‐3‐1 Niijyuku, Katsushika‐kuTokyo125‐8585Japan
| | - Kazuko Yamaguchi‐Shinozaki
- Laboratory of Plant Molecular PhysiologyGraduate School of Agricultural and Life SciencesThe University of Tokyo1‐1‐1 Yayoi, Bunkyo‐kuTokyo113‐8657Japan
- Research Institute for Agricultural and Life SciencesTokyo University of Agriculture1‐1‐1 Sakuragaoka, Setagaya‐kuTokyo156‐8502Japan
| | - Kazuo Shinozaki
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science2‐1 HirosawaWakoSaitama351‐0198Japan
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science3‐1‐1 KoyadaiTsukubaIbaraki305‐0074Japan
- Biotechonology CenterNational Chung Hsing University (NCHU)Taichung402Taiwan
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28
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Langstroff A, Heuermann MC, Stahl A, Junker A. Opportunities and limits of controlled-environment plant phenotyping for climate response traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1-16. [PMID: 34302493 PMCID: PMC8741719 DOI: 10.1007/s00122-021-03892-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 06/17/2021] [Indexed: 05/19/2023]
Abstract
Rising temperatures and changing precipitation patterns will affect agricultural production substantially, exposing crops to extended and more intense periods of stress. Therefore, breeding of varieties adapted to the constantly changing conditions is pivotal to enable a quantitatively and qualitatively adequate crop production despite the negative effects of climate change. As it is not yet possible to select for adaptation to future climate scenarios in the field, simulations of future conditions in controlled-environment (CE) phenotyping facilities contribute to the understanding of the plant response to special stress conditions and help breeders to select ideal genotypes which cope with future conditions. CE phenotyping facilities enable the collection of traits that are not easy to measure under field conditions and the assessment of a plant's phenotype under repeatable, clearly defined environmental conditions using automated, non-invasive, high-throughput methods. However, extrapolation and translation of results obtained under controlled environments to field environments is ambiguous. This review outlines the opportunities and challenges of phenotyping approaches under controlled environments complementary to conventional field trials. It gives an overview on general principles and introduces existing phenotyping facilities that take up the challenge of obtaining reliable and robust phenotypic data on climate response traits to support breeding of climate-adapted crops.
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Affiliation(s)
- Anna Langstroff
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich Buff-Ring 26, 35392, Giessen, Germany
| | - Marc C Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, OT Gatersleben, 06466, Seeland, Germany
| | - Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich Buff-Ring 26, 35392, Giessen, Germany
- Institute for Resistance Research and Stress Tolerance, Federal Research Centre for Cultivated Plants, Julius Kühn-Institut (JKI), Erwin-Baur-Strasse 27, 06484, Quedlinburg, Germany
| | - Astrid Junker
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, OT Gatersleben, 06466, Seeland, Germany.
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29
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Zhu Y, Sun G, Ding G, Zhou J, Wen M, Jin S, Zhao Q, Colmer J, Ding Y, Ober ES, Zhou J. Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat. PLANT PHYSIOLOGY 2021; 187:716-738. [PMID: 34608970 PMCID: PMC8491082 DOI: 10.1093/plphys/kiab324] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 06/22/2021] [Indexed: 05/12/2023]
Abstract
Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.
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Affiliation(s)
- Yulei Zhu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Gang Sun
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Guohui Ding
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Jie Zhou
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Mingxing Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- Zhenjiang Institute of Agricultural Science in Hill Area of Jiangsu Province, Jurong 212400, China
| | - Shichao Jin
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Qiang Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Joshua Colmer
- Earlham Institute, Norwich Research Park, Norwich NR4 7UH, UK
| | - Yanfeng Ding
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Eric S. Ober
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
| | - Ji Zhou
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
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30
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Wen W, Wang Y, Wu S, Liu K, Gu S, Guo X. 3D phytomer-based geometric modelling method for plants-the case of maize. AOB PLANTS 2021; 13:plab055. [PMID: 34603653 PMCID: PMC8482417 DOI: 10.1093/aobpla/plab055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Geometric plant modelling is crucial in in silico plants. Existing geometric modelling methods have focused on the topological structure and basic organ profiles, simplifying the morphological features. However, the models cannot effectively differentiate cultivars, limiting FSPM application in crop breeding and management. This study proposes a 3D phytomer-based geometric modelling method with maize (Zea Mays) as the representative plant. Specifically, conversion methods between skeleton and mesh models of 3D phytomer are specified. This study describes the geometric modelling of maize shoots and populations by assembling 3D phytomers. Results show that the method can quickly and efficiently construct 3D models of maize plants and populations, with the ability to show morphological, structural and functional differences among four representative cultivars. The method takes into account both the geometric modelling efficiency and 3D detail features to achieve automatic operation of geometric modelling through the standardized description of 3D phytomers. Therefore, this study provides a theoretical and technical basis for the research and application of in silico plants.
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Affiliation(s)
- Weiliang Wen
- Beijing Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
| | - Yongjian Wang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
| | - Sheng Wu
- Beijing Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
| | - Kai Liu
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
| | - Shenghao Gu
- Beijing Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
| | - Xinyu Guo
- Beijing Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
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Liu F, Song Q, Zhao J, Mao L, Bu H, Hu Y, Zhu XG. Canopy occupation volume as an indicator of canopy photosynthetic capacity. THE NEW PHYTOLOGIST 2021; 232:941-956. [PMID: 34245568 DOI: 10.1111/nph.17611] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/03/2021] [Indexed: 06/13/2023]
Abstract
Leaf angle and leaf area index together influence canopy light interception and canopy photosynthesis. However, so far, there is no effective method to identify the optimal combination of these two parameters for canopy photosynthesis. In this study, first a robust high-throughput method for accurate segmentation of maize organs based on 3D point clouds data was developed, then the segmented plant organs were used to generate new 3D point clouds for the canopy of altered architectures. With this, we simulated the synergistic effect of leaf area and leaf angle on canopy photosynthesis. The results show that, compared to the traditional parameters describing the canopy photosynthesis including leaf area index, facet angle and canopy coverage, a new parameter - the canopy occupation volume (COV) - can better explain the variations of canopy photosynthetic capacity. Specifically, COV can explain > 79% variations of canopy photosynthesis generated by changing leaf angle and > 84% variations of canopy photosynthesis generated by changing leaf area. As COV can be calculated in a high-throughput manner based on the canopy point clouds, it can be used to evaluate canopy architecture in breeding and agronomic research.
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Affiliation(s)
- Fusang Liu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qingfeng Song
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jinke Zhao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Linxiong Mao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hongyi Bu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Yong Hu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Xin-Guang Zhu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
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32
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Jaramillo Roman V, van de Zedde R, Peller J, Visser RGF, van der Linden CG, van Loo EN. High-Resolution Analysis of Growth and Transpiration of Quinoa Under Saline Conditions. FRONTIERS IN PLANT SCIENCE 2021; 12:634311. [PMID: 34421935 PMCID: PMC8376478 DOI: 10.3389/fpls.2021.634311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 07/13/2021] [Indexed: 06/13/2023]
Abstract
The Plantarray 3.0 phenotyping platform® was used to monitor the growth and water use of the quinoa varieties Pasto and selRiobamba under salinity (0-300 mM NaCl). Salinity reduced the cumulative transpiration of both varieties by 60% at 200 mM NaCl and by 75 and 82% at 300 mM NaCl for selRiobamba and Pasto, respectively. Stomatal conductance was reduced by salinity, but at 200 mM NaCl Pasto showed a lower reduction (15%) than selRiobamba (35%), along with decreased specific leaf area. Diurnal changes in water use parameters indicate that under salt stress, daily transpiration in quinoa is less responsive to changes in light irradiance, and stomatal conductance is modulated to maximize CO2 uptake and minimize water loss following the changes in VPD (vapor pressure deficit). These changes might contribute to the enhanced water use efficiency of both varieties under salt stress. The mechanistic crop model LINTUL was used to integrate physiological responses into the radiation use efficiency of the plants (RUE), which was more reduced in Pasto than selRiobamba under salinity. By the end of the experiment (eleven weeks after sowing, six weeks after stress), the growth of Pasto was significantly lower than selRiobamba, fresh biomass was 50 and 35% reduced at 200 mM and 70 and 50% reduced at 300 mM NaCl for Pasto and selRiobamba, respectively. We argue that contrasting water management strategies can at least partly explain the differences in salt tolerance between Pasto and selRiobamba. Pasto adopted a "conservative-growth" strategy, saving water at the expense of growth, while selRiobamba used an "acquisitive-growth" strategy, maximizing growth in spite of the stress. The implementation of high-resolution phenotyping could help to dissect these complex growth traits that might be novel breeding targets for abiotic stress tolerance.
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Affiliation(s)
- Viviana Jaramillo Roman
- Plant Breeding, Wageningen University and Research, Wageningen, Netherlands
- Graduate School Experimental Plant Sciences, Wageningen University, Wageningen, Netherlands
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Abstract
Use of 3D sensors in plant phenotyping has increased in the last few years. Various image acquisition, 3D representations, 3D model processing and analysis techniques exist to help the researchers. However, a review of approaches, algorithms, and techniques used for 3D plant physiognomic analysis is lacking. In this paper, we investigate the techniques and algorithms used at various stages of processing and analysing 3D models of plants, and identify their current limiting factors. This review will serve potential users as well as new researchers in this field. The focus is on exploring studies monitoring the plant growth of single plants or small scale canopies as opposed to large scale monitoring in the field.
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34
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Miao T, Wen W, Li Y, Wu S, Zhu C, Guo X. Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots. Gigascience 2021; 10:6272094. [PMID: 33963385 PMCID: PMC8105162 DOI: 10.1093/gigascience/giab031] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 03/10/2021] [Accepted: 04/12/2021] [Indexed: 01/31/2023] Open
Abstract
Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.
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Affiliation(s)
- Teng Miao
- College of Information and Electrical Engineering, Shenyang Agricultural University, Dongling Road, Shenhe District, Liaoning Province, Shenyang 110161, China
| | - Weiliang Wen
- Beijing Research Center for Information Technology in Agriculture, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China.,National Engineering Research Center for Information Technology in Agriculture, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China.,Beijing Key Lab of Digital Plant, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
| | - Yinglun Li
- National Engineering Research Center for Information Technology in Agriculture, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China.,Beijing Key Lab of Digital Plant, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
| | - Sheng Wu
- Beijing Research Center for Information Technology in Agriculture, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China.,National Engineering Research Center for Information Technology in Agriculture, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China.,Beijing Key Lab of Digital Plant, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
| | - Chao Zhu
- College of Information and Electrical Engineering, Shenyang Agricultural University, Dongling Road, Shenhe District, Liaoning Province, Shenyang 110161, China
| | - Xinyu Guo
- Beijing Research Center for Information Technology in Agriculture, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China.,National Engineering Research Center for Information Technology in Agriculture, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China.,Beijing Key Lab of Digital Plant, 11#Shuguang Huayuan Middle Road, Haidian District, Beijing 100097, China
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35
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Zhang X, Man Y, Zhuang X, Shen J, Zhang Y, Cui Y, Yu M, Xing J, Wang G, Lian N, Hu Z, Ma L, Shen W, Yang S, Xu H, Bian J, Jing Y, Li X, Li R, Mao T, Jiao Y, Sodmergen, Ren H, Lin J. Plant multiscale networks: charting plant connectivity by multi-level analysis and imaging techniques. SCIENCE CHINA-LIFE SCIENCES 2021; 64:1392-1422. [PMID: 33974222 DOI: 10.1007/s11427-020-1910-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/04/2021] [Indexed: 12/21/2022]
Abstract
In multicellular and even single-celled organisms, individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation. Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes. Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project. In plant science, network analysis has similarly been applied to study the connectivity of plant components at the molecular, subcellular, cellular, organic, and organism levels. Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype. In this review, we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities. We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants. Finally, we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.
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Affiliation(s)
- Xi Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Yi Man
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Xiaohong Zhuang
- School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, 999077, China
| | - Jinbo Shen
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300, China
| | - Yi Zhang
- Key Laboratory of Cell Proliferation and Regulation Biology, Ministry of Education, College of Life Science, Beijing Normal University, Beijing, 100875, China
| | - Yaning Cui
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Meng Yu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Jingjing Xing
- Key Laboratory of Plant Stress Biology, School of Life Sciences, Henan University, Kaifeng, 457004, China
| | - Guangchao Wang
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Na Lian
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Zijian Hu
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Lingyu Ma
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Weiwei Shen
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Shunyao Yang
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Huimin Xu
- College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Jiahui Bian
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Yanping Jing
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Xiaojuan Li
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Ruili Li
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Tonglin Mao
- State Key Laboratory of Plant Physiology and Biochemistry, Department of Plant Sciences, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Yuling Jiao
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and National Center for Plant Gene Research, Beijing, 100101, China
| | - Sodmergen
- Key Laboratory of Ministry of Education for Cell Proliferation and Differentiation, College of Life Sciences, Peking University, Beijing, 100871, China
| | - Haiyun Ren
- Key Laboratory of Cell Proliferation and Regulation Biology, Ministry of Education, College of Life Science, Beijing Normal University, Beijing, 100875, China
| | - Jinxing Lin
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China. .,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China.
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36
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Meyer RC, Weigelt-Fischer K, Knoch D, Heuermann M, Zhao Y, Altmann T. Temporal dynamics of QTL effects on vegetative growth in Arabidopsis thaliana. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:476-490. [PMID: 33080013 DOI: 10.1093/jxb/eraa490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
We assessed early vegetative growth in a population of 382 accessions of Arabidopsis thaliana using automated non-invasive high-throughput phenotyping. All accessions were imaged daily from 7 d to 18 d after sowing in three independent experiments and genotyped using the Affymetrix 250k SNP array. Projected leaf area (PLA) was derived from image analysis and used to calculate relative growth rates (RGRs). In addition, initial seed size was determined. The generated datasets were used jointly for a genome-wide association study that identified 238 marker-trait associations (MTAs) individually explaining up to 8% of the total phenotypic variation. Co-localization of MTAs occurred at 33 genomic positions. At 21 of these positions, sequential co-localization of MTAs for 2-9 consecutive days was observed. The detected MTAs for PLA and RGR could be grouped according to their temporal expression patterns, emphasizing that temporal variation of MTA action can be observed even during the vegetative growth phase, a period of continuous formation and enlargement of seemingly similar rosette leaves. This indicates that causal genes may be differentially expressed in successive periods. Analyses of the temporal dynamics of biological processes are needed to gain important insight into the molecular mechanisms of growth-controlling processes in plants.
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Affiliation(s)
- Rhonda C Meyer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Kathleen Weigelt-Fischer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Dominic Knoch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Marc Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Breeding Research, Research Group Quantitative Genetics, OT Gatersleben, Corrensstraße, Seeland, Germany
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, Research Group Heterosis, OT Gatersleben, Corrensstraße, Seeland, Germany
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37
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Williamson HF, Brettschneider J, Caccamo M, Davey RP, Goble C, Kersey PJ, May S, Morris RJ, Ostler R, Pridmore T, Rawlings C, Studholme D, Tsaftaris SA, Leonelli S. Data management challenges for artificial intelligence in plant and agricultural research. F1000Res 2021; 10:324. [PMID: 36873457 PMCID: PMC9975417 DOI: 10.12688/f1000research.52204.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.
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Affiliation(s)
- Hugh F Williamson
- Exeter Centre for the Study of the Life Sciences & Institute for Data Science and Artificial Intelligence, University of Exeter, Exeter, UK
| | | | - Mario Caccamo
- NIAB, National Research Institute of Brewing, East Malling, UK
| | | | - Carole Goble
- Department of Computer Science, University of Manchester, Manchester, UK
| | | | - Sean May
- School of Biosciences, University of Nottingham, Loughborough, UK
| | | | - Richard Ostler
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpendem, UK
| | - Tony Pridmore
- School of Computer Science, University of Nottingham, Nottingham, UK
| | - Chris Rawlings
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpendem, UK
| | | | - Sotirios A Tsaftaris
- Institute of Digital Communications, University of Edinburgh, Edinburgh, UK.,Alan Turing Institute, London, UK
| | - Sabina Leonelli
- Exeter Centre for the Study of the Life Sciences & Institute for Data Science and Artificial Intelligence, University of Exeter, Exeter, UK.,Alan Turing Institute, London, UK
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38
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Yoon HI, Kim HY, Kim J, Son JE. Quantitative Analysis of UV-B Radiation Interception and Bioactive Compound Contents in Kale by Leaf Position According to Growth Progress. FRONTIERS IN PLANT SCIENCE 2021; 12:667456. [PMID: 34305968 PMCID: PMC8297650 DOI: 10.3389/fpls.2021.667456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 06/07/2021] [Indexed: 05/13/2023]
Abstract
UV-B (280-315 nm) radiation has been used as an effective tool to improve bioactive compound contents in controlled environments, such as plant factories. However, plant structure changes with growth progress induce different positional distributions of UV-B radiation interception, which cause difficulty in accurately evaluating the effects of UV-B on biosynthesis of bioactive compounds. The objective of this study was to quantitatively analyze the positional distributions of UV-B radiation interception and bioactive compound contents of kales (Brassica oleracea L. var. acephala) with growth progress and their relationships. Short-term moderate UV-B levels did not affect the plant growth and photosynthetic parameters. Spatial UV-B radiation interception was analyzed quantitatively by using 3D-scanned plant models and ray-tracing simulations. As growth progressed, the differences in absorbed UV-B energy between leaf positions were more pronounced. The concentrations of total phenolic compound (TPC) and total flavonoid compound (TFC) were higher with more cumulative absorbed UV-B energy. The cumulative UV energy yields for TFC were highest for the upper leaves of the older plants, while those for TPC were highest in the middle leaves of the younger plants. Despite the same UV-B levels, the UV-B radiation interception and UV-B susceptibility in the plants varied with leaf position and growth stage, which induced the different biosynthesis of TFC and TPC. This attempt to quantify the relationship between UV-B radiation interception and bioactive compound contents will contribute to the estimation and production of bioactive compounds in plant factories.
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Affiliation(s)
- Hyo In Yoon
- Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University, Seoul, South Korea
| | - Hyun Young Kim
- Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University, Seoul, South Korea
| | - Jaewoo Kim
- Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University, Seoul, South Korea
| | - Jung Eek Son
- Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University, Seoul, South Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, South Korea
- *Correspondence: Jung Eek Son,
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39
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Das Choudhury S, Maturu S, Samal A, Stoerger V, Awada T. Leveraging Image Analysis to Compute 3D Plant Phenotypes Based on Voxel-Grid Plant Reconstruction. FRONTIERS IN PLANT SCIENCE 2020; 11:521431. [PMID: 33362806 PMCID: PMC7755976 DOI: 10.3389/fpls.2020.521431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 11/17/2020] [Indexed: 05/31/2023]
Abstract
High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.
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Affiliation(s)
- Sruti Das Choudhury
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Srikanth Maturu
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Ashok Samal
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Vincent Stoerger
- Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tala Awada
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States
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40
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Gaillard M, Miao C, Schnable JC, Benes B. Voxel carving-based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency. PLANT DIRECT 2020; 4:e00255. [PMID: 33073164 PMCID: PMC7541904 DOI: 10.1002/pld3.255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 07/09/2020] [Indexed: 05/02/2023]
Abstract
Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and light interception efficiency of agricultural crop canopies will be essential to meeting the growing food needs. Canopy architecture is inherently three-dimensional (3D), but many approaches to measuring canopy architecture component traits treat the canopy as a two-dimensional (2D) structure to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos. Our approach builds on the voxel carving algorithm to allow for fully automatic reconstruction of hundreds of plants. It was employed to generate 3D reconstructions of individual plants within a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of known and previously unreported loci controlling light interception efficiency in sorghum. The approach is generalizable and scalable, and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. We also propose a set of best practices to increase 3D reconstructions' accuracy.
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Affiliation(s)
- Mathieu Gaillard
- Department of Computer Graphics TechnologyPurdue UniversityWest LafayetteINUSA
| | - Chenyong Miao
- Center for Plant Science Innovation and Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNEUSA
| | - James C. Schnable
- Center for Plant Science Innovation and Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNEUSA
| | - Bedrich Benes
- Department of Computer Graphics TechnologyPurdue UniversityWest LafayetteINUSA
- Department of Computer SciencePurdue UniversityWest LafayetteINUSA
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41
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Lacube S, Manceau L, Welcker C, Millet EJ, Gouesnard B, Palaffre C, Ribaut JM, Hammer G, Parent B, Tardieu F. Simulating the effect of flowering time on maize individual leaf area in contrasting environmental scenarios. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:5577-5588. [PMID: 32526015 PMCID: PMC7501815 DOI: 10.1093/jxb/eraa278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 06/08/2020] [Indexed: 06/11/2023]
Abstract
The quality of yield prediction is linked to that of leaf area. We first analysed the consequences of flowering time and environmental conditions on the area of individual leaves in 127 genotypes presenting contrasting flowering times in fields of Europe, Mexico, and Kenya. Flowering time was the strongest determinant of leaf area. Combined with a detailed field experiment, this experiment showed a large effect of flowering time on the final leaf number and on the distribution of leaf growth rate and growth duration along leaf ranks, in terms of both length and width. Equations with a limited number of genetic parameters predicted the beginning, end, and maximum growth rate (length and width) for each leaf rank. The genotype-specific environmental effects were analysed with datasets in phenotyping platforms that assessed the effects (i) of the amount of intercepted light on leaf width, and (ii) of temperature, evaporative demand, and soil water potential on leaf elongation rate. The resulting model was successfully tested for 31 hybrids in 15 European and Mexican fields. It potentially allows prediction of the vertical distribution of leaf area of a large number of genotypes in contrasting field conditions, based on phenomics and on sensor networks.
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Affiliation(s)
| | | | | | | | - Brigitte Gouesnard
- Univ. Montpellier, INRAE, CIRAD, Institut Agro, UMR AGAP, Montpellier, France
| | - Carine Palaffre
- INRAE, UE 0394, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, Saint-Martin-De-Hinx, France
| | | | - Graeme Hammer
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Brisbane, QLD, Australia
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Linear Variance, P-splines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related? JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2020. [DOI: 10.1007/s13253-020-00412-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AbstractNearest-neighbour methods based on first differences are an approach to spatial analysis of field trials with a long history, going back to the early work by Papadakis first published in 1937. These methods are closely related to a geostatistical model that assumes spatial covariance to be a linear function of distance. Recently, P-splines have been proposed as a flexible alternative to spatial analysis of field trials. On the surface, P-splines may appear like a completely new type of method, but closer scrutiny reveals intimate ties with earlier proposals based on first differences and the linear variance model. This paper studies these relations in detail, first focussing on one-dimensional spatial models and then extending to the two-dimensional case. Two yield trial datasets serve to illustrate the methods and their equivalence relations. Parsimonious linear variance and random walk models are suggested as a good point of departure for exploring possible improvements of model fit via the flexible P-spline framework.
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Maize adaptation across temperate climates was obtained via expression of two florigen genes. PLoS Genet 2020; 16:e1008882. [PMID: 32673315 DOI: 10.1371/journal.pgen.1008882] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 07/28/2020] [Accepted: 05/22/2020] [Indexed: 11/19/2022] Open
Abstract
Expansion of the maize growing area was central for food security in temperate regions. In addition to the suppression of the short-day requirement for floral induction, it required breeding for a large range of flowering time that compensates the effect of South-North gradients of temperatures. Here we show the role of a novel florigen gene, ZCN12, in the latter adaptation in cooperation with ZCN8. Strong eQTLs of ZCN8 and ZCN12, measured in 327 maize lines, accounted for most of the genetic variance of flowering time in platform and field experiments. ZCN12 had a strong effect on flowering time of transgenic Arabidopsis thaliana plants; a path analysis showed that it directly affected maize flowering time together with ZCN8. The allelic composition at ZCN QTLs showed clear signs of selection by breeders. This suggests that florigens played a central role in ensuring a large range of flowering time, necessary for adaptation to temperate areas.
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Reynolds M, Chapman S, Crespo-Herrera L, Molero G, Mondal S, Pequeno DNL, Pinto F, Pinera-Chavez FJ, Poland J, Rivera-Amado C, Saint Pierre C, Sukumaran S. Breeder friendly phenotyping. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2020; 295:110396. [PMID: 32534615 DOI: 10.1016/j.plantsci.2019.110396] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 12/12/2019] [Accepted: 12/26/2019] [Indexed: 05/18/2023]
Abstract
The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.
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Affiliation(s)
| | - Scott Chapman
- CISRO Agriculture and Food, The University of Queensland, Australia
| | | | - Gemma Molero
- International Maize and Wheat Improvement Centre, Mexico
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Pieters O, De Swaef T, Lootens P, Stock M, Roldán-Ruiz I, wyffels F. Gloxinia-An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants. SENSORS 2020; 20:s20113055. [PMID: 32481619 PMCID: PMC7309107 DOI: 10.3390/s20113055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/20/2020] [Accepted: 05/26/2020] [Indexed: 11/28/2022]
Abstract
The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations.
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Affiliation(s)
- Olivier Pieters
- IDLab-AIRO—Ghent University—imec, Technologiepark-Zwijnaarde 126, 9052 Zwijnaarde, Belgium;
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Caritasstraat 39, 9090 Melle, Belgium; (T.D.S.); (P.L.); (I.R.-R.)
- Correspondence:
| | - Tom De Swaef
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Caritasstraat 39, 9090 Melle, Belgium; (T.D.S.); (P.L.); (I.R.-R.)
| | - Peter Lootens
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Caritasstraat 39, 9090 Melle, Belgium; (T.D.S.); (P.L.); (I.R.-R.)
| | - Michiel Stock
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure links 653, 9000 Ghent, Belgium;
| | - Isabel Roldán-Ruiz
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Caritasstraat 39, 9090 Melle, Belgium; (T.D.S.); (P.L.); (I.R.-R.)
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ledeganckstraat 35, 9000 Gent, Belgium
| | - Francis wyffels
- IDLab-AIRO—Ghent University—imec, Technologiepark-Zwijnaarde 126, 9052 Zwijnaarde, Belgium;
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Watt M, Fiorani F, Usadel B, Rascher U, Muller O, Schurr U. Phenotyping: New Windows into the Plant for Breeders. ANNUAL REVIEW OF PLANT BIOLOGY 2020; 71:689-712. [PMID: 32097567 DOI: 10.1146/annurev-arplant-042916-041124] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.
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Affiliation(s)
- Michelle Watt
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Fabio Fiorani
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Björn Usadel
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
- Institute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany
| | - Uwe Rascher
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Onno Muller
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Ulrich Schurr
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
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Wu S, Wen W, Wang Y, Fan J, Wang C, Gou W, Guo X. MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:1848437. [PMID: 33313542 PMCID: PMC7706320 DOI: 10.34133/2020/1848437] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/19/2020] [Indexed: 05/26/2023]
Abstract
Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform's device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R 2) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.
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Affiliation(s)
- Sheng Wu
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Weiliang Wen
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Yongjian Wang
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Jiangchuan Fan
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Chuanyu Wang
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Wenbo Gou
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Xinyu Guo
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
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48
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Feng X, Zhan Y, Wang Q, Yang X, Yu C, Wang H, Tang Z, Jiang D, Peng C, He Y. Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 101:1448-1461. [PMID: 31680357 DOI: 10.1111/tpj.14597] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 10/10/2019] [Accepted: 10/23/2019] [Indexed: 05/23/2023]
Abstract
The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high-throughput plant phenotyping technologies have been adopted that use plant morphological and physiological measurements in a non-destructive manner to accelerate plant breeding processes. Here, a hyperspectral imaging (HSI) technique was implemented to monitor the plant phenotypes of 13 okra (Abelmoschus esculentus L.) genotypes after 2 and 7 days of salt treatment. Physiological and biochemical traits, such as fresh weight, SPAD, elemental contents and photosynthesis-related parameters, which require laborious, time-consuming measurements, were also investigated. Traditional laboratory-based methods indicated the diverse performance levels of different okra genotypes in response to salinity stress. We introduced improved plant and leaf segmentation approaches to RGB images extracted from HSI imaging based on deep learning. The state-of-the-art performance of the deep-learning approach for segmentation resulted in an intersection over union score of 0.94 for plant segmentation and a symmetric best dice score of 85.4 for leaf segmentation. Moreover, deleterious effects of salinity affected the physiological and biochemical processes of okra, which resulted in substantial changes in the spectral information. Four sample predictions were constructed based on the spectral data, with correlation coefficients of 0.835, 0.704, 0.609 and 0.588 for SPAD, sodium concentration, photosynthetic rate and transpiration rate, respectively. The results confirmed the usefulness of high-throughput phenotyping for studying plant salinity stress using a combination of HSI and deep-learning approaches.
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Affiliation(s)
- Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Yihua Zhan
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Qi Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Xufeng Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Chenliang Yu
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Haoyu Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - ZhiYu Tang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Dean Jiang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Cheng Peng
- Institute of Quality and Standard for Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
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Knoch D, Abbadi A, Grandke F, Meyer RC, Samans B, Werner CR, Snowdon RJ, Altmann T. Strong temporal dynamics of QTL action on plant growth progression revealed through high-throughput phenotyping in canola. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:68-82. [PMID: 31125482 PMCID: PMC6920335 DOI: 10.1111/pbi.13171] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 05/13/2019] [Accepted: 05/15/2019] [Indexed: 05/08/2023]
Abstract
A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high-throughput phenotyping in conjunction with genome-wide association studies to elucidate genotype-phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non-invasive phenotyping. Time-resolved data for early growth-related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome-wide SNP array data provided the framework for genome-wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker-trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time-resolved analyses to effectively unravel the complex and stage-specific contributions of genes affecting growth processes that operate at different developmental phases.
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Affiliation(s)
- Dominic Knoch
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Amine Abbadi
- Norddeutsche Pflanzenzucht Innovation GmbH (NPZi)HoltseeGermany
| | - Fabian Grandke
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
| | - Rhonda C. Meyer
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Birgit Samans
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
- Present address:
Technische Hochschule Mittelhessen (THM), University of Applied SciencesFachbereich Gesundheit35390GiessenGermany
| | - Christian R. Werner
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
- Present address:
The Roslin InstituteUniversity of EdinburghEaster Bush CampusMidlothianEH25 9RGUK
| | - Rod J. Snowdon
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
| | - Thomas Altmann
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
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50
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Konate M, Wilkinson MJ, Taylor J, Scott ES, Berger B, Rodriguez Lopez CM. Greenhouse Spatial Effects Detected in the Barley ( Hordeum vulgare L.) Epigenome Underlie Stochasticity of DNA Methylation. FRONTIERS IN PLANT SCIENCE 2020; 11:553907. [PMID: 33013971 PMCID: PMC7511590 DOI: 10.3389/fpls.2020.553907] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 08/24/2020] [Indexed: 05/10/2023]
Abstract
Environmental cues are known to alter the methylation profile of genomic DNA, and thereby change the expression of some genes. A proportion of such modifications may become adaptive by adjusting expression of stress response genes but others have been shown to be highly stochastic, even under controlled conditions. The influence of environmental flux on plants adds an additional layer of complexity that has potential to confound attempts to interpret interactions between environment, methylome, and plant form. We therefore adopt a positional and longitudinal approach to study progressive changes to barley DNA methylation patterns in response to salt exposure during development under greenhouse conditions. Methylation-sensitive amplified polymorphism (MSAP) and phenotypic analyses of nine diverse barley varieties were grown in a randomized plot design, under two salt treatments (0 and 75 mM NaCl). Combining environmental, phenotypic and epigenetic data analyses, we show that at least part of the epigenetic variability, previously described as stochastic, is linked to environmental micro-variations during plant growth. Additionally, we show that differences in methylation increase with time of exposure to micro-variations in environment. We propose that subsequent epigenetic studies take into account microclimate-induced epigenetic variability.
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Affiliation(s)
- Moumouni Konate
- Institut de l'Environnement et de Recherche Agricole (INERA), DRREA-Ouest, Bobo Dioulasso, Burkina Faso
| | - Michael J. Wilkinson
- Institute of Biological, Environmental and Rural Sciences, Penglais Campus, Aberystwyth, United Kingdom
- *Correspondence: Carlos Marcelino Rodriguez Lopez, ; Michael J. Wilkinson,
| | - Julian Taylor
- Biometry Hub, School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, Glen Osmond, SA, Australia
| | - Eileen S. Scott
- School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, Glen Osmond, SA, Australia
| | - Bettina Berger
- School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, Glen Osmond, SA, Australia
- The Plant Accelerator, Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, Glen Osmond, SA, Australia
| | - Carlos Marcelino Rodriguez Lopez
- Environmental Epigenetics and Genetics Group, Department of Horticulture, College of Agriculture, Food and Environment, University of Kentucky, Lexington, KY, United States
- *Correspondence: Carlos Marcelino Rodriguez Lopez, ; Michael J. Wilkinson,
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