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Anderegg J, Kirchgessner N, Aasen H, Zumsteg O, Keller B, Zenkl R, Walter A, Hund A. Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions. FRONTIERS IN PLANT SCIENCE 2024; 15:1335037. [PMID: 38895615 PMCID: PMC11184164 DOI: 10.3389/fpls.2024.1335037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 05/13/2024] [Indexed: 06/21/2024]
Abstract
Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.
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Affiliation(s)
- Jonas Anderegg
- Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Norbert Kirchgessner
- Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Helge Aasen
- Earth Observation of Agroecosystems Team, Research Division Agroecology and Environment, Agroscope, Zurich, Switzerland
| | - Olivia Zumsteg
- Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Beat Keller
- Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Radek Zenkl
- Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Achim Walter
- Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Andreas Hund
- Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
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Roth L, Binder M, Kirchgessner N, Tschurr F, Yates S, Hund A, Kronenberg L, Walter A. From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0185. [PMID: 38827955 PMCID: PMC11142864 DOI: 10.34133/plantphenomics.0185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/08/2024] [Indexed: 06/05/2024]
Abstract
Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.
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Affiliation(s)
- Lukas Roth
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Martina Binder
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Norbert Kirchgessner
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Flavian Tschurr
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Steven Yates
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Andreas Hund
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
| | | | - Achim Walter
- ETH Zurich, Institute of Agricultural Sciences, Universitätstrasse 2, 8092 Zürich, Switzerland
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3
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Brunner SM, Dinglasan E, Baraibar S, Alahmad S, Katsikis C, van der Meer S, Godoy J, Moody D, Smith M, Hickey L, Robinson H. Characterizing stay-green in barley across diverse environments: unveiling novel haplotypes. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:120. [PMID: 38709310 PMCID: PMC11074220 DOI: 10.1007/s00122-024-04612-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/23/2024] [Indexed: 05/07/2024]
Abstract
KEY MESSAGE There is variation in stay-green within barley breeding germplasm, influenced by multiple haplotypes and environmental conditions. The positive genetic correlation between stay-green and yield across multiple environments highlights the potential as a future breeding target. Barley is considered one of the most naturally resilient crops making it an excellent candidate to dissect the genetics of drought adaptive component traits. Stay-green, is thought to contribute to drought adaptation, in which the photosynthetic machinery is maintained for a longer period post-anthesis increasing the photosynthetic duration of the plant. In other cereal crops, including wheat, stay-green has been linked to increased yield under water-limited conditions. Utilizing a panel of diverse barley breeding lines from a commercial breeding program we aimed to characterize stay-green in four environments across two years. Spatiotemporal modeling was used to accurately model senescence patterns from flowering to maturity characterizing the variation for stay-green in barley for the first time. Environmental effects were identified, and multi-environment trait analysis was performed for stay-green characteristics during grain filling. A consistently positive genetic correlation was found between yield and stay-green. Twenty-two chromosomal regions with large effect haplotypes were identified across and within environment types, with ten being identified in multiple environments. In silico stacking of multiple desirable haplotypes showed an opportunity to improve the stay-green phenotype through targeted breeding. This study is the first of its kind to model barley stay-green in a large breeding panel and has detected novel, stable and environment specific haplotypes. This provides a platform for breeders to develop Australian barley with custom senescence profiles for improved drought adaptation.
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Affiliation(s)
- Stephanie M Brunner
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Eric Dinglasan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | | | - Samir Alahmad
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Christina Katsikis
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Sarah van der Meer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | | | - David Moody
- InterGrain Pty Ltd, Perth, WA, 6163, Australia
| | - Millicent Smith
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
- School of Agriculture and Food Sustainability, The University of Queensland, Gatton, QLD, Australia
| | - Lee Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
| | - Hannah Robinson
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
- InterGrain Pty Ltd, Perth, WA, 6163, Australia.
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Chang-Brahim I, Koppensteiner LJ, Beltrame L, Bodner G, Saranti A, Salzinger J, Fanta-Jende P, Sulzbachner C, Bruckmüller F, Trognitz F, Samad-Zamini M, Zechner E, Holzinger A, Molin EM. Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding. FRONTIERS IN PLANT SCIENCE 2024; 15:1319938. [PMID: 38699541 PMCID: PMC11064034 DOI: 10.3389/fpls.2024.1319938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/13/2024] [Indexed: 05/05/2024]
Abstract
Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.
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Affiliation(s)
- Ignacio Chang-Brahim
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Lorenzo Beltrame
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Gernot Bodner
- Department of Crop Sciences, Institute of Agronomy, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
| | - Anna Saranti
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Jules Salzinger
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Phillipp Fanta-Jende
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Christoph Sulzbachner
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Felix Bruckmüller
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Friederike Trognitz
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Elisabeth Zechner
- Verein zur Förderung einer nachhaltigen und regionalen Pflanzenzüchtung, Zwettl, Austria
| | - Andreas Holzinger
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Eva M. Molin
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
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Kumar B, Pal M, Yadava P, Kumar K, Langyan S, Jha AK, Singh I. Physiological and biochemical effects of 24-Epibrassinolide on drought stress adaptation in maize ( Zea mays L.). PeerJ 2024; 12:e17190. [PMID: 38560461 PMCID: PMC10981409 DOI: 10.7717/peerj.17190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/12/2024] [Indexed: 04/04/2024] Open
Abstract
Maize production and productivity are affected by drought stress in tropical and subtropical ecologies, as the majority of the area under maize cultivation in these ecologies is rain-fed. The present investigation was conducted to study the physiological and biochemical effects of 24-Epibrassinolide (EBR) as a plant hormone on drought tolerance in maize. Two maize hybrids, Vivek hybrid 9 and Bio 9637, were grown under three different conditions: (i) irrigated, (ii) drought, and (iii) drought+EBR. A total of 2 weeks before the anthesis, irrigation was discontinued to produce a drought-like condition. In the drought+EBR treatment group, irrigation was also stopped, and in addition, EBR was applied as a foliar spray on the same day in the drought plots. It was observed that drought had a major influence on the photosynthesis rate, membrane stability index, leaf area index, relative water content, and leaf water potential; this effect was more pronounced in Bio 9637. Conversely, the activities of antioxidant enzymes such as catalase (CAT), ascorbate peroxidase (APX), and superoxide dismutase (SOD) increased in both hybrids under drought conditions. Specifically, Vivek hybrid 9 showed 74% higher CAT activity under drought conditions as compared to the control. Additionally, EBR application further enhanced the activity of this enzyme by 23% compared to plants under drought conditions. Both hybrids experienced a significant reduction in plant girth due to drought stress. However, it was found that exogenously applying EBR reduced the detrimental effects of drought stress on the plant, and this effect was more pronounced in Bio 9637. In fact, Bio 9637 treated with EBR showed an 86% increase in proline content and a 70% increase in glycine betaine content compared to untreated plants under drought conditions. Taken together, our results suggested EBR enhanced tolerance to drought in maize hybrids. Hence, pre-anthesis foliar application of EBR might partly overcome the adverse effects of flowering stage drought in maize.
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Affiliation(s)
- Bicky Kumar
- Pusa Campus, ICAR-Indian Institute of Maize Research, New Delhi, India
- Pusa Campus, ICAR-Indian Agricultural Research Institute, New Delhi, Delhi, India
| | - Madan Pal
- Pusa Campus, ICAR-Indian Agricultural Research Institute, New Delhi, Delhi, India
| | - Pranjal Yadava
- Pusa Campus, ICAR-Indian Institute of Maize Research, New Delhi, India
- Pusa Campus, ICAR-Indian Agricultural Research Institute, New Delhi, Delhi, India
| | - Krishan Kumar
- Pusa Campus, ICAR-Indian Institute of Maize Research, New Delhi, India
| | - Sapna Langyan
- Pusa Campus, ICAR-Indian Institute of Maize Research, New Delhi, India
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | | | - Ishwar Singh
- Pusa Campus, ICAR-Indian Institute of Maize Research, New Delhi, India
- Crop Science Division, Indian Council of Agricultural Research, New Delhi, Delhi, India
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6
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Tavares CJ, Ribeiro Junior WQ, Ramos MLG, Pereira LF, Muller O, Casari RADCN, de Sousa CAF, da Silva AR. Water Stress Alters Physiological, Spectral, and Agronomic Indexes of Wheat Genotypes. PLANTS (BASEL, SWITZERLAND) 2023; 12:3571. [PMID: 37896034 PMCID: PMC10609785 DOI: 10.3390/plants12203571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/07/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023]
Abstract
Selecting drought-tolerant and more water-efficient wheat genotypes is a research priority, specifically in regions with irregular rainfall or areas where climate change is expected to result in reduced water availability. The objective of this work was to use high-throughput measurements with morphophysiological traits to characterize wheat genotypes in relation to water stress. Field experiments were conducted from May to September 2018 and 2019, using a sprinkler bar irrigation system to control water availability to eighteen wheat genotypes: BRS 254; BRS 264; CPAC 01019; CPAC 01047; CPAC 07258; CPAC 08318; CPAC 9110; BRS 394 (irrigated biotypes), and Aliança; BR 18_Terena; BRS 404; MGS Brilhante; PF 020037; PF 020062; PF 120337; PF 100368; PF 080492; and TBIO Sintonia (rainfed biotypes). The water regimes varied from 22 to 100% of the crop evapotranspiration replacement. Water stress negatively affected gas exchange, vegetation indices, and grain yield. High throughput variables TCARI, NDVI, OSAVI, SAVI, PRI, NDRE, and GNDVI had higher yield and morphophysiological measurement correlations. The drought resistance index indicated that genotypes Aliança, BRS 254, BRS 404, CPAC 01019, PF 020062, and PF 080492 were more drought tolerant.
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Affiliation(s)
- Cássio Jardim Tavares
- Federal Institute Goiano, Campus Cristalina (IF Goiano), Cristalina 73850-000, GO, Brazil;
| | | | | | | | - Onno Muller
- Institute for Bio-and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany;
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7
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Kovačević TK, Išić N, Major N, Krpan M, Ban D, Franić M, Goreta Ban S. The Interplay of Physiological and Biochemical Response to Short-Term Drought Exposure in Garlic ( Allium sativum L.). PLANTS (BASEL, SWITZERLAND) 2023; 12:3215. [PMID: 37765378 PMCID: PMC10536737 DOI: 10.3390/plants12183215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023]
Abstract
The impacts of global climate change and a rapid increase in population have emerged as major concerns threatening global food security. Environmental abiotic stress, such as drought, severely impairs plants' morphology, physiology, growth, and yield more than many other environmental factors. Plants use a complex set of physiological, biochemical, and molecular mechanisms to combat the negative effects caused by drought-induced stress. The aim of this study was to investigate morphological, spectral, physiological, and biochemical changes occurring in 30 garlic accessions exposed to short-term drought stress in a greenhouse setting and to identify potential early drought-induced stress markers. The results showed that, on average, garlic plants exposed to drought conditions exhibited a decrease in assimilation, transpiration, and stomatal conductance of 39%, 52%, and 50%, respectively, and an average increase in dry matter and proline content of 10.13% and 14.29%, respectively. Nevertheless, a significant interaction between the treatment and accessions was observed in the investigated photosynthetic and biochemical parameters. The plants' early response to drought ranged from mild to strong depending on garlic accession. Multivariate analysis showed that accessions with a mild early drought response were characterized by higher values of assimilation, transpiration, and stomatal conductance compared to plants with moderate or strong early drought response. Additionally, accessions with strong early drought response were characterized by higher proline content, lipid peroxidation, and antioxidant capacity as measured by FRAP compared to accessions with mild-to-moderate early drought response.
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Affiliation(s)
- Tvrtko Karlo Kovačević
- Department of Agriculture and Nutrition, Institute of Agriculture and Tourism, K. Hugues 8, 52440 Poreč, Croatia; (T.K.K.); (N.I.); (D.B.); (M.F.)
| | - Nina Išić
- Department of Agriculture and Nutrition, Institute of Agriculture and Tourism, K. Hugues 8, 52440 Poreč, Croatia; (T.K.K.); (N.I.); (D.B.); (M.F.)
| | - Nikola Major
- Department of Agriculture and Nutrition, Institute of Agriculture and Tourism, K. Hugues 8, 52440 Poreč, Croatia; (T.K.K.); (N.I.); (D.B.); (M.F.)
- Centre of Excellence for Biodiversity and Molecular Plant Breeding, Svetošimunska 1, 10000 Zagreb, Croatia
| | - Marina Krpan
- Department of Food Quality Control, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia;
| | - Dean Ban
- Department of Agriculture and Nutrition, Institute of Agriculture and Tourism, K. Hugues 8, 52440 Poreč, Croatia; (T.K.K.); (N.I.); (D.B.); (M.F.)
- Centre of Excellence for Biodiversity and Molecular Plant Breeding, Svetošimunska 1, 10000 Zagreb, Croatia
| | - Mario Franić
- Department of Agriculture and Nutrition, Institute of Agriculture and Tourism, K. Hugues 8, 52440 Poreč, Croatia; (T.K.K.); (N.I.); (D.B.); (M.F.)
- Centre of Excellence for Biodiversity and Molecular Plant Breeding, Svetošimunska 1, 10000 Zagreb, Croatia
| | - Smiljana Goreta Ban
- Department of Agriculture and Nutrition, Institute of Agriculture and Tourism, K. Hugues 8, 52440 Poreč, Croatia; (T.K.K.); (N.I.); (D.B.); (M.F.)
- Centre of Excellence for Biodiversity and Molecular Plant Breeding, Svetošimunska 1, 10000 Zagreb, Croatia
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8
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Carlier A, Dandrifosse S, Dumont B, Mercatoris B. To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status? PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0083. [PMID: 37681000 PMCID: PMC10482323 DOI: 10.34133/plantphenomics.0083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 08/03/2023] [Indexed: 09/09/2023]
Abstract
The utilization of high-throughput in-field phenotyping systems presents new opportunities for evaluating crop stress. However, existing studies have primarily focused on individual stresses, overlooking the fact that crops in field conditions frequently encounter multiple stresses, which can display similar symptoms or interfere with the detection of other stress factors. Therefore, this study aimed to investigate the impact of wheat yellow rust on reflectance measurements and nitrogen status assessment. A multi-sensor mobile platform was utilized to capture RGB and multispectral images throughout a 2-year fertilization-fungicide trial. To identify disease-induced damage, the SegVeg approach, which combines a U-NET architecture and a pixel-wise classifier, was applied to RGB images, generating a mask capable of distinguishing between healthy and damaged areas of the leaves. The observed proportion of damage in the images demonstrated similar effectiveness to visual scoring methods in explaining grain yield. Furthermore, the study discovered that the disease not only affected reflectance through leaf damage but also influenced the reflectance of healthy areas by disrupting the overall nitrogen status of the plants. This emphasizes the importance of incorporating disease impact into reflectance-based decision support tools to account for its effects on spectral data. This effect was successfully mitigated by employing the NDRE vegetation index calculated exclusively from the healthy portions of the leaves or by incorporating the proportion of damage into the model. However, these findings also highlight the necessity for further research specifically addressing the challenges presented by multiple stresses in crop phenotyping.
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Affiliation(s)
- Alexis Carlier
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
| | - Sebastien Dandrifosse
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
| | - Benjamin Dumont
- Plant Sciences, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
| | - Benoît Mercatoris
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
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9
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Gracia-Romero A, Vatter T, Kefauver SC, Rezzouk FZ, Segarra J, Nieto-Taladriz MT, Aparicio N, Araus JL. Defining durum wheat ideotypes adapted to Mediterranean environments through remote sensing traits. FRONTIERS IN PLANT SCIENCE 2023; 14:1254301. [PMID: 37731983 PMCID: PMC10508639 DOI: 10.3389/fpls.2023.1254301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 08/03/2023] [Indexed: 09/22/2023]
Abstract
An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differences in water status among cultivars were already accounted by the measurement of canopy temperature.
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Affiliation(s)
- Adrian Gracia-Romero
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Thomas Vatter
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Shawn C. Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Fatima Zahra Rezzouk
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Joel Segarra
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | | | - Nieves Aparicio
- Agro-technological Institute of Castilla y León (ITACyL), Valladolid, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
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10
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Bazhenov M, Litvinov D, Karlov G, Divashuk M. Evaluation of phosphate rock as the only source of phosphorus for the growth of tall and semi-dwarf durum wheat and rye plants using digital phenotyping. PeerJ 2023; 11:e15972. [PMID: 37663276 PMCID: PMC10473039 DOI: 10.7717/peerj.15972] [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: 03/03/2023] [Accepted: 08/06/2023] [Indexed: 09/05/2023] Open
Abstract
Background Phosphorus nutrition is important for obtaining high yields of crop plants. However, wheat plants are known to be almost incapable of taking up phosphorus from insoluble phosphate sources, and reduced height genes are supposed to decrease this ability further. Methods We performed a pot experiment using Triticum durum Desf. tall spring variety LD222, its near-isogenic semidwarf line carrying Rht17 (Reduced height 17) gene, and winter rye (Secale cereale L.) variety Chulpan. The individual plants were grown in quartz sand. The phosphorus was provided either as phosphate rock powder mixed with sand, or as monopotassium phosphate solution (normal nutrition control) or was not supplemented at all (no-phosphorus control). Other nutrients were provided in soluble form. During experiment the plants were assessed using the TraitFinder (Phenospex Ltd., Heerlen, Netherlands) digital phenotyping system for a standard set of parameters. Double scan with 90 degrees turns of pots around vertical axis vs. single scan were compared for accuracy of phenotyping. Results The phenotyping showed that at least 20 days of growth after seedling emergence were necessary to get stable differences between genotypes. After this initial period, phenotyping confirmed poor ability of wheat to grow on substrate with phosphate rock as the only source of phosphorus compared to rye; however, Rht17 did not cause an additional reduction in growth parameters other than plant height under this variant of substrate. The agreement between digital phenotyping and conventionally measured traits was at previously reported level for grasses (R2 = 0.85 and 0.88 for digital biomass and 3D leaf area vs. conventionally measured biomass and leaf area, single scan). Among vegetation indices, only the normalized differential vegetation index (NDVI) and the green leaf index (GLI) showed significant correlations with manually measured traits, including the percentage of dead leaves area. The double scan improved phenotyping accuracy, but not substantially.
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Affiliation(s)
- Mikhail Bazhenov
- Laboratory of Applied Genomics and Crop Breeding, All-Russia Research Institute of Agricultural Biotechnology, Moscow, Russia
| | - Dmitry Litvinov
- Kurchatov Genomics Center-ARRIAB, All-Russia Research Institute of Agricultural Biotechnology, Moscow, Russia
| | - Gennady Karlov
- Laboratory of Applied Genomics and Crop Breeding, All-Russia Research Institute of Agricultural Biotechnology, Moscow, Russia
| | - Mikhail Divashuk
- Laboratory of Applied Genomics and Crop Breeding, All-Russia Research Institute of Agricultural Biotechnology, Moscow, Russia
- Kurchatov Genomics Center-ARRIAB, All-Russia Research Institute of Agricultural Biotechnology, Moscow, Russia
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11
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Shrestha A, Bheemanahalli R, Adeli A, Samiappan S, Czarnecki JMP, McCraine CD, Reddy KR, Moorhead R. Phenological stage and vegetation index for predicting corn yield under rainfed environments. FRONTIERS IN PLANT SCIENCE 2023; 14:1168732. [PMID: 37546255 PMCID: PMC10401276 DOI: 10.3389/fpls.2023.1168732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 06/26/2023] [Indexed: 08/08/2023]
Abstract
Uncrewed aerial systems (UASs) provide high temporal and spatial resolution information for crop health monitoring and informed management decisions to improve yields. However, traditional in-season yield prediction methodologies are often inconsistent and inaccurate due to variations in soil types and environmental factors. This study aimed to identify the best phenological stage and vegetation index (VI) for estimating corn yield under rainfed conditions. Multispectral images were collected over three years (2020-2022) during the corn growing season and over fifty VIs were analyzed. In the three-year period, thirty-one VIs exhibited significant correlations (r ≥ 0.7) with yield. Sixteen VIs were significantly correlated with the yield at least for two years, and five VIs had a significant correlation with the yield for all three years. A strong correlation with yield was achieved by combining red, red edge, and near infrared-based indices. Further, combined correlation and random forest an alyses between yield and VIs led to the identification of consistent and highest predictive power VIs for corn yield prediction. Among them, leaf chlorophyll index, Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index and modified normalized difference at 705 were the most consistent predictors of corn yield when recorded around the reproductive stage (R1). This study demonstrated the dynamic nature of canopy reflectance and the importance of considering growth stages, and environmental conditions for accurate corn yield prediction.
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Affiliation(s)
- Amrit Shrestha
- Department of Agricultural & Biological Engineering, Mississippi State University, Mississippi State, MS, United States
| | - Raju Bheemanahalli
- Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS, United States
| | - Ardeshir Adeli
- United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS, United States
| | - Sathishkumar Samiappan
- Geosystems Research Institute, Mississippi State University, Mississippi State, MS, United States
| | - Joby M. Prince Czarnecki
- Geosystems Research Institute, Mississippi State University, Mississippi State, MS, United States
| | - Cary Daniel McCraine
- Geosystems Research Institute, Mississippi State University, Mississippi State, MS, United States
| | - K. Raja Reddy
- Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS, United States
| | - Robert Moorhead
- Geosystems Research Institute, Mississippi State University, Mississippi State, MS, United States
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12
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Roth L, Fossati D, Krähenbühl P, Walter A, Hund A. Image-based phenomic prediction can provide valuable decision support in wheat breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:162. [PMID: 37368140 DOI: 10.1007/s00122-023-04395-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 05/29/2023] [Indexed: 06/28/2023]
Abstract
KEY MESSAGE Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.
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Affiliation(s)
- Lukas Roth
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland.
| | | | - Patrick Krähenbühl
- Delley Samen und Pflanzen AG, Route de Portalban 40, 1567, Delley, Switzerland
| | - Achim Walter
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
| | - Andreas Hund
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
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13
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Anderegg J, Zenkl R, Walter A, Hund A, McDonald BA. Combining High-Resolution Imaging, Deep Learning, and Dynamic Modeling to Separate Disease and Senescence in Wheat Canopies. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0053. [PMID: 37363146 PMCID: PMC10287056 DOI: 10.34133/plantphenomics.0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/25/2023] [Indexed: 06/28/2023]
Abstract
Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling. Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions. Besides direct effects on green leaf area in terms of leaf damage, stressors often anticipate or accelerate physiological senescence, which may multiply their negative impact on grain filling. Here, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation. A vegetation segmentation model was trained using semisynthetic training data generated using image composition and generative adversarial neural networks, which greatly reduced the risk of annotation uncertainties and annotation effort. Application of the models to image time series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis. Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations (r ≈ 0.9). Contrasting patterns were observed for plots with different levels of foliar diseases, particularly septoria tritici blotch. Our results suggest that tracking the chlorotic and necrotic fractions separately may enable (a) a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and (b) investigation of interactions between biotic stress and physiological senescence. The high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.
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Affiliation(s)
- Jonas Anderegg
- Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Radek Zenkl
- Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Achim Walter
- Crop Science Group, Institute of Agricultural Sciences,
ETH Zurich, Zurich, Switzerland
| | - Andreas Hund
- Crop Science Group, Institute of Agricultural Sciences,
ETH Zurich, Zurich, Switzerland
| | - Bruce A. McDonald
- Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
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14
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Anderegg J, Kirchgessner N, Kronenberg L, McDonald BA. Automated Quantitative Measurement of Yellow Halos Suggests Activity of Necrotrophic Effectors in Septoria tritici Blotch. PHYTOPATHOLOGY 2022; 112:2560-2573. [PMID: 35793150 DOI: 10.1094/phyto-11-21-0465-r] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Many necrotrophic plant pathogens utilize host-selective toxins or necrotrophic effectors during the infection process. We hypothesized that the chlorotic yellow halos frequently observed around necrotic lesions caused by the wheat pathogen Zymoseptoria tritici could result from the activity of necrotrophic effectors interacting with the products of toxin sensitivity genes. As an initial step toward testing this hypothesis, we developed an automated image analysis (AIA) workflow that could quantify the degree of yellow halo formation occurring in wheat leaves naturally infected by a highly diverse pathogen population under field conditions. This AIA based on statistical learning was applied to more than 10,000 naturally infected leaves collected from 335 wheat cultivars grown in a replicated field experiment. We estimated a high heritability (h2 = 0.71) for the degree of yellow halo formation, suggesting that this quantitative trait has a significant genetic component. Using genome-wide association mapping, we identified six chromosome segments significantly associated with the yellow halo phenotype. Most of these segments contained candidate genes associated with targets of necrotrophic effectors in other necrotrophic pathogens. Our findings conform with the hypothesis that toxin sensitivity genes could account for a significant fraction of the observed variation in quantitative resistance to Septoria tritici blotch. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.
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Affiliation(s)
- Jonas Anderegg
- Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Norbert Kirchgessner
- Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Lukas Kronenberg
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Bruce A McDonald
- Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
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15
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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16
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Serouart M, Madec S, David E, Velumani K, Lopez Lozano R, Weiss M, Baret F. SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:9803570. [PMID: 36451876 PMCID: PMC9680505 DOI: 10.34133/2022/9803570] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 08/26/2022] [Indexed: 05/30/2023]
Abstract
Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest. We have developed the SegVeg approach for semantic segmentation of RGB images into three classes (background, green, and senescent vegetation). This is achieved in two steps: A U-net model is first trained on a very large dataset to separate whole vegetation from background. The green and senescent vegetation pixels are then separated using SVM, a shallow machine learning technique, trained over a selection of pixels extracted from images. The performances of the SegVeg approach is then compared to a 3-class U-net model trained using weak supervision over RGB images segmented with SegVeg as groundtruth masks. Results show that the SegVeg approach allows to segment accurately the three classes. However, some confusion is observed mainly between the background and senescent vegetation, particularly over the dark and bright regions of the images. The U-net model achieves similar performances, with slight degradation over the green vegetation: the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of U-net. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent. Finally, the models are used to predict the fraction of three classes over whole images or regularly spaced grid-pixels. Results show that green fraction is very well estimated (R 2 = 0.94) by the SegVeg model, while the senescent and background fractions show slightly degraded performances (R 2 = 0.70 and 0.73, respectively) with a mean 95% confidence error interval of 2.7% and 2.1% for the senescent vegetation and background, versus 1% for green vegetation. We have made SegVeg publicly available as a ready-to-use script and model, along with the entire annotated grid-pixels dataset. We thus hope to render segmentation accessible to a broad audience by requiring neither manual annotation nor knowledge or, at least, offering a pretrained model for more specific use.
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Affiliation(s)
- Mario Serouart
- Arvalis, Institut du végétal, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
- INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
| | - Simon Madec
- Arvalis, Institut du végétal, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
- CIRAD, UMR TETIS, F-34398 Montpellier, France
| | - Etienne David
- Arvalis, Institut du végétal, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
- INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
- Hiphen SAS, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
| | - Kaaviya Velumani
- Hiphen SAS, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
| | - Raul Lopez Lozano
- INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
| | - Marie Weiss
- INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
| | - Frédéric Baret
- INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l'aérodrome - CS 40509, 84914 Avignon Cedex 9, France
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Villesseche H, Ecarnot M, Ballini E, Bendoula R, Gorretta N, Roumet P. Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case. PLANT METHODS 2022; 18:100. [PMID: 35962438 PMCID: PMC9373489 DOI: 10.1186/s13007-022-00927-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND As a rapid and non-destructive method, Near Infrared Spectroscopy is classically proposed to assess plant traits in many scientific fields, to observe enlarged genotype panels and to document the temporal kinetic of some biological processes. Most often, supervised models are used. The signal is calibrated thanks to reference measurements, and dedicated models are generated to predict biological traits. An alternative unsupervised approach considers the whole spectra information in order to point out various matrix changes. Although more generic, and faster to implement, as it does not require a reference data set, this latter approach is rarely used to document biological processes, and does requires more information of the process. METHODS In our work, an unsupervised model was used to document the flag leaf senescence of durum wheat (Triticum turgidum durum). Leaf spectra changes were observed using Moving Window Principal Component Analysis (MWPCA). The dates related to earlier and later spectra changes were compared to two key points on the senescence time course: senescence onset (T0) and the end of the leaf span (T1) derived from a supervised strategy. RESULTS For almost all leaves and whatever the signal pre-treatments and window size considered, the MWPCA found significant spectral changes. The latter was highly correlated with T1 (0.59 ≤ r ≤ 0.86) whereas the correlations between the first significant spectrum changes and T0 were lower (0.09 ≤ r ≤ 0.56). These different relationships are discussed below since they define the potential as well as the limitations of MWPCA to model biological processes. CONCLUSION Overall, our study demonstrates that the information contained in the spectra can be used when applying an unsupervised method, here the MWPCA, to characterize a complex biological phenomenon such leaf senescence. It also means that using whole spectra may be relevant in agriculture and plant biology.
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Affiliation(s)
| | - Martin Ecarnot
- AGAP, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France
| | - Elsa Ballini
- PHIM, CIRAD, INRAE, IRD, Institut Agro, Univ Montpellier, Montpellier, France
| | - Ryad Bendoula
- ITAP, INRAE, Institut Agro, Univ Montpellier, Montpellier, France
| | - Nathalie Gorretta
- AGAP, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France
| | - Pierre Roumet
- AGAP, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France
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A First Approach to Determine If It Is Possible to Delineate In-Season N Fertilization Maps for Wheat Using NDVI Derived from Sentinel-2. REMOTE SENSING 2022. [DOI: 10.3390/rs14122872] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Adjusting nitrogen fertilization to the nutritional requirements of crops is one of the major challenges of modern agriculture. The amount of N needed is mainly determined by crop yield, so yield maps can be used to optimize N fertilization. As the adoption of yield monitors is low among farmers, implementation of this approach is still low. However, as the Normalized Difference Vegetation Index (NDVI) is related to grain yield, the main objective of this work was to identify at which wheat growth stage a moderate agreement between NDVI and yield is obtained. For this, NDVI images obtained from Sentinel-2 were used, and the evolution of concordance was analyzed in 13 classified parcels of wheat employing the Kappa index (KI). In one-third of the plots, a moderate agreement (KI > 0.4) was reached before the stem elongation growth phase (when the last N application was made). In another one-third, moderate agreement was reached later, in more advanced development stages. For the cases in which this agreement did not exist, an attempt was made to find the causes. The MANOVA and subsequent descriptive discriminant analysis (DDA) showed that the NDVI dates that contribute the most to the differentiation between plots with and without agreement between grain yield maps and NDVI images were those corresponding to tillering. The sum of the NDVI values of the tillering phase was significantly lower in the group of plots that did not show concordance. Sentinel-2 imagery was successful on 66% of plots for delineation of management zones after GS 30, and thus is useful for producing fertilization maps for the upcoming season. However, to produce in-season fertilization maps, further studies are needed to better understand the mechanisms that regulate the relation between yield and NDVI at early growth stages (<GS 30).
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Spectral Reflectance Indices as a High Throughput Selection Tool in a Sesame Breeding Scheme. REMOTE SENSING 2022. [DOI: 10.3390/rs14112629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
On-farm genotype screening is at the core of every breeding scheme, but it comes with a high cost and often high degree of uncertainty. Phenomics is a new approach by plant breeders, who use optical sensors for accurate germplasm phenotyping, selection and enhancement of the genetic gain. The objectives of this study were to: (1) develop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor; (2) test the ability of spectral reflectance indices (SRIs) to distinguish between sesame genotypes throughout the crop growth period; and (3) identify specific stages in the sesame growth cycle that contribute to phenotyping accuracy and functionality and evaluate the efficiency of SRIs as a selection tool. A diversity panel of 24 sesame genotypes was grown at normal and late planting dates in 2020 and 2021. To determine the SRIs the Crop Circle ACS-430 active crop canopy sensor was used from the beginning of the sesame reproductive stage to the end of the ripening stage. NDVI and NDRE reached about the same high accuracy in genotype phenotyping, even under dense biomass conditions where “saturation” problems were expected. NDVI produced higher broad-sense heritability (max 0.928) and NDRE higher phenotypic and genotypic correlation with the yield (max 0.593 and 0.748, respectively). NDRE had the highest relative efficiency (61%) as an indirect selection index to yield direct selection. Both SRIs had optimal results when the monitoring took place at the end of the reproductive stage and the beginning of the ripening stage. Thus, an active canopy sensor as this study demonstrated can assist breeders to differentiate and classify sesame genotypes.
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Differentiation of Six Grassland/Forage Types in Three Canadian Ecoregions Based on Spectral Characteristics. REMOTE SENSING 2022. [DOI: 10.3390/rs14092121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Differentiation of grassland/forage types and accurate estimates of their location and extent are important for understanding their ecological processes and for applying appropriate management practices. We are aiming to reveal the different spectral characteristics of six grassland/forage land covers in three ecoregions located in the Canadian Prairies, based on field data and satellite images. Three spectral indices representing productivity (Normalized Difference Vegetation Index (NDVI)), moisture content (Normalized Difference Moisture Index (NDMI)), and plant photosynthetic activity (Plant Senescence Reflectance Index (PSRI)) were used for comparison of means, comparison of coefficient of variation (CV), and analysis of variance (ANOVA). The results indicated that different grassland types show distinguishable spectral characteristics in the Moist-Mixed and Mixed Ecoregions, while it was not possible to differentiate the classes in the Fescue Ecoregion. To further investigate the within-sites and between-sites heterogeneity, we calculated the CV in a 3 × 3 window and placed them in comparative triangles to demonstrate their potential separability. Results indicated that the triangles based on the CV offered greater class separability in the Fescue Ecoregion and in the Mixed Ecoregion.
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21
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Royimani L, Mutanga O, Odindi J, Sibanda M, Chamane S. Determining the onset of autumn grass senescence in subtropical sour-veld grasslands using remote sensing proxies and the breakpoint approach. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101651] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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22
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Stoy PC, Khan AM, Wipf A, Silverman N, Powell SL. The spatial variability of NDVI within a wheat field: Information content and implications for yield and grain protein monitoring. PLoS One 2022; 17:e0265243. [PMID: 35316290 PMCID: PMC8939815 DOI: 10.1371/journal.pone.0265243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 02/24/2022] [Indexed: 12/05/2022] Open
Abstract
Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.
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Affiliation(s)
- Paul C. Stoy
- Department of Biological Systems Engineering, University of Wisconsin–Madison, Madison, WI, United States of America
- Nelson Institute for Environmental Studies, University of Wisconsin–Madison, Madison, WI, United States of America
- * E-mail:
| | - Anam M. Khan
- Nelson Institute for Environmental Studies, University of Wisconsin–Madison, Madison, WI, United States of America
| | - Aaron Wipf
- Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, United States of America
| | - Nick Silverman
- Adaptive Hydrology LLC, Missoula, MT, United States of America
| | - Scott L. Powell
- Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, United States of America
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23
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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] [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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Grieco M, Schmidt M, Warnemünde S, Backhaus A, Klück HC, Garibay A, Tandrón Moya YA, Jozefowicz AM, Mock HP, Seiffert U, Maurer A, Pillen K. Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 315:111123. [PMID: 35067296 DOI: 10.1016/j.plantsci.2021.111123] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/24/2021] [Accepted: 11/16/2021] [Indexed: 06/14/2023]
Abstract
Biofortification, the enrichment of nutrients in crop plants, is of increasing importance to improve human health. The wild barley nested association mapping (NAM) population HEB-25 was developed to improve agronomic traits including nutrient concentration. Here, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients, sampled from two field experiments and four developmental stages. Particularly accurate predictions were obtained by partial least squares regression (PLS) modeling of leaf concentrations for N, P and K reaching coefficients of determination of 0.90, 0.75 and 0.89, respectively. We recognized nutrient-specific patterns of variation of leaf nutrient concentration between developmental stages. A number of quantitative trait loci (QTL) associated with the simultaneous expression of leaf nutrients were detected, indicating their potential co-regulation in barley. For example, the wild barley allele of QTL-4H-1 simultaneously increased leaf concentration of N, P, K and Cu. Similar effects of the same QTL were previously reported for nutrient concentrations in grains, supporting a potential parallel regulation of N, P, K and Cu in leaves and grains of HEB-25. Our study provides a new approach for nutrient assessment in large-scale field experiments to ultimately select genes and genotypes supporting plant biofortification.
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Affiliation(s)
- Michele Grieco
- Martin-Luther-University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120, Halle, Germany
| | - Maria Schmidt
- Martin-Luther-University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120, Halle, Germany
| | - Sebastian Warnemünde
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstraße 22, 39106, Magdeburg, Germany
| | - Andreas Backhaus
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstraße 22, 39106, Magdeburg, Germany
| | - Hans-Christian Klück
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstraße 22, 39106, Magdeburg, Germany
| | - Adriana Garibay
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Physiology and Cell Biology, Corrensstraße 3, 06466, Seeland OT, Gatersleben, Germany
| | - Yudelsy Antonia Tandrón Moya
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Physiology and Cell Biology, Corrensstraße 3, 06466, Seeland OT, Gatersleben, Germany
| | - Anna Maria Jozefowicz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Physiology and Cell Biology, Corrensstraße 3, 06466, Seeland OT, Gatersleben, Germany
| | - Hans-Peter Mock
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Physiology and Cell Biology, Corrensstraße 3, 06466, Seeland OT, Gatersleben, Germany
| | - Udo Seiffert
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstraße 22, 39106, Magdeburg, Germany
| | - Andreas Maurer
- Martin-Luther-University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120, Halle, Germany
| | - Klaus Pillen
- Martin-Luther-University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120, Halle, Germany.
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25
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Carlier A, Dandrifosse S, Dumont B, Mercatoris B. Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:9841985. [PMID: 35169713 PMCID: PMC8817947 DOI: 10.34133/2022/9841985] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 12/24/2021] [Indexed: 05/05/2023]
Abstract
The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs. Recent deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of conditions. However, they remain complicated to implement and necessitate a huge training database. This paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage. The tested method was based on superpixel classification exploiting features from RGB and multispectral cameras. Three classifiers were trained with wheat images acquired from heading to maturity on two cultivars at different levels of fertilizer. The best classifier, the support vector machine (SVM), yielded satisfactory segmentation and reached 94% accuracy. However, the segmentation at the pixel level could not be assessed only by the superpixel classification accuracy. For this reason, a second assessment method was proposed to consider the entire process. A simple graphical tool was developed to annotate pixels. The strategy was to annotate a few pixels per image to be able to quickly annotate the entire image set, and thus account for very diverse conditions. Results showed a lesser segmentation score (F1-score) for the heading and flowering stages and for the zero nitrogen input object. The methodology appeared appropriate for further work on the growth dynamics of the different wheat organs and in the frame of other segmentation challenges.
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Affiliation(s)
- Alexis Carlier
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Sébastien Dandrifosse
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Benjamin Dumont
- Plant Sciences, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Benoît Mercatoris
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
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Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding. REMOTE SENSING 2021. [DOI: 10.3390/rs13142670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.
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Langridge P, Reynolds M. Breeding for drought and heat tolerance in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1753-1769. [PMID: 33715017 DOI: 10.1007/s00122-021-03795-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/16/2021] [Indexed: 05/02/2023]
Abstract
Many approaches have been adopted to enhance the heat and drought tolerance of wheat with mixed success. An assessment of the relative merits of different strategies is presented. Wheat is the most widely grown crop globally and plays a key role in human nutrition. However, it is grown in environments that are prone to heat and drought stress, resulting in severely reduced yield in some seasons. Increased climate variability is expected to have a particularly adverse effect of wheat production. Breeding for stable yield across both good and bad seasons while maintaining high yield under optimal conditions is a high priority for most wheat breeding programs and has been a focus of research activities. Multiple strategies have been explored to enhance the heat and drought tolerance of wheat including extensive genetic analysis and modify the expression of genes involved in stress responses, targeting specific physiological traits and direct selection under a range of stress scenarios. These approaches have been combined with improvements in phenotyping, the development of genetic and genomic resources, and extended screening and analysis techniques. The results have greatly expanded our knowledge and understanding of the factors that influence yield under stress, but not all have delivered the hoped-for progress. Here, we provide an overview of the different strategies and an assessment of the most promising approaches.
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Affiliation(s)
- Peter Langridge
- School of Agriculture Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia.
- Wheat Initiative, Julius-Kühn-Institute, 14195, Berlin, Germany.
| | - Matthew Reynolds
- International Maize and Wheat Improvement Centre (CIMMYT), Int. AP 6-641, 06600, Mexico, D.F., Mexico
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Smith DT, Potgieter AB, Chapman SC. Scaling up high-throughput phenotyping for abiotic stress selection in the field. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1845-1866. [PMID: 34076731 DOI: 10.1007/s00122-021-03864-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/13/2021] [Indexed: 05/18/2023]
Abstract
High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.
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Affiliation(s)
- Daniel T Smith
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Andries B Potgieter
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Scott C Chapman
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
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Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy). REMOTE SENSING 2021. [DOI: 10.3390/rs13081530] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
On the 29th of October 2018, a storm named “Vaia” hit North-Eastern Italy, causing the loss of 8 million m3 of standing trees and creating serious damage to the forested areas, with many economic and ecological implications. This event brought up the necessity of a standard procedure for windthrow detection and monitoring based on satellite data as an alternative to foresters’ fieldwork. The proposed methodology was applied in Carnic Alps (Friuli Venezia Giulia, NE Italy) in natural stands dominated by Picea abies and Abies alba. We used images from the Sentinel-2 mission: 1) to test vegetation indices performance in monitoring the vegetation dynamics in the short period after the storm, and 2) to create a windthrow map for the whole Friuli Venezia Giulia region. Results showed that windthrows in forests have a significant influence on visible and short-wave infrared (SWIR) spectral bands of Sentinel-2, both in the short and the long-term timeframes. NDWI8A and NDWI were the best indices for windthrow detection (R2 = 0.80 and 0.77, respectively) and NDVI, PSRI, SAVI and GNDVI had an overall good performance in spotting wind-damaged areas (R2 = 0.60–0.76). Moreover, these indices allowed to monitor post-Vaia forest die-off and showed a dynamic recovery process in cleaned sites. The NDWI8A index, employed in the vegetation index differencing (VID) change detection technique, delimited damaged areas comparable to the estimations provided by Regional Forest System (2545 ha and 3183 ha, respectively). Damaged forests detected by NDWI8A VID ranged from 500 m to 1500 m a.s.l., mainly covering steep slopes in the south and east aspects (42% and 25%, respectively). Our results suggested that the NDWI8A VID method may be a cost-effective and accurate way to produce windthrow maps, which could limit the risks associated with fieldwork and may provide a valuable tool to plan tree removal interventions in a more efficient way.
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Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping. REMOTE SENSING 2021. [DOI: 10.3390/rs13061144] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.
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Kronenberg L, Yates S, Boer MP, Kirchgessner N, Walter A, Hund A. Temperature response of wheat affects final height and the timing of stem elongation under field conditions. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:700-717. [PMID: 33057698 PMCID: PMC7853599 DOI: 10.1093/jxb/eraa471] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/10/2020] [Indexed: 05/18/2023]
Abstract
In wheat, temperature affects the timing and intensity of stem elongation. Genetic variation for this process is therefore important for adaptation. This study investigates the genetic response to temperature fluctuations during stem elongation and its relationship to phenology and height. Canopy height of 315 wheat genotypes (GABI wheat panel) was scanned twice weekly in the field phenotyping platform (FIP) of ETH Zurich using a LIDAR. Temperature response was modelled using linear regressions between stem elongation and mean temperature in each measurement interval. This led to a temperature-responsive (slope) and a temperature-irresponsive (intercept) component. The temperature response was highly heritable (H2=0.81) and positively related to a later start and end of stem elongation as well as final height. Genome-wide association mapping revealed three temperature-responsive and four temperature-irresponsive quantitative trait loci (QTLs). Furthermore, putative candidate genes for temperature-responsive QTLs were frequently related to the flowering pathway in Arabidopsis thaliana, whereas temperature-irresponsive QTLs corresponded to growth and reduced height genes. In combination with Rht and Ppd alleles, these loci, together with the loci for the timing of stem elongation, accounted for 71% of the variability in height. This demonstrates how high-throughput field phenotyping combined with environmental covariates can contribute to a smarter selection of climate-resilient crops.
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Affiliation(s)
- Lukas Kronenberg
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Steven Yates
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Martin P Boer
- Biometris, Wageningen University & Research, PB Wageningen, The Netherlands
| | - Norbert Kirchgessner
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Achim Walter
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Andreas Hund
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
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32
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Kronenberg L, Yates S, Boer MP, Kirchgessner N, Walter A, Hund A. Temperature response of wheat affects final height and the timing of stem elongation under field conditions. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:700-717. [PMID: 33057698 DOI: 10.1101/756700] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/10/2020] [Indexed: 05/29/2023]
Abstract
In wheat, temperature affects the timing and intensity of stem elongation. Genetic variation for this process is therefore important for adaptation. This study investigates the genetic response to temperature fluctuations during stem elongation and its relationship to phenology and height. Canopy height of 315 wheat genotypes (GABI wheat panel) was scanned twice weekly in the field phenotyping platform (FIP) of ETH Zurich using a LIDAR. Temperature response was modelled using linear regressions between stem elongation and mean temperature in each measurement interval. This led to a temperature-responsive (slope) and a temperature-irresponsive (intercept) component. The temperature response was highly heritable (H2=0.81) and positively related to a later start and end of stem elongation as well as final height. Genome-wide association mapping revealed three temperature-responsive and four temperature-irresponsive quantitative trait loci (QTLs). Furthermore, putative candidate genes for temperature-responsive QTLs were frequently related to the flowering pathway in Arabidopsis thaliana, whereas temperature-irresponsive QTLs corresponded to growth and reduced height genes. In combination with Rht and Ppd alleles, these loci, together with the loci for the timing of stem elongation, accounted for 71% of the variability in height. This demonstrates how high-throughput field phenotyping combined with environmental covariates can contribute to a smarter selection of climate-resilient crops.
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Affiliation(s)
- Lukas Kronenberg
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Steven Yates
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Martin P Boer
- Biometris, Wageningen University & Research, PB Wageningen, The Netherlands
| | - Norbert Kirchgessner
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Achim Walter
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Andreas Hund
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
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33
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Deery DM, Jones HG. Field Phenomics: Will It Enable Crop Improvement? PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9871989. [PMID: 34549194 PMCID: PMC8433881 DOI: 10.34133/2021/9871989] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 08/14/2021] [Indexed: 05/19/2023]
Abstract
Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders' needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders.
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Affiliation(s)
| | - Hamlyn G. Jones
- CSIRO Agriculture and Food, Canberra, ACT, Australia
- Division of Plant Sciences, University of Dundee, UK
- School of Agriculture and Environment, University of Western Australia, Australia
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34
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Togeiro de Alckmin G, Kooistra L, Rawnsley R, de Bruin S, Lucieer A. Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation. SENSORS (BASEL, SWITZERLAND) 2020; 20:E7192. [PMID: 33333952 PMCID: PMC7765461 DOI: 10.3390/s20247192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/10/2020] [Accepted: 12/10/2020] [Indexed: 11/21/2022]
Abstract
The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.
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Affiliation(s)
- Gustavo Togeiro de Alckmin
- School of Technology, Environments & Design, University of Tasmania-Geography and Spatial Sciences, Hobart, TAS 7001, Australia; (G.T.d.A.); (A.L.)
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands;
| | - Lammert Kooistra
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands;
| | - Richard Rawnsley
- Centre for Dairy, Grains and Grazing, Tasmanian Institute of Agriculture, Cradle Coast Campus, 16–20 Mooreville Road, Burnie, TAS 7320, Australia;
| | - Sytze de Bruin
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands;
| | - Arko Lucieer
- School of Technology, Environments & Design, University of Tasmania-Geography and Spatial Sciences, Hobart, TAS 7001, Australia; (G.T.d.A.); (A.L.)
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35
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David E, Madec S, Sadeghi-Tehran P, Aasen H, Zheng B, Liu S, Kirchgessner N, Ishikawa G, Nagasawa K, Badhon MA, Pozniak C, de Solan B, Hund A, Chapman SC, Baret F, Stavness I, Guo W. Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:3521852. [PMID: 33313551 PMCID: PMC7706323 DOI: 10.34133/2020/3521852] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 07/01/2020] [Indexed: 05/19/2023]
Abstract
The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.
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Affiliation(s)
- Etienne David
- Arvalis, Institut du végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
| | - Simon Madec
- Arvalis, Institut du végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
| | | | - Helge Aasen
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Bangyou Zheng
- CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, 4067 QLD, Australia
| | - Shouyang Liu
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
| | - Norbert Kirchgessner
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Goro Ishikawa
- Institute of Crop Science, National Agriculture and Food Research Organization, Japan
| | - Koichi Nagasawa
- Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization, Japan
| | | | - Curtis Pozniak
- Department of Plant Sciences, University of Saskatchewan, Canada
| | - Benoit de Solan
- Arvalis, Institut du végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
| | - Andreas Hund
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Scott C. Chapman
- CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, 4067 QLD, Australia
- School of Food and Agricultural Sciences, The University of Queensland, Gatton, 4343 QLD, Australia
| | - Frédéric Baret
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
| | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Canada
| | - Wei Guo
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
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36
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Aasen H, Kirchgessner N, Walter A, Liebisch F. PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits. FRONTIERS IN PLANT SCIENCE 2020; 11:593. [PMID: 32625216 PMCID: PMC7314959 DOI: 10.3389/fpls.2020.00593] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/20/2020] [Indexed: 05/19/2023]
Abstract
Understanding the interaction of plant growth with environmental conditions is crucial to increase the resilience of current cropping systems to a changing climate. Here, we investigate PhenoCams as a high-throughput approach for field phenotyping experiments to assess growth dynamics of many different genotypes simultaneously in high temporal (daily) resolution. First, we develop a method that extracts a daily phenological signal that is normalized for the different viewing geometries of the pixels within the images. Second, we investigate the extraction of the in season traits of early vigor, leaf area index (LAI), and senescence dynamic from images of a soybean (Glycine max) field phenotyping experiment and show that it is possible to rate early vigor, senescence dynamics, and track the LAI development between LAI 1 and 4.5. Third, we identify the start of green up, green peak, senescence peak, and end of senescence in the phenological signal. Fourth, we extract the timing of these points and show how this information can be used to assess the impact of phenology on harvest traits (yield, thousand kernel weight, and oil content). The results demonstrate that PhenoCams can track growth dynamics and fill the gap of high temporal monitoring in field phenotyping experiments.
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Affiliation(s)
- Helge Aasen
- Group of Crop Science, Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Norbert Kirchgessner
- Group of Crop Science, Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Achim Walter
- Group of Crop Science, Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
| | - Frank Liebisch
- Group of Crop Science, Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland
- Water Protection and Substance Flows, Research Division Agroecology and Environment, Agroscope, Zurich, Switzerland
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