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Einspanier S, Tominello-Ramirez C, Hasler M, Barbacci A, Raffaele S, Stam R. High-Resolution Disease Phenotyping Reveals Distinct Resistance Mechanisms of Tomato Crop Wild Relatives against Sclerotinia sclerotiorum. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0214. [PMID: 39105186 PMCID: PMC11298253 DOI: 10.34133/plantphenomics.0214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 06/19/2024] [Indexed: 08/07/2024]
Abstract
Besides the well-understood qualitative disease resistance, plants possess a more complex quantitative form of resistance: quantitative disease resistance (QDR). QDR is commonly defined as a partial but more durable form of resistance and, therefore, might display a valuable target for resistance breeding. The characterization of QDR phenotypes, especially of wild crop relatives, displays a bottleneck in deciphering QDR's genomic and regulatory background. Moreover, the relationship between QDR parameters, such as infection frequency, lag-phase duration, and lesion growth rate, remains elusive. High hurdles for applying modern phenotyping technology, such as the low availability of phenotyping facilities or complex data analysis, further dampen progress in understanding QDR. Here, we applied a low-cost (<1.000 €) phenotyping system to measure lesion growth dynamics of wild tomato species (e.g., Solanum pennellii or Solanum pimpinellifolium). We provide insight into QDR diversity of wild populations and derive specific QDR mechanisms and their cross-talk. We show how temporally continuous observations are required to dissect end-point severity into functional resistance mechanisms. The results of our study show how QDR can be maintained by facilitating different defense mechanisms during host-parasite interaction and that the capacity of the QDR toolbox highly depends on the host's genetic context. We anticipate that the present findings display a valuable resource for more targeted functional characterization of the processes involved in QDR. Moreover, we show how modest phenotyping technology can be leveraged to help answer highly relevant biological questions.
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Affiliation(s)
- Severin Einspanier
- Department of Phytopathology and Crop Protection, Institute of Phytopathology, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, 24118 Kiel, Germany
| | - Christopher Tominello-Ramirez
- Department of Phytopathology and Crop Protection, Institute of Phytopathology, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, 24118 Kiel, Germany
| | - Mario Hasler
- Lehrfach Variationsstatistik, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, Kiel, 24118 Kiel, Germany
| | - Adelin Barbacci
- Laboratoire des Interactions Plantes Microorganismes Environnement (LIPME), INRAE, CNRS, Castanet Tolosan Cedex, France
| | - Sylvain Raffaele
- Laboratoire des Interactions Plantes Microorganismes Environnement (LIPME), INRAE, CNRS, Castanet Tolosan Cedex, France
| | - Remco Stam
- Department of Phytopathology and Crop Protection, Institute of Phytopathology, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, 24118 Kiel, Germany
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Murphy KM, Ludwig E, Gutierrez J, Gehan MA. Deep Learning in Image-Based Plant Phenotyping. ANNUAL REVIEW OF PLANT BIOLOGY 2024; 75:771-795. [PMID: 38382904 DOI: 10.1146/annurev-arplant-070523-042828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly and efficiently. Image-based, high-throughput phenotyping has a number of advantages because it is nondestructive and reduces human labor, but a new challenge arises in extracting meaningful information from large quantities of image data. Deep learning, a type of artificial intelligence, is an approach used to analyze image data and make predictions on unseen images that ultimately reduces the need for human input in computation. Here, we review the basics of deep learning, assessments of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.
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Affiliation(s)
| | - Ella Ludwig
- Donald Danforth Plant Science Center, St. Louis, Missouri, USA;
| | - Jorge Gutierrez
- Donald Danforth Plant Science Center, St. Louis, Missouri, USA;
| | - Malia A Gehan
- Donald Danforth Plant Science Center, St. Louis, Missouri, USA;
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3
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Mehta D, Scandola S, Kennedy C, Lummer C, Gallo MCR, Grubb LE, Tan M, Scarpella E, Uhrig RG. Twilight length alters growth and flowering time in Arabidopsis via LHY/ CCA1. SCIENCE ADVANCES 2024; 10:eadl3199. [PMID: 38941453 PMCID: PMC11212724 DOI: 10.1126/sciadv.adl3199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 05/28/2024] [Indexed: 06/30/2024]
Abstract
Decades of research have uncovered how plants respond to two environmental variables that change across latitudes and over seasons: photoperiod and temperature. However, a third such variable, twilight length, has so far gone unstudied. Here, using controlled growth setups, we show that the duration of twilight affects growth and flowering time via the LHY/CCA1 clock genes in the model plant Arabidopsis. Using a series of progressively truncated no-twilight photoperiods, we also found that plants are more sensitive to twilight length compared to equivalent changes in solely photoperiods. Transcriptome and proteome analyses showed that twilight length affects reactive oxygen species metabolism, photosynthesis, and carbon metabolism. Genetic analyses suggested a twilight sensing pathway from the photoreceptors PHY E, PHY B, PHY D, and CRY2 through LHY/CCA1 to flowering modulation through the GI-FT pathway. Overall, our findings call for more nuanced models of day-length perception in plants and posit that twilight is an important determinant of plant growth and development.
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Affiliation(s)
- Devang Mehta
- Department of Biosystems, KU Leuven, B-3001 Leuven, Belgium
- Leuven Plant Institute, KU Leuven, B-3001 Leuven, Belgium
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Sabine Scandola
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Curtis Kennedy
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Christina Lummer
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | | | - Lauren E. Grubb
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Maryalle Tan
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Enrico Scarpella
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - R. Glen Uhrig
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Department of Biochemistry, University of Alberta, Edmonton, AB T6G 2E9, Canada
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Rankenberg T, van Veen H, Sedaghatmehr M, Liao CY, Devaiah MB, Stouten EA, Balazadeh S, Sasidharan R. Differential leaf flooding resilience in Arabidopsis thaliana is controlled by ethylene signaling-activated and age-dependent phosphorylation of ORESARA1. PLANT COMMUNICATIONS 2024; 5:100848. [PMID: 38379284 PMCID: PMC11211547 DOI: 10.1016/j.xplc.2024.100848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/19/2024] [Accepted: 02/18/2024] [Indexed: 02/22/2024]
Abstract
The phytohormone ethylene is a major regulator of plant adaptive responses to flooding. In flooded plant tissues, ethylene quickly increases to high concentrations owing to its low solubility and diffusion rates in water. Ethylene accumulation in submerged plant tissues makes it a reliable cue for triggering flood acclimation responses, including metabolic adjustments to cope with flood-induced hypoxia. However, persistent ethylene accumulation also accelerates leaf senescence. Stress-induced senescence hampers photosynthetic capacity and stress recovery. In submerged Arabidopsis, senescence follows a strict age-dependent pattern starting with the older leaves. Although mechanisms underlying ethylene-mediated senescence have been uncovered, it is unclear how submerged plants avoid indiscriminate breakdown of leaves despite high systemic ethylene accumulation. We demonstrate that although submergence triggers leaf-age-independent activation of ethylene signaling via EIN3 in Arabidopsis, senescence is initiated only in old leaves. EIN3 stabilization also leads to overall transcript and protein accumulation of the senescence-promoting transcription factor ORESARA1 (ORE1) in both old and young leaves during submergence. However, leaf-age-dependent senescence can be explained by ORE1 protein activation via phosphorylation specifically in old leaves, independent of the previously identified age-dependent control of ORE1 via miR164. A systematic analysis of the roles of the major flooding stress cues and signaling pathways shows that only the combination of ethylene and darkness is sufficient to mimic submergence-induced senescence involving ORE1 accumulation and phosphorylation. Hypoxia, most often associated with flooding stress in plants, appears to have no role in these processes. Our results reveal a mechanism by which plants regulate the speed and pattern of senescence during environmental stresses such as flooding. Age-dependent ORE1 activity ensures that older, expendable leaves are dismantled first, thus prolonging the life of younger leaves and meristematic tissues that are vital to whole-plant survival.
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Affiliation(s)
- Tom Rankenberg
- Plant Stress Resilience, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Hans van Veen
- Plant Stress Resilience, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands; Evolutionary Plant-Ecophysiology, Groningen Institute for Evolutionary LIfe Sciences, Nijenborgh 7, 9747 AG Groningen, the Netherlands
| | - Mastoureh Sedaghatmehr
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam, Germany
| | - Che-Yang Liao
- Experimental and Computational Plant Development, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Muthanna Biddanda Devaiah
- Experimental and Computational Plant Development, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Evelien A Stouten
- Plant Stress Resilience, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | | | - Rashmi Sasidharan
- Plant Stress Resilience, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands.
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Agnew E, Ziegler G, Lee S, Lizárraga C, Fahlgren N, Baxter I, Mockler TC, Shakoor N. Longitudinal genome-wide association study reveals early QTL that predict biomass accumulation under cold stress in sorghum. FRONTIERS IN PLANT SCIENCE 2024; 15:1278802. [PMID: 38807776 PMCID: PMC11130433 DOI: 10.3389/fpls.2024.1278802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/24/2024] [Indexed: 05/30/2024]
Abstract
Introduction Sorghum bicolor is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. Methods We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. Results The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. Discussion The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.
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Affiliation(s)
| | | | | | | | | | | | | | - Nadia Shakoor
- Donald Danforth Plant Science Center, Saint Louis, MO, United States
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Jadhav Y, Thakur NR, Ingle KP, Ceasar SA. The role of phenomics and genomics in delineating the genetic basis of complex traits in millets. PHYSIOLOGIA PLANTARUM 2024; 176:e14349. [PMID: 38783512 DOI: 10.1111/ppl.14349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/25/2024]
Abstract
Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.
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Affiliation(s)
- Yashoda Jadhav
- International Crops Research Institutes for the Semi-Arid Tropics, Patancheru, TS, India
| | - Niranjan Ravindra Thakur
- International Crops Research Institutes for the Semi-Arid Tropics, Patancheru, TS, India
- Vasantrao Naik Marathwada Agricultural University, Parbhani, MS, India
| | | | - Stanislaus Antony Ceasar
- Division of Plant Molecular Biology and Biotechnology, Department of Biosciences, Rajagiri College of Social Sciences, Kochi, KL, India
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Ludwig E, Sumner J, Berry J, Polydore S, Ficor T, Agnew E, Haines K, Greenham K, Fahlgren N, Mockler TC, Gehan MA. Natural variation in Brachypodium distachyon responses to combined abiotic stresses. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:1676-1701. [PMID: 37483133 DOI: 10.1111/tpj.16387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 06/26/2023] [Accepted: 07/05/2023] [Indexed: 07/25/2023]
Abstract
The demand for agricultural production is becoming more challenging as climate change increases global temperature and the frequency of extreme weather events. This study examines the phenotypic variation of 149 accessions of Brachypodium distachyon under drought, heat, and the combination of stresses. Heat alone causes the largest amounts of tissue damage while the combination of stresses causes the largest decrease in biomass compared to other treatments. Notably, Bd21-0, the reference line for B. distachyon, did not have robust growth under stress conditions, especially the heat and combined drought and heat treatments. The climate of origin was significantly associated with B. distachyon responses to the assessed stress conditions. Additionally, a GWAS found loci associated with changes in plant height and the amount of damaged tissue under stress. Some of these SNPs were closely located to genes known to be involved in responses to abiotic stresses and point to potential causative loci in plant stress response. However, SNPs found to be significantly associated with a response to heat or drought individually are not also significantly associated with the combination of stresses. This, with the phenotypic data, suggests that the effects of these abiotic stresses are not simply additive, and the responses to the combined stresses differ from drought and heat alone.
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Affiliation(s)
- Ella Ludwig
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Joshua Sumner
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Jeffrey Berry
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
- Bayer Crop Sciences, St. Louis, Missouri, 63017, USA
| | - Seth Polydore
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Tracy Ficor
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Erica Agnew
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Kristina Haines
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Kathleen Greenham
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
- University of Minnesota, St. Paul, Minnesota, 55108, USA
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Todd C Mockler
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
| | - Malia A Gehan
- Donald Danforth Plant Science Center, St. Louis, Missouri, 63132, USA
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8
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Ginzburg DN, Cox JA, Rhee SY. Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress. PLANT DIRECT 2024; 8:e571. [PMID: 38464685 PMCID: PMC10918709 DOI: 10.1002/pld3.571] [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: 12/05/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 03/12/2024]
Abstract
Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.
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Affiliation(s)
- Daniel N. Ginzburg
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
- Present address:
Department of Plant SciencesUniversity of CambridgeCambridgeUK
| | - Jack A. Cox
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
| | - Seung Y. Rhee
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
- Present address:
Plant Resilience Institute, Departments of Biochemistry and Molecular Biology, Plant Biology, and Plant, Soil, and Microbial SciencesMichigan State UniversityEast LansingMichiganUSA
- Present address:
Water and Life Interface InstituteEast LansingMichigan48824USA
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Bengoa Luoni SA, Garassino F, Aarts MGM. A High-Throughput Approach for Photosynthesis Studies in a Brassicaceae Panel. Methods Mol Biol 2024; 2787:39-53. [PMID: 38656480 DOI: 10.1007/978-1-0716-3778-4_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
The study of natural variations in photosynthesis in the Brassicaceae family offers the possibility of identifying mechanisms to enhance photosynthetic efficiency in crop plants. Indeed, this family, and particularly its tribe Brassiceae, has been shown to harbor species that have a higher-than-expected photosynthetic efficiency, possibly as a result of a complex evolutionary history. Over the past two decades, methods have been developed to measure photosynthetic efficiency based on chlorophyll fluorescence. Chlorophyll fluorescence measurements are performed with special cameras, such as the FluorCams, which can be included in robotic systems to create high-throughput phenotyping platforms. While these platforms have so far demonstrated high efficiency in measuring small model species like Arabidopsis thaliana, they have the drawback of limited adaptability to accommodate different plant sizes. As a result, the range of species that can be analyzed is restricted. This chapter presents our approach to analyze the photosynthetic parameters: ϕPSII and Fv/Fm for a panel of Brassicaceae species, including a high-photosynthesis species, Hirschfeldia incana, and the adaptations to the phenotyping platform that are required to accommodate this varied group of plants.
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Affiliation(s)
- Sofia A Bengoa Luoni
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands.
| | - Francesco Garassino
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
| | - Mark G M Aarts
- Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
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Theerawitaya C, Praseartkul P, Taota K, Tisarum R, Samphumphuang T, Singh HP, Cha-Um S. Investigating high throughput phenotyping based morpho-physiological and biochemical adaptations of indian pennywort (Centella asiatica L. urban) in response to different irrigation regimes. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2023; 202:107927. [PMID: 37544120 DOI: 10.1016/j.plaphy.2023.107927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/03/2023] [Accepted: 08/01/2023] [Indexed: 08/08/2023]
Abstract
Indian pennywort (Centella asiatica L. Urban; Apiaceae) is a herbaceous plant used as traditional medicine in several regions worldwide. An adequate supply of fresh water in accordance with crop requirements is an important tool for maintaining the productivity and quality of medicinal plants. The objective of this study was to find a suitable irrigation schedule for improving the morphological and physiological characteristics, and crop productivity of Indian pennywort using high-throughput phenotyping. Four treatments were considered based on irrigation schedules (100, 75, 50, and 25% of field capacity denoted by I100 [control], I75, I50, and I25, respectively). The number of leaves, plant perimeter, plant volume, and shoot dry weight were sustained in I75 irrigated plants, whereas adverse effects on plant growth parameters were observed when plants were subjected to I25 irrigation for 21 days. Leaf temperature (Tleaf) was also retained in I75 irrigated plants, when compared with control. An increase of 2.0 °C temperature was detected in the Tleaf of plants under I25 irrigation treatment when compared with control. The increase in Tleaf was attributed to a decreased transpiration rate (R2 = 0.93), leading to an elevated crop water stress index. Green reflectance and leaf greenness remained unchanged in plants under I75 irrigation, while significantly decreased under I50 and I25 irrigation. These decreases were attributed to declined leaf osmotic potential, increased non-photochemical quenching, and inhibition of net photosynthetic rate (Pn). The asiatic acid and total centellosides in the leaf tissues, and centellosides yield of plants under I75 irrigation were retained when compared with control, while these parameters were regulated to maximal when exposed to I50 irrigation. Based on the results, I75 irrigation treatment was identified as the optimum irrigation schedule for Indian pennywort in terms of sustained biomass and a stable total centellosides. However, further validation in the field trials at multiple locations and involving different crop rotations is recommended to confirm these findings.
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Affiliation(s)
- Cattarin Theerawitaya
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani, 12120, Thailand
| | - Patchara Praseartkul
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani, 12120, Thailand
| | - Kanyarat Taota
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani, 12120, Thailand
| | - Rujira Tisarum
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani, 12120, Thailand
| | - Thapanee Samphumphuang
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani, 12120, Thailand
| | - Harminder Pal Singh
- Department of Environment Studies, Faculty of Science, Panjab University, Chandigarh, 160014, India
| | - Suriyan Cha-Um
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani, 12120, Thailand.
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Osuna-Caballero S, Olivoto T, Jiménez-Vaquero MA, Rubiales D, Rispail N. RGB image-based method for phenotyping rust disease progress in pea leaves using R. PLANT METHODS 2023; 19:86. [PMID: 37605206 PMCID: PMC10440949 DOI: 10.1186/s13007-023-01069-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/04/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection. RESULTS A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method's optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error. CONCLUSIONS A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.
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Affiliation(s)
| | - Tiago Olivoto
- Department of Plant Science, Federal University of Santa Catarina, Florianópolis, 88034-000, SC, Brazil
| | | | - Diego Rubiales
- Institute for Sustainable Agriculture, CSIC, Av. Menéndez Pidal s/n 14004, Córdoba, Spain
| | - Nicolas Rispail
- Institute for Sustainable Agriculture, CSIC, Av. Menéndez Pidal s/n 14004, Córdoba, Spain
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Panda K, Mohanasundaram B, Gutierrez J, McLain L, Castillo SE, Sheng H, Casto A, Gratacós G, Chakrabarti A, Fahlgren N, Pandey S, Gehan MA, Slotkin RK. The plant response to high CO 2 levels is heritable and orchestrated by DNA methylation. THE NEW PHYTOLOGIST 2023; 238:2427-2439. [PMID: 36918471 DOI: 10.1111/nph.18876] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 03/07/2023] [Indexed: 05/19/2023]
Abstract
Plant responses to abiotic environmental challenges are known to have lasting effects on the plant beyond the initial stress exposure. Some of these lasting effects are transgenerational, affecting the next generation. The plant response to elevated carbon dioxide (CO2 ) levels has been well studied. However, these investigations are typically limited to plants grown for a single generation in a high CO2 environment while transgenerational studies are rare. We aimed to determine transgenerational growth responses in plants after exposure to high CO2 by investigating the direct progeny when returned to baseline CO2 levels. We found that both the flowering plant Arabidopsis thaliana and seedless nonvascular plant Physcomitrium patens continue to display accelerated growth rates in the progeny of plants exposed to high CO2 . We used the model species Arabidopsis to dissect the molecular mechanism and found that DNA methylation pathways are necessary for heritability of this growth response. More specifically, the pathway of RNA-directed DNA methylation is required to initiate methylation and the proteins CMT2 and CMT3 are needed for the transgenerational propagation of this DNA methylation to the progeny plants. Together, these two DNA methylation pathways establish and then maintain a cellular memory to high CO2 exposure.
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Affiliation(s)
- Kaushik Panda
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | | | - Jorge Gutierrez
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Lauren McLain
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | | | - Hudanyun Sheng
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Anna Casto
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Gustavo Gratacós
- Department of Computer Science & Engineering, Washington University in St Louis, St Louis, MO, 63130, USA
| | - Ayan Chakrabarti
- Department of Computer Science & Engineering, Washington University in St Louis, St Louis, MO, 63130, USA
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Sona Pandey
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Malia A Gehan
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - R Keith Slotkin
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
- Division of Biological Sciences, University of Missouri, MO, 65211, Columbia, USA
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13
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Gonzalez EM, Zarei A, Hendler N, Simmons T, Zarei A, Demieville J, Strand R, Rozzi B, Calleja S, Ellingson H, Cosi M, Davey S, Lavelle DO, Truco MJ, Swetnam TL, Merchant N, Michelmore RW, Lyons E, Pauli D. PhytoOracle: Scalable, modular phenomics data processing pipelines. FRONTIERS IN PLANT SCIENCE 2023; 14:1112973. [PMID: 36950362 PMCID: PMC10025408 DOI: 10.3389/fpls.2023.1112973] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).
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Affiliation(s)
| | - Ariyan Zarei
- Department of Computer Science, University of Arizona, Tucson, AZ, United States
| | - Nathanial Hendler
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Travis Simmons
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Arman Zarei
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Jeffrey Demieville
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Robert Strand
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Bruno Rozzi
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Sebastian Calleja
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Holly Ellingson
- Data Science Institute, University of Arizona, Tucson, AZ, United States
| | - Michele Cosi
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Sean Davey
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, United States
| | - Dean O. Lavelle
- The Genome and Biomedical Sciences Facility, University of California, Davis, Davis, CA, United States
| | - Maria José Truco
- The Genome and Biomedical Sciences Facility, University of California, Davis, Davis, CA, United States
| | - Tyson L. Swetnam
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, United States
| | - Nirav Merchant
- Data Science Institute, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Richard W. Michelmore
- The Genome and Biomedical Sciences Facility, University of California, Davis, Davis, CA, United States
- Department of Plant Sciences, University of California, Davis, Davis, CA, United States
| | - Eric Lyons
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
- Data Science Institute, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Duke Pauli
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
- Data Science Institute, University of Arizona, Tucson, AZ, United States
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14
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Kinose R, Utsumi Y, Iwamura M, Kise K. Tiller estimation method using deep neural networks. FRONTIERS IN PLANT SCIENCE 2023; 13:1016507. [PMID: 36714728 PMCID: PMC9880423 DOI: 10.3389/fpls.2022.1016507] [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: 08/11/2022] [Accepted: 12/19/2022] [Indexed: 06/18/2023]
Abstract
This paper describes a method based on a deep neural network (DNN) for estimating the number of tillers on a plant. A tiller is a branch on a grass plant, and the number of tillers is one of the most important determinants of yield. Traditionally, the tiller number is usually counted by hand, and so an automated approach is necessary for high-throughput phenotyping. Conventional methods use heuristic features to estimate the tiller number. Based on the successful application of DNNs in the field of computer vision, the use of DNN-based features instead of heuristic features is expected to improve the estimation accuracy. However, as DNNs generally require large volumes of data for training, it is difficult to apply them to estimation problems for which large training datasets are unavailable. In this paper, we use two strategies to overcome the problem of insufficient training data: the use of a pretrained DNN model and the use of pretext tasks for learning the feature representation. We extract features using the resulting DNNs and estimate the tiller numbers through a regression technique. We conducted experiments using side-view whole plant images taken with plan backgroud. The experimental results show that the proposed methods using a pretrained model and specific pretext tasks achieve better performance than the conventional method.
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Affiliation(s)
- Rikuya Kinose
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
| | - Yuzuko Utsumi
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
| | - Masakazu Iwamura
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
| | - Koichi Kise
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
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15
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Aggarwal PR, Pramitha L, Choudhary P, Singh RK, Shukla P, Prasad M, Muthamilarasan M. Multi-omics intervention in Setaria to dissect climate-resilient traits: Progress and prospects. FRONTIERS IN PLANT SCIENCE 2022; 13:892736. [PMID: 36119586 PMCID: PMC9470963 DOI: 10.3389/fpls.2022.892736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 08/05/2022] [Indexed: 06/15/2023]
Abstract
Millets constitute a significant proportion of underutilized grasses and are well known for their climate resilience as well as excellent nutritional profiles. Among millets, foxtail millet (Setaria italica) and its wild relative green foxtail (S. viridis) are collectively regarded as models for studying broad-spectrum traits, including abiotic stress tolerance, C4 photosynthesis, biofuel, and nutritional traits. Since the genome sequence release, the crop has seen an exponential increase in omics studies to dissect agronomic, nutritional, biofuel, and climate-resilience traits. These studies have provided first-hand information on the structure, organization, evolution, and expression of several genes; however, knowledge of the precise roles of such genes and their products remains elusive. Several open-access databases have also been instituted to enable advanced scientific research on these important crops. In this context, the current review enumerates the contemporary trend of research on understanding the climate resilience and other essential traits in Setaria, the knowledge gap, and how the information could be translated for the crop improvement of related millets, biofuel crops, and cereals. Also, the review provides a roadmap for studying other underutilized crop species using Setaria as a model.
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Affiliation(s)
- Pooja Rani Aggarwal
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Lydia Pramitha
- School of Agriculture and Biosciences, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
| | - Pooja Choudhary
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | | | - Pooja Shukla
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manoj Prasad
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
- National Institute of Plant Genome Research (NIPGR), New Delhi, India
| | - Mehanathan Muthamilarasan
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
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16
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Wang W, Talide L, Viljamaa S, Niittylä T. Aspen growth is not limited by starch reserves. Curr Biol 2022; 32:3619-3627.e4. [PMID: 35820419 DOI: 10.1016/j.cub.2022.06.056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/13/2022] [Accepted: 06/16/2022] [Indexed: 11/16/2022]
Abstract
All photosynthetic organisms balance CO2 assimilation with growth and carbon storage. Stored carbon is used for growth at night and when demand exceeds assimilation. Gaining a mechanistic understanding of carbon partitioning between storage and growth in trees is important for biological studies and for estimating the potential of terrestrial photosynthesis to sequester anthropogenic CO2 emissions.1,2 Starch represents the main carbon storage in plants.3,4 To examine the carbon storage mechanism and role of starch during tree growth, we generated and characterized low-starch hybrid aspen (Populus tremula × tremuloides) trees using CRISPR-Cas9-mediated gene editing of two PHOSPHOGLUCOMUTASE (PGM) genes coding for plastidial PGM isoforms essential for starch biosynthesis. We demonstrate that starch deficiency does not reduce tree growth even in short days, showing that starch is not a critical carbon reserve during diel growth of aspen. The low-starch trees assimilated up to ∼30% less CO2 compared to the wild type under a range of irradiance levels, but this did not reduce growth or wood density. This implies that aspen growth is not limited by carbon assimilation under benign growth conditions. Moreover, the timing of bud set and bud flush in the low-starch trees was not altered, implying that starch reserves are not critical for the seasonal growth-dormancy cycle. The findings are consistent with a passive starch storage mechanism that contrasts with the annual Arabidopsis and indicate that the capacity of the aspen to absorb CO2 is limited by the rate of sink tissue growth.
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Affiliation(s)
- Wei Wang
- Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå Plant Science Centre, Umeå S-901 83, Sweden
| | - Loic Talide
- Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå Plant Science Centre, Umeå S-901 83, Sweden
| | - Sonja Viljamaa
- Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå Plant Science Centre, Umeå S-901 83, Sweden
| | - Totte Niittylä
- Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå Plant Science Centre, Umeå S-901 83, Sweden.
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17
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Al-Tamimi N, Langan P, Bernád V, Walsh J, Mangina E, Negrão S. Capturing crop adaptation to abiotic stress using image-based technologies. Open Biol 2022; 12:210353. [PMID: 35728624 PMCID: PMC9213114 DOI: 10.1098/rsob.210353] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.
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Affiliation(s)
- Nadia Al-Tamimi
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Patrick Langan
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Villő Bernád
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Jason Walsh
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland,School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Eleni Mangina
- School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Sónia Negrão
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
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18
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Wilson ML, VanBuren R. Leveraging millets for developing climate resilient agriculture. Curr Opin Biotechnol 2022; 75:102683. [DOI: 10.1016/j.copbio.2022.102683] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 12/16/2021] [Accepted: 01/03/2022] [Indexed: 01/31/2023]
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19
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Castillo SE, Tovar JC, Shamin A, Gutirerrez J, Pearson P, Gehan MA. A protocol for Chenopodium quinoa pollen germination. PLANT METHODS 2022; 18:65. [PMID: 35585546 PMCID: PMC9118578 DOI: 10.1186/s13007-022-00900-3] [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: 01/31/2022] [Accepted: 05/03/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Quinoa is an increasingly popular seed crop frequently studied for its tolerance to various abiotic stresses as well as its susceptibility to heat. Estimations of quinoa pollen viability through staining methods have resulted in conflicting results. A more effective alternative to stains is to estimate pollen viability through in vitro germination. Here we report a method for in vitro quinoa pollen germination that could be used to understand the impact of various stresses on quinoa fertility and therefore seed yield or to identify male-sterile lines for breeding. RESULTS A semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community. Pollen collected on day 4 after first anthesis at zeitgeber time 5 was optimum for pollen germination with an average germination of 68% for accession QQ74 (PI 614886). The optimal length of pollen incubation was found to be 48 h, because it maximizes germination rates while minimizing contamination. The pollen germination medium's pH, boric acid, and sucrose concentrations were optimized. The highest germination rates were obtained with 16% sucrose, 0.03% boric acid, 0.007% calcium nitrate, and pH 5.5. This medium was tested on quinoa accessions QQ74, and cherry vanilla with 68%, and 64% germination efficiencies, respectively. CONCLUSIONS We provide an in vitro pollen germination method for quinoa with average germination rates of 64 and 68% on the two accessions tested. This method is a valuable tool to estimate pollen viability in quinoa, and to test how stress affects quinoa fertility. We also developed an image analysis tool to semi-automate the process of counting germinating pollen. Quinoa produces many new flowers during most of its panicle development period, leading to significant variation in pollen maturity and viability between different flowers of the same panicle. Therefore, collecting pollen at 4 days after first anthesis is very important to collect more uniformly developed pollen and to obtain high germination rates.
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Affiliation(s)
| | - Jose C Tovar
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA
| | | | | | - Paige Pearson
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA
| | - Malia A Gehan
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA.
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20
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Estimation of Ground PM2.5 Concentrations in Pakistan Using Convolutional Neural Network and Multi-Pollutant Satellite Images. REMOTE SENSING 2022. [DOI: 10.3390/rs14071735] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
During the last few decades, worsening air quality has been diagnosed in many cities around the world. The accurately prediction of air pollutants, particularly, particulate matter 2.5 (PM2.5) is extremely important for environmental management. A Convolutional Neural Network (CNN) P-CNN model is presented in this paper, which uses seven different pollutant satellite images, such as Aerosol index (AER AI), Methane (CH4), Carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Ozone (O3) and Sulfur dioxide (SO2), as auxiliary variables to estimate daily average PM2.5 concentrations. This study estimates daily average of PM2.5 concentrations in various cities of Pakistan (Islamabad, Lahore, Peshawar and Karachi) by using satellite images. The dataset contains a total of 2562 images from May-2019 to April-2020. We compare and analyze AlexNet, VGG16, ResNet50 and P-CNN model on every dataset. The accuracy of machine learning models was checked with Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that P-CNN is more accurate than other approaches in estimating PM2.5 concentrations from satellite images. This study presents robust model using satellite images, useful for estimating PM2.5 concentrations.
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21
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Boogaard FP, van Henten EJ, Kootstra G. Improved Point-Cloud Segmentation for Plant Phenotyping Through Class-Dependent Sampling of Training Data to Battle Class Imbalance. FRONTIERS IN PLANT SCIENCE 2022; 13:838190. [PMID: 35419014 PMCID: PMC8996061 DOI: 10.3389/fpls.2022.838190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
Plant scientists and breeders require high-quality phenotypic data. However, obtaining accurate manual measurements for large plant populations is often infeasible, due to the high labour requirement involved. This is especially the case for more complex plant traits, like the traits defining the plant architecture. Computer-vision methods can help in solving this bottleneck. The current work focusses on methods using 3D point cloud data to obtain phenotypic datasets of traits related to the plant architecture. A first step is the segmentation of the point clouds into plant organs. One of the issues in point-cloud segmentation is that not all plant parts are equally represented in the data and that the segmentation performance is typically lower for minority classes than for majority classes. To address this class-imbalance problem, we used a common practice to divide large point clouds into chunks that were independently segmented and recombined later. In our case, the chunks were created by selecting anchor points and combining those with points in their neighbourhood. As a baseline, the anchor points were selected in a class-independent way, representing the class distribution in the original data. Then, we propose a class-dependent sampling strategy to battle class imbalance. The difference in segmentation performance between the class-independent and the class-dependent training set was analysed first. Additionally, the effect of the number of points selected as the neighbourhood was investigated. Smaller neighbourhoods resulted in a higher level of class balance, but also in a loss of context that was contained in the points around the anchor point. The overall segmentation quality, measured as the mean intersection-over-union (IoU), increased from 0.94 to 0.96 when the class-dependent training set was used. The biggest class improvement was found for the "node," for which the percentage of correctly segmented points increased by 46.0 percentage points. The results of the second experiment clearly showed that higher levels of class balance did not necessarily lead to better segmentation performance. Instead, the optimal neighbourhood size differed per class. In conclusion, it was demonstrated that our class-dependent sampling strategy led to an improved point-cloud segmentation method for plant phenotyping.
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Affiliation(s)
- Frans P. Boogaard
- Wageningen University & Research, Farm Technology Group, Wageningen, Netherlands
- Rijk Zwaan Breeding, Fijnaart, Netherlands
| | - Eldert J. van Henten
- Wageningen University & Research, Farm Technology Group, Wageningen, Netherlands
| | - Gert Kootstra
- Wageningen University & Research, Farm Technology Group, Wageningen, Netherlands
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22
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Teramoto S, Uga Y. Improving the efficiency of plant root system phenotyping through digitization and automation. BREEDING SCIENCE 2022; 72:48-55. [PMID: 36045896 PMCID: PMC8987843 DOI: 10.1270/jsbbs.21053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/11/2021] [Indexed: 05/19/2023]
Abstract
Root system architecture (RSA) determines unevenly distributed water and nutrient availability in soil. Genetic improvement of RSA, therefore, is related to crop production. However, RSA phenotyping has been carried out less frequently than above-ground phenotyping because measuring roots in the soil is difficult and labor intensive. Recent advancements have led to the digitalization of plant measurements; this digital phenotyping has been widely used for measurements of both above-ground and RSA traits. Digital phenotyping for RSA is slower and more difficult than for above-ground traits because the roots are hidden underground. In this review, we summarized recent trends in digital phenotyping for RSA traits. We classified the sample types into three categories: soil block containing roots, section of soil block, and root sample. Examples of the use of digital phenotyping are presented for each category. We also discussed room for improvement in digital phenotyping in each category.
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Affiliation(s)
- Shota Teramoto
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8518, Japan
| | - Yusaku Uga
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8518, Japan
- Corresponding author (e-mail: )
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23
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Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning. AGRIENGINEERING 2022. [DOI: 10.3390/agriengineering4010010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Crown rot is one of the major stubble soil fungal diseases that bring significant yield loss to the cereal industry. The most effective crown rot management approach is removal of infected crop residue from fields and rotation of nonhost crops. However, disease screening is challenging as there are no clear visible symptoms on upper stems and leaves at early growth stages. The current manual screening method requires experts to observe the crown and roots of plants to detect disease, which is time-consuming, subjective, labor-intensive, and costly. As digital color imaging has the advantages of low cost and easy use, it has a high potential to be an economical solution for crown rot detection. In this research, a crown rot disease detection method was developed using a smartphone camera and machine learning technologies. Four common wheat varieties were grown in greenhouse conditions with a controlled environment, and all infected group plants were infected with crown rot without the presence of other plant diseases. We used a smartphone to take digital color images of the lower stems of plants. Using imaging processing techniques and a support vector machine algorithm, we successfully distinguished infected and healthy plants as early as 14 days after disease infection. The results provide a vital first step toward developing a digital color imaging phenotyping platform for crown rot detection to enable the management of crown rot disease effectively. As an easy-access phenotyping method, this method could provide support for researchers to develop an efficiency and economic disease screening method in field conditions.
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24
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Van Tassel DL, DeHaan LR, Diaz-Garcia L, Hershberger J, Rubin MJ, Schlautman B, Turner K, Miller AJ. Re-imagining crop domestication in the era of high throughput phenomics. CURRENT OPINION IN PLANT BIOLOGY 2022; 65:102150. [PMID: 34883308 DOI: 10.1016/j.pbi.2021.102150] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 10/19/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
De novo domestication is an exciting option for increasing species diversity and ecosystem service functionality of agricultural landscapes. Genomic selection (GS), the application of genomic markers to predict phenotypic traits in a breeding population, offers the possibility of rapid genetic improvement, making GS especially attractive for modifying traits of long-lived species. However, for some wild species just entering the domestication pipeline, especially those with large and complex genomes, a lack of funding and/or prior genome characterization, GS is often out of reach. High throughput phenomics has the potential to augment traditional pedigree selection, reduce costs and amplify impacts of genomic selection, and even create new predictive selection approaches independent of sequencing or pedigrees.
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Affiliation(s)
| | - Lee R DeHaan
- The Land Institute, 2440 E Water Well Rd., Salina, KS, 67401, USA
| | | | - Jenna Hershberger
- The Land Institute, 2440 E Water Well Rd., Salina, KS, 67401, USA; Donald Danforth Plant Science Center, 975 North Warson Road, Saint Louis, MO, 63132, USA
| | - Matthew J Rubin
- Donald Danforth Plant Science Center, 975 North Warson Road, Saint Louis, MO, 63132, USA
| | | | - Kathryn Turner
- The Land Institute, 2440 E Water Well Rd., Salina, KS, 67401, USA
| | - Allison J Miller
- Donald Danforth Plant Science Center, 975 North Warson Road, Saint Louis, MO, 63132, USA; Saint Louis University Department of Biology, 3507 Laclede Avenue, St. Louis, MO, 63103, USA.
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25
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Tross MC, Gaillard M, Zwiener M, Miao C, Grove RJ, Li B, Benes B, Schnable JC. 3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves. PeerJ 2022; 9:e12628. [PMID: 35036135 PMCID: PMC8710048 DOI: 10.7717/peerj.12628] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/21/2021] [Indexed: 12/22/2022] Open
Abstract
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
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Affiliation(s)
- Michael C Tross
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America.,Complex Biosystems Graduate Program, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | - Mathieu Gaillard
- Computer Science, Purdue University, West Lafayette, IN, United States of America
| | - Mackenzie Zwiener
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | - Chenyong Miao
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | - Ryleigh J Grove
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America.,Lincoln North Star High School, Lincoln, NE, United States of America
| | - Bosheng Li
- Computer Science, Purdue University, West Lafayette, IN, United States of America
| | - Bedrich Benes
- Computer Science, Purdue University, West Lafayette, IN, United States of America.,Department of Computer Graphics Technology, Purdue University, West Lafayette, IN, United States of America
| | - James C Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE, United States of America.,Complex Biosystems Graduate Program, University of Nebraska - Lincoln, Lincoln, NE, United States of America
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26
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Ebersbach J, Khan NA, McQuillan I, Higgins EE, Horner K, Bandi V, Gutwin C, Vail SL, Robinson SJ, Parkin IAP. Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population. FRONTIERS IN PLANT SCIENCE 2022; 12:780250. [PMID: 35069637 PMCID: PMC8767643 DOI: 10.3389/fpls.2021.780250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.
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Affiliation(s)
| | - Nazifa Azam Khan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian McQuillan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | | | - Kyla Horner
- Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | - Venkat Bandi
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Carl Gutwin
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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27
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Ismael A, Xue J, Meason DF, Klápště J, Gallart M, Li Y, Bellè P, Gomez-Gallego M, Bradford KT, Telfer E, Dungey H. Genetic Variation in Drought-Tolerance Traits and Their Relationships to Growth in Pinus radiata D. Don Under Water Stress. FRONTIERS IN PLANT SCIENCE 2022; 12:766803. [PMID: 35058945 PMCID: PMC8764257 DOI: 10.3389/fpls.2021.766803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 11/29/2021] [Indexed: 05/08/2023]
Abstract
The selection of drought-tolerant genotypes is globally recognized as an effective strategy to maintain the growth and survival of commercial tree species exposed to future drought periods. New genomic selection tools that reduce the time of progeny trials are required to substitute traditional tree breeding programs. We investigated the genetic variation of water stress tolerance in New Zealand-grown Pinus radiata D. Don using 622 commercially-used genotypes from 63 families. We used quantitative pedigree-based (Genomic Best Linear Unbiased Prediction or ABLUP) and genomic-based (Genomic Best Linear Unbiased Prediction or GBLUP) approaches to examine the heritability estimates associated with water stress tolerance in P. radiata. Tree seedling growth traits, foliar carbon isotope composition (δ13C), and dark-adapted chlorophyll fluorescence (Y) were monitored before, during and after 10 months of water stress. Height growth showed a constant and moderate heritability level, while the heritability estimate for diameter growth and δ13C decreased with water stress. In contrast, chlorophyll fluorescence exhibited low heritability after 5 and 10 months of water stress. The GBLUP approach provided less breeding value accuracy than ABLUP, however, the relative selection efficiency of GBLUP was greater compared with ABLUP selection techniques. Although there was no significant relationship directly between δ13C and Y, the genetic correlations were significant and stronger for GBLUP. The positive genetic correlations between δ13C and tree biomass traits under water stress indicated that intraspecific variation in δ13C was likely driven by differences in the genotype's photosynthetic capacity. The results show that foliar δ13C can predict P. radiata genotype tolerance to water stress using ABLUP and GBLUP approaches and that such approaches can provide a faster screening and selection of drought-tolerant genotypes for forestry breeding programs.
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Affiliation(s)
- Ahmed Ismael
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua, New Zealand
- Research and Development, Livestock Improvement Corporation, Hamilton, New Zealand
| | - Jianming Xue
- Scion (New Zealand Forest Research Institute Ltd.), Christchurch, New Zealand
| | | | - Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua, New Zealand
| | - Marta Gallart
- Centre for Planetary Health and Food Security, Griffith University, Nathan, QLD, Australia
| | - Yongjun Li
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua, New Zealand
- Agriculture Victoria, AgriBio Center, Bundoora, VIC, Australia
| | - Pierre Bellè
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua, New Zealand
| | - Mireia Gomez-Gallego
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua, New Zealand
- INRAE, IAM, Université de Lorraine, Nancy, France
| | | | - Emily Telfer
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua, New Zealand
| | - Heidi Dungey
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua, New Zealand
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28
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Stager A, Tanner HG, Sparks E. Design and Construction of Unmanned Ground Vehicles for Sub-canopy Plant Phenotyping. Methods Mol Biol 2022; 2539:191-211. [PMID: 35895205 DOI: 10.1007/978-1-0716-2537-8_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Unmanned ground vehicles can capture a sub-canopy perspective for plant phenotyping, but their design and construction can be a challenge for scientists unfamiliar with robotics. Here we describe the necessary components and provide guidelines for designing and constructing an autonomous ground robot that can be used for plant phenotyping.
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Affiliation(s)
- Adam Stager
- Department of Mechanical Engineering, University of Delaware, Newark, DE, USA
- TRIC Robotics LLC, Newark, DE, USA
| | - Herbert G Tanner
- Department of Mechanical Engineering, University of Delaware, Newark, DE, USA
| | - Erin Sparks
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA.
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29
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Kuromori T, Fujita M, Takahashi F, Yamaguchi‐Shinozaki K, Shinozaki K. Inter-tissue and inter-organ signaling in drought stress response and phenotyping of drought tolerance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 109:342-358. [PMID: 34863007 PMCID: PMC9300012 DOI: 10.1111/tpj.15619] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 05/10/2023]
Abstract
Plant response to drought stress includes systems for intracellular regulation of gene expression and signaling, as well as inter-tissue and inter-organ signaling, which helps entire plants acquire stress resistance. Plants sense water-deficit conditions both via the stomata of leaves and roots, and transfer water-deficit signals from roots to shoots via inter-organ signaling. Abscisic acid is an important phytohormone involved in the drought stress response and adaptation, and is synthesized mainly in vascular tissues and guard cells of leaves. In leaves, stress-induced abscisic acid is distributed to various tissues by transporters, which activates stomatal closure and expression of stress-related genes to acquire drought stress resistance. Moreover, the stepwise stress response at the whole-plant level is important for proper understanding of the physiological response to drought conditions. Drought stress is sensed by multiple types of sensors as molecular patterns of abiotic stress signals, which are transmitted via separate parallel signaling networks to induce downstream responses, including stomatal closure and synthesis of stress-related proteins and metabolites. Peptide molecules play important roles in the inter-organ signaling of dehydration from roots to shoots, as well as signaling of osmotic changes and reactive oxygen species/Ca2+ . In this review, we have summarized recent advances in research on complex plant drought stress responses, focusing on inter-tissue signaling in leaves and inter-organ signaling from roots to shoots. We have discussed the mechanisms via which drought stress adaptations and resistance are acquired at the whole-plant level, and have proposed the importance of quantitative phenotyping for measuring plant growth under drought conditions.
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Affiliation(s)
- Takashi Kuromori
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science2‐1 HirosawaWakoSaitama351‐0198Japan
| | - Miki Fujita
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science3‐1‐1 KoyadaiTsukubaIbaraki305‐0074Japan
| | - Fuminori Takahashi
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science3‐1‐1 KoyadaiTsukubaIbaraki305‐0074Japan
- Department of Biological Science and TechnologyGraduate School of Advanced EngineeringTokyo University of Science6‐3‐1 Niijyuku, Katsushika‐kuTokyo125‐8585Japan
| | - Kazuko Yamaguchi‐Shinozaki
- Laboratory of Plant Molecular PhysiologyGraduate School of Agricultural and Life SciencesThe University of Tokyo1‐1‐1 Yayoi, Bunkyo‐kuTokyo113‐8657Japan
- Research Institute for Agricultural and Life SciencesTokyo University of Agriculture1‐1‐1 Sakuragaoka, Setagaya‐kuTokyo156‐8502Japan
| | - Kazuo Shinozaki
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science2‐1 HirosawaWakoSaitama351‐0198Japan
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science3‐1‐1 KoyadaiTsukubaIbaraki305‐0074Japan
- Biotechonology CenterNational Chung Hsing University (NCHU)Taichung402Taiwan
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30
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Tabb A, Holguín GA, Naegele R. Using Cameras for Precise Measurement of Two-Dimensional Plant Features: CASS. Methods Mol Biol 2022; 2539:87-94. [PMID: 35895199 DOI: 10.1007/978-1-0716-2537-8_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Images are used frequently in plant phenotyping to capture measurements. This chapter offers a repeatable method for capturing two-dimensional measurements of plant parts in field or laboratory settings using a variety of camera styles (cellular phone, DSLR), with the addition of a printed calibration pattern. The method is based on calibrating the camera using information available from the EXIF tags from the image, as well as visual information from the pattern. Code is provided to implement the method, as well as a dataset for testing. We include steps to verify protocol correctness by imaging an artifact. The use of this protocol for two-dimensional plant phenotyping will allow data capture from different cameras and environments, with comparison on the same physical scale. We abbreviate this method as CASS, CAmera aS Scanner.
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Affiliation(s)
- Amy Tabb
- United States Department of Agriculture, Agricultural Research Service, Appalachian Fruit Research Station (USDA-ARS-AFRS), Kearneysville, WV, USA.
| | - Germán A Holguín
- Electrical Engineering Department, Universidad Tecnológica de Pereira, Pereira, Colombia
| | - Rachel Naegele
- United States Department of Agriculture, Agricultural Research Service, Sugarbeet and Bean Research Unit (USDA-ARS), East Lansing, MI, USA
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31
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Pignon CP, Fernandes SB, Valluru R, Bandillo N, Lozano R, Buckler E, Gore MA, Long SP, Brown PJ, Leakey ADB. Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes. PLANT PHYSIOLOGY 2021; 187:2544-2562. [PMID: 34618072 PMCID: PMC8644692 DOI: 10.1093/plphys/kiab395] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/26/2021] [Indexed: 05/07/2023]
Abstract
Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.
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Affiliation(s)
- Charles P Pignon
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Samuel B Fernandes
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln LN1 3QE, UK
| | - Nonoy Bandillo
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota 58105, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Edward Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS) R.W. Holley Center for Agriculture and Health, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Stephen P Long
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Lancaster Environment Centre, University of Lancaster, Lancaster LA1 1YX, UK
| | - Patrick J Brown
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Andrew D B Leakey
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Author for communication:
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32
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Zhang H, Tang S, Schnable JC, He Q, Gao Y, Luo M, Jia G, Feng B, Zhi H, Diao X. Genome-Wide DNA Polymorphism Analysis and Molecular Marker Development for the Setaria italica Variety "SSR41" and Positional Cloning of the Setaria White Leaf Sheath Gene SiWLS1. FRONTIERS IN PLANT SCIENCE 2021; 12:743782. [PMID: 34858451 PMCID: PMC8632227 DOI: 10.3389/fpls.2021.743782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 10/13/2021] [Indexed: 05/03/2023]
Abstract
Genome-wide DNA polymorphism analysis and molecular marker development are important for forward genetics research and DNA marker-assisted breeding. As an ideal model system for Panicoideae grasses and an important minor crop in East Asia, foxtail millet (Setaria italica) has a high-quality reference genome as well as large mutant libraries based on the "Yugu1" variety. However, there is still a lack of genetic and mutation mapping tools available for forward genetics research on S. italica. Here, we screened another S. italica genotype, "SSR41", which is morphologically similar to, and readily cross-pollinates with, "Yugu1". High-throughput resequencing of "SSR41" identified 1,102,064 reliable single nucleotide polymorphisms (SNPs) and 196,782 insertions/deletions (InDels) between the two genotypes, indicating that these two genotypes have high genetic diversity. Of the 8,361 high-quality InDels longer than 20 bp that were developed as molecular markers, 180 were validated with 91.5% accuracy. We used "SSR41" and these developed molecular markers to map the white leaf sheath gene SiWLS1. Further analyses showed that SiWLS1 encodes a chloroplast-localized protein that is involved in the regulation of chloroplast development in bundle sheath cells in the leaf sheath in S. italica and is related to sensitivity to heavy metals. Our study provides the methodology and an important resource for forward genetics research on Setaria.
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Affiliation(s)
- Hui Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Sha Tang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - James C. Schnable
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, United States
| | - Qiang He
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuanzhu Gao
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mingzhao Luo
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Guanqing Jia
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Baili Feng
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Hui Zhi
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xianmin Diao
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
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33
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Armanhi JSL, de Souza RSC, Biazotti BB, Yassitepe JEDCT, Arruda P. Modulating Drought Stress Response of Maize by a Synthetic Bacterial Community. Front Microbiol 2021; 12:747541. [PMID: 34745050 PMCID: PMC8566980 DOI: 10.3389/fmicb.2021.747541] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/20/2021] [Indexed: 01/21/2023] Open
Abstract
Plant perception and responses to environmental stresses are known to encompass a complex set of mechanisms in which the microbiome is involved. Knowledge about plant physiological responses is therefore critical for understanding the contribution of the microbiome to plant resilience. However, as plant growth is a dynamic process, a major hurdle is to find appropriate tools to effectively measure temporal variations of different plant physiological parameters. Here, we used a non-invasive real-time phenotyping platform in a one-to-one (plant–sensors) set up to investigate the impact of a synthetic community (SynCom) harboring plant-beneficial bacteria on the physiology and response of three commercial maize hybrids to drought stress (DS). SynCom inoculation significantly reduced yield loss and modulated vital physiological traits. SynCom-inoculated plants displayed lower leaf temperature, reduced turgor loss under severe DS and a faster recovery upon rehydration, likely as a result of sap flow modulation and better water usage. Microbiome profiling revealed that SynCom bacterial members were able to robustly colonize mature plants and recruit soil/seed-borne beneficial microbes. The high-resolution temporal data allowed us to record instant plant responses to daily environmental fluctuations, thus revealing the impact of the microbiome in modulating maize physiology, resilience to drought, and crop productivity.
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Affiliation(s)
- Jaderson Silveira Leite Armanhi
- Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.,Genomics for Climate Change Research Center (GCCRC), Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
| | - Rafael Soares Correa de Souza
- Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.,Genomics for Climate Change Research Center (GCCRC), Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
| | - Bárbara Bort Biazotti
- Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.,Genomics for Climate Change Research Center (GCCRC), Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.,Departamento de Genética e Evolução, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
| | | | - Paulo Arruda
- Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.,Genomics for Climate Change Research Center (GCCRC), Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.,Departamento de Genética e Evolução, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
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34
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Geldhof B, Pattyn J, Eyland D, Carpentier S, Van de Poel B. A digital sensor to measure real-time leaf movements and detect abiotic stress in plants. PLANT PHYSIOLOGY 2021; 187:1131-1148. [PMID: 34618089 PMCID: PMC8566216 DOI: 10.1093/plphys/kiab407] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 08/02/2021] [Indexed: 05/31/2023]
Abstract
Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36 ± 0.53° for pitch and 0.50 ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e. bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.
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Affiliation(s)
- Batist Geldhof
- Department of Biosystems, Division of Crop Biotechnics, Molecular Plant Hormone Physiology Lab, University of Leuven, Leuven 3001, Belgium
| | - Jolien Pattyn
- Department of Biosystems, Division of Crop Biotechnics, Molecular Plant Hormone Physiology Lab, University of Leuven, Leuven 3001, Belgium
| | - David Eyland
- Department of Biosystems, Division of Crop Biotechnics, Tropical Crop Improvement Laboratory, University of Leuven, Leuven 3001, Belgium
| | - Sebastien Carpentier
- Department of Biosystems, Division of Crop Biotechnics, Tropical Crop Improvement Laboratory, University of Leuven, Leuven 3001, Belgium
- Bioversity International, Leuven, 3001, Belgium
| | - Bram Van de Poel
- Department of Biosystems, Division of Crop Biotechnics, Molecular Plant Hormone Physiology Lab, University of Leuven, Leuven 3001, Belgium
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Danilevicz MF, Bayer PE, Nestor BJ, Bennamoun M, Edwards D. Resources for image-based high-throughput phenotyping in crops and data sharing challenges. PLANT PHYSIOLOGY 2021; 187:699-715. [PMID: 34608963 PMCID: PMC8561249 DOI: 10.1093/plphys/kiab301] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 05/26/2021] [Indexed: 05/06/2023]
Abstract
High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.
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Affiliation(s)
- Monica F. Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Philipp E. Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Benjamin J. Nestor
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, University of Western Australia, Perth, Western Australia 6009, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
- Author for communication:
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Hilty J, Muller B, Pantin F, Leuzinger S. Plant growth: the What, the How, and the Why. THE NEW PHYTOLOGIST 2021; 232:25-41. [PMID: 34245021 DOI: 10.1111/nph.17610] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 06/19/2021] [Indexed: 05/28/2023]
Abstract
Growth is a widely used term in plant science and ecology, but it can have different meanings depending on the context and the spatiotemporal scale of analysis. At the meristem level, growth is associated with the production of cells and initiation of new organs. At the organ or plant scale and over short time periods, growth is often used synonymously with tissue expansion, while over longer time periods the increase in biomass is a common metric. At even larger temporal and spatial scales, growth is mostly described as net primary production. Here, we first address the question 'what is growth?'. We propose a general framework to distinguish between the different facets of growth, and the corresponding physiological processes, environmental drivers and mathematical formalisms. Based on these different definitions, we then review how plant growth can be measured and analysed at different organisational, spatial and temporal scales. We conclude by discussing why gaining a better understanding of the different facets of plant growth is essential to disentangle genetic and environmental effects on the phenotype, and to uncover the causalities around source or sink limitations of plant growth.
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Affiliation(s)
- Jonas Hilty
- School of Science, Auckland University of Technology, 46 Wakefield Street, Auckland, 1142, New Zealand
| | - Bertrand Muller
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, 34000, France
| | - Florent Pantin
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, 34000, France
| | - Sebastian Leuzinger
- School of Science, Auckland University of Technology, 46 Wakefield Street, Auckland, 1142, New Zealand
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Tsai NC, Hsu TS, Kuo SC, Kao CT, Hung TH, Lin DG, Yeh CS, Chu CC, Lin JS, Lin HH, Ko CY, Chang TH, Su JC, Lin YCJ. Large-scale data analysis for robotic yeast one-hybrid platforms and multi-disciplinary studies using GateMultiplex. BMC Biol 2021; 19:214. [PMID: 34560855 PMCID: PMC8461970 DOI: 10.1186/s12915-021-01140-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 09/03/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Yeast one-hybrid (Y1H) is a common technique for identifying DNA-protein interactions, and robotic platforms have been developed for high-throughput analyses to unravel the gene regulatory networks in many organisms. Use of these high-throughput techniques has led to the generation of increasingly large datasets, and several software packages have been developed to analyze such data. We previously established the currently most efficient Y1H system, meiosis-directed Y1H; however, the available software tools were not designed for processing the additional parameters suggested by meiosis-directed Y1H to avoid false positives and required programming skills for operation. RESULTS We developed a new tool named GateMultiplex with high computing performance using C++. GateMultiplex incorporated a graphical user interface (GUI), which allows the operation without any programming skills. Flexible parameter options were designed for multiple experimental purposes to enable the application of GateMultiplex even beyond Y1H platforms. We further demonstrated the data analysis from other three fields using GateMultiplex, the identification of lead compounds in preclinical cancer drug discovery, the crop line selection in precision agriculture, and the ocean pollution detection from deep-sea fishery. CONCLUSIONS The user-friendly GUI, fast C++ computing speed, flexible parameter setting, and applicability of GateMultiplex facilitate the feasibility of large-scale data analysis in life science fields.
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Affiliation(s)
- Ni-Chiao Tsai
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Tzu-Shu Hsu
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Shang-Che Kuo
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, 10617, Taiwan
| | - Chung-Ting Kao
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Tzu-Huan Hung
- Biotechnology Division, Taiwan Agricultural Research Institute, Taichung, 41362, Taiwan
| | - Da-Gin Lin
- Biotechnology Division, Taiwan Agricultural Research Institute, Taichung, 41362, Taiwan
| | - Chung-Shu Yeh
- Genomics Research Center, Academia Sinica, Taipei, 11529, Taiwan
| | - Chia-Chen Chu
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Jeng-Shane Lin
- Department of Life Sciences, National Chung Hsing University, Taichung, 40227, Taiwan
| | - Hsin-Hung Lin
- Department of Horticulture and Biotechnology, Chinese Culture University, Taipei, 11114, Taiwan
| | - Chia-Ying Ko
- Department of Life Sciences and Institute of Fisheries Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Tien-Hsien Chang
- Genomics Research Center, Academia Sinica, Taipei, 11529, Taiwan.
| | - Jung-Chen Su
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan.
| | - Ying-Chung Jimmy Lin
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan.
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, 10617, Taiwan.
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Nakhle F, Harfouche AL. Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis. PATTERNS (NEW YORK, N.Y.) 2021; 2:100323. [PMID: 34553170 PMCID: PMC8441561 DOI: 10.1016/j.patter.2021.100323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
High-throughput image-based technologies are now widely used in the rapidly developing field of digital phenomics and are generating ever-increasing amounts and diversity of data. Artificial intelligence (AI) is becoming a game changer in turning the vast seas of data into valuable predictions and insights. However, this requires specialized programming skills and an in-depth understanding of machine learning, deep learning, and ensemble learning algorithms. Here, we attempt to methodically review the usage of different tools, technologies, and services available to the phenomics data community and show how they can be applied to selected problems in explainable AI-based image analysis. This tutorial provides practical and useful resources for novices and experts to harness the potential of the phenomic data in explainable AI-led breeding programs.
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Affiliation(s)
- Farid Nakhle
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
| | - Antoine L. Harfouche
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
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Hu D, Jing J, Snowdon RJ, Mason AS, Shen J, Meng J, Zou J. Exploring the gene pool of Brassica napus by genomics-based approaches. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:1693-1712. [PMID: 34031989 PMCID: PMC8428838 DOI: 10.1111/pbi.13636] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/13/2021] [Accepted: 05/14/2021] [Indexed: 05/08/2023]
Abstract
De novo allopolyploidization in Brassica provides a very successful model for reconstructing polyploid genomes using progenitor species and relatives to broaden crop gene pools and understand genome evolution after polyploidy, interspecific hybridization and exotic introgression. B. napus (AACC), the major cultivated rapeseed species and the third largest oilseed crop in the world, is a young Brassica species with a limited genetic base resulting from its short history of domestication, cultivation, and intensive selection during breeding for target economic traits. However, the gene pool of B. napus has been significantly enriched in recent decades that has been benefit from worldwide effects by the successful introduction of abundant subgenomic variation and novel genomic variation via intraspecific, interspecific and intergeneric crosses. An important question in this respect is how to utilize such variation to breed crops adapted to the changing global climate. Here, we review the genetic diversity, genome structure, and population-level differentiation of the B. napus gene pool in relation to known exotic introgressions from various species of the Brassicaceae, especially those elucidated by recent genome-sequencing projects. We also summarize progress in gene cloning, trait-marker associations, gene editing, molecular marker-assisted selection and genome-wide prediction, and describe the challenges and opportunities of these techniques as molecular platforms to exploit novel genomic variation and their value in the rapeseed gene pool. Future progress will accelerate the creation and manipulation of genetic diversity with genomic-based improvement, as well as provide novel insights into the neo-domestication of polyploid crops with novel genetic diversity from reconstructed genomes.
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Affiliation(s)
- Dandan Hu
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Jinjie Jing
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Rod J. Snowdon
- Department of Plant BreedingIFZ Research Centre for Biosystems, Land Use and NutritionJustus Liebig UniversityGiessenGermany
| | - Annaliese S. Mason
- Department of Plant BreedingIFZ Research Centre for Biosystems, Land Use and NutritionJustus Liebig UniversityGiessenGermany
- Plant Breeding DepartmentINRESThe University of BonnBonnGermany
| | - Jinxiong Shen
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Jinling Meng
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Jun Zou
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
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40
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Al-Lami MK, Nguyen D, Oustriere N, Burken JG. High throughput screening of native species for tailings eco-restoration using novel computer visualization for plant phenotyping. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 780:146490. [PMID: 34030344 DOI: 10.1016/j.scitotenv.2021.146490] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 03/09/2021] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
Historical hard-rock mine activities have resulted in nearly half a million mining-impacted sites scattered around the US. Compared to conventional remediation, (aided) phytostabilization is generally cost-effective and ecologically productive approach, particularly for large-scale sites. Native species act to maintain higher local biodiversity, providing a foundation for natural ecological succession. Due to heterogeneity of mine waste, revegetation strategies are inconsistent in approach, and to avoid failure scenarios, greenhouse screening studies can identify candidate plants and amendment strategies before scaling up. This greenhouse study aimed to concurrently screen a variety of native species for their potential to revegetate Cu/Pb/Zn mine tailings and develop a high throughput and non-destructive approach utilizing computer vision and image-based phenotyping technologies to quantify plant responses. A total number of 34 species were screened in this study, which included: 5 trees, 8 grasses, and 21 forbs and legumes. Most of the species tested were Missouri native and prairie species. Plants were non-destructively imaged, and 15 shape and color phenotypic attributes were extracted utilizing computer vision techniques of PlantCV. Compared to reference soil, all species tested were negatively impacted by the tailings' characteristics, with lowest tolerance generally observed in tree species. However, significant improvement in plant growth and tolerance generally observed with biosolids addition with biomass surpassing reference soil for most legumes. Accumulation of Cu, Pb, and Zn was below Domestic Animal Toxicity Limits in most species. Statistically robust differences in species responses were observed using phenotypic data, such as area, height, width, color, and 9 other morphological attributes. Correlations with destructive data indicated that area displayed the greatest positive correlation with biomass and color the greatest negative correlation with shoot metals. Computer visualization greatly increased the phenotypic data and offers a breakthrough in rapid, high throughput data collection to project site-specific phytostabilization strategies to efficiently restore mine-impacted sites.
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Affiliation(s)
- Mariam K Al-Lami
- Department of Civil, Architectural and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65409, United States of America.
| | - Dane Nguyen
- Department of Civil, Architectural and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65409, United States of America.
| | - Nadège Oustriere
- Laboratoire Génie Civil Et Géoenvironnement (LGCgE), Yncréa Hauts-De-France, Institut Supérieur Agriculture, 48 Boulevard Vauban, 59046 Lille Cedex, France.
| | - Joel G Burken
- Department of Civil, Architectural and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65409, United States of America.
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Souza A, Yang Y. High-Throughput Corn Image Segmentation and Trait Extraction Using Chlorophyll Fluorescence Images. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9792582. [PMID: 34382005 PMCID: PMC8323024 DOI: 10.34133/2021/9792582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 06/29/2021] [Indexed: 06/13/2023]
Abstract
Plant segmentation and trait extraction for individual organs are two of the key challenges in high-throughput phenotyping (HTP) operations. To address this challenge, the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University utilizes chlorophyll fluorescence images (CFIs) to enable consistent and efficient automatic segmentation of plants of different species, age, or color. A series of image analysis routines were also developed to facilitate the quantitative measurements of key corn plant traits. A proof-of-concept experiment was conducted to demonstrate the utility of the extracted traits in assessing drought stress reaction of corn plants. The image analysis routines successfully measured several corn morphological characteristics for different sizes such as plant height, area, top-node height and diameter, number of leaves, leaf area, and angle in relation to the stem. Data from the proof-of-concept experiment showed how corn plants behaved when treated with different water regiments or grown in pot of different sizes. High-throughput image segmentation and analysis basing on a plant's fluorescence image was proved to be efficient and reliable. Extracted trait on the segmented stem and leaves of a corn plant demonstrated the importance and utility of this kind of trait data in evaluating the performance of corn plant under stress. Data collected from corn plants grown in pots of different volumes showed the importance of using pot of standard size when conducting and reporting plant phenotyping data in a controlled-environment facility.
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Affiliation(s)
- Augusto Souza
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, USA
| | - Yang Yang
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, USA
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42
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Pandey AK, Jiang L, Moshelion M, Gosa SC, Sun T, Lin Q, Wu R, Xu P. Functional physiological phenotyping with functional mapping: A general framework to bridge the phenotype-genotype gap in plant physiology. iScience 2021; 24:102846. [PMID: 34381971 PMCID: PMC8333144 DOI: 10.1016/j.isci.2021.102846] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 05/27/2021] [Accepted: 07/09/2021] [Indexed: 11/19/2022] Open
Abstract
The recent years have witnessed the emergence of high-throughput phenotyping techniques. In particular, these techniques can characterize a comprehensive landscape of physiological traits of plants responding to dynamic changes in the environment. These innovations, along with the next-generation genomic technologies, have brought plant science into the big-data era. However, a general framework that links multifaceted physiological traits to DNA variants is still lacking. Here, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM). This integration, implemented with high-dimensional statistical reasoning, can aid in our understanding of how genotype is translated toward phenotype. As a demonstration of method, we implemented the transpiration and soil-plant-atmosphere measurements of a tomato introgression line population into the FPP-FM framework, facilitating the identification of quantitative trait loci (QTLs) that mediate the spatiotemporal change of transpiration rate and the test of how these QTLs control, through their interaction networks, phenotypic plasticity under drought stress.
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Affiliation(s)
- Arun K. Pandey
- College of Life Sciences, China Jiliang University, Hangzhou 310018, China
| | - Libo Jiang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100080, China
| | - Menachem Moshelion
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 76100, Israel
- Corresponding author
| | - Sanbon Chaka Gosa
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 76100, Israel
| | - Ting Sun
- College of Life Sciences, China Jiliang University, Hangzhou 310018, China
| | - Qin Lin
- Biozeron Biotechnology Co., Ltd, Shanghai 201800, China
| | - Rongling Wu
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA
- Corresponding author
| | - Pei Xu
- College of Life Sciences, China Jiliang University, Hangzhou 310018, China
- Corresponding author
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Atefi A, Ge Y, Pitla S, Schnable J. Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives. FRONTIERS IN PLANT SCIENCE 2021; 12:611940. [PMID: 34249028 PMCID: PMC8267384 DOI: 10.3389/fpls.2021.611940] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 05/14/2021] [Indexed: 05/18/2023]
Abstract
Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era.
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Affiliation(s)
- Abbas Atefi
- Department of Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE, United States
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE, United States
| | - Santosh Pitla
- Department of Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE, United States
| | - James Schnable
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, United States
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Feldman A, Wang H, Fukano Y, Kato Y, Ninomiya S, Guo W. EasyDCP: An affordable, high‐throughput tool to measure plant phenotypic traits in 3D. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13645] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Alexander Feldman
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
| | - Haozhou Wang
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
| | - Yuya Fukano
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
| | - Yoichiro Kato
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
| | - Seishi Ninomiya
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
- Plant Phenomics Research Center Nanjing Agricultural University Nanjing China
| | - Wei Guo
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
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Renaud JB, DesRochers N, Hoogstra S, Garnham CP, Sumarah MW. Structure Activity Relationship for Fumonisin Phytotoxicity. Chem Res Toxicol 2021; 34:1604-1611. [PMID: 33891387 DOI: 10.1021/acs.chemrestox.1c00057] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Fumonisins are mycotoxins produced by a number of species of Fusarium and Aspergillus. They are polyketides that possess a linear polyol structure with two tricarballylic acid side chains and an amine moiety. Toxicity results from their inhibition of Ceramide Synthase (CerS), which perturbs sphingolipid concentrations. The tricarballylic side chains and amine group of fumonisins are key molecular features responsible for inhibiting CerS, however their individual contributions toward overall toxicity are not fully understood. We have recently reported novel, deaminated fumonisins produced by A. niger and have identified an enzyme (AnFAO) responsible for their synthesis. Here we performed a structure/function activity assay to investigate the individual contributions of the tricarballylic acid and amine toward overall fumonisin toxicity. Lemna minor was treated at 40 μM against FB1, hydrolyzed FB1 (hFB1), deaminated FB1 (FPy1), or hydrolyzed/deaminated (hFPy1). Four end points were monitored: plant dry weight, frond surface area, lipidomics, and metabolomics. Overall, hFB1 was less toxic than FB1 and FPy1 was less toxic than hFB1. hFPy1 which lacks both the amine group and tricarballylic side chains was also less toxic than FB1 and hFB1, however it was not significantly less toxic than FPy1. Lipidomic analysis showed that FB1 treatment significantly increased levels of phosphotidylcholines, ceramides, and pheophorbide A, while significantly decreasing the levels of diacylglycerides, sulfoquinovosyl diacylglycerides, and chlorophyll. Metabolomic profiling revealed a number of significantly increased compounds that were unique to FB1 treatment including phenylalanine, asymmetric dimethylarginine (ADMA), S-methylmethionine, saccharopine, and tyrosine. Conversely, citrulline, N-acetylornithine and ornithine were significantly elevated in the presence of hFB1 but not any of the other fumonisin analogues. These data provide evidence that although removal of the tricarballylic side chains significantly reduces toxicity of fumonisins, the amine functional group is a key contributor to fumonisin toxicity in L. minor and justify future toxicity studies in mammalian systems.
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Affiliation(s)
- Justin B Renaud
- London Research and Development Centre, Agriculture and Agri-Food Canada, 1391 Sandford Street, London, Ontario N5V 4T3, Canada
| | - Natasha DesRochers
- London Research and Development Centre, Agriculture and Agri-Food Canada, 1391 Sandford Street, London, Ontario N5V 4T3, Canada
| | - Shawn Hoogstra
- London Research and Development Centre, Agriculture and Agri-Food Canada, 1391 Sandford Street, London, Ontario N5V 4T3, Canada
| | - Christopher P Garnham
- London Research and Development Centre, Agriculture and Agri-Food Canada, 1391 Sandford Street, London, Ontario N5V 4T3, Canada
| | - Mark W Sumarah
- London Research and Development Centre, Agriculture and Agri-Food Canada, 1391 Sandford Street, London, Ontario N5V 4T3, Canada
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Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field. PLANTS 2021; 10:plants10050840. [PMID: 33921967 PMCID: PMC8143480 DOI: 10.3390/plants10050840] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/15/2021] [Accepted: 04/19/2021] [Indexed: 12/04/2022]
Abstract
Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.
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Li Q, Liu N, Liu Q, Zheng X, Lu L, Gao W, Liu Y, Liu Y, Zhang S, Wang Q, Pan J, Chen C, Mi Y, Yang M, Cheng X, Ren G, Yuan YW, Zhang X. DEAD-box helicases modulate dicing body formation in Arabidopsis. SCIENCE ADVANCES 2021; 7:7/18/eabc6266. [PMID: 33910901 PMCID: PMC8081359 DOI: 10.1126/sciadv.abc6266] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 03/10/2021] [Indexed: 05/02/2023]
Abstract
Eukaryotic cells contain numerous membraneless organelles that are made from liquid droplets of proteins and nucleic acids and that provide spatiotemporal control of various cellular processes. However, the molecular mechanisms underlying the formation and rapid stress-induced alterations of these organelles are relatively uncharacterized. Here, we investigated the roles of DEAD-box helicases in the formation and alteration of membraneless nuclear dicing bodies (D-bodies) in Arabidopsis thaliana We uncovered that RNA helicase 6 (RH6), RH8, and RH12 are previously unidentified D-body components. These helicases interact with and promote the phase separation of SERRATE, a key component of D-bodies, and drive the formation of D-bodies through liquid-liquid phase separations (LLPSs). The accumulation of these helicases in the nuclei decreases upon Turnip mosaic virus infections, which couples with the decrease of D-bodies. Our results thus reveal the key roles of RH6, RH8, and RH12 in modulating D-body formation via LLPSs.
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Affiliation(s)
- Qi Li
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Ningkun Liu
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qing Liu
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xingguo Zheng
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - Lu Lu
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Wenrui Gao
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yang Liu
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yan Liu
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shicheng Zhang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qian Wang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Pan
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Chen Chen
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yingjie Mi
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Life Sciences, Henan Normal University, Xinxiang, Henan 453007, China
| | - Meiling Yang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaofei Cheng
- Key Laboratory of Germplasm Enhancement, Physiology and Ecology of Food Crops in Cold Region of Chinese Education Ministry, College of Agriculture, Northeast Agricultural University, Harbin 150030, China
| | - Guodong Ren
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Plant Biology, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Yao-Wu Yuan
- Department of Ecology and Evolutionary Biology, University of Connecticut, 75 North Eagleville Road, Unit 3043, Storrs, CT 06269, USA
| | - Xiaoming Zhang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
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48
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Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041403] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.
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49
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Li D, Quan C, Song Z, Li X, Yu G, Li C, Muhammad A. High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field. Front Bioeng Biotechnol 2021; 8:623705. [PMID: 33520974 PMCID: PMC7838587 DOI: 10.3389/fbioe.2020.623705] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/15/2020] [Indexed: 11/13/2022] Open
Abstract
Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.
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Affiliation(s)
- Daoliang Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Chaoqun Quan
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhaoyang Song
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Xiang Li
- Department of Psychology, College of Education, Hubei University, Wuhan, China
| | - Guanghui Yu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Cheng Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Akhter Muhammad
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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50
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Gupta S, Huang CH, Singh GP, Park BS, Chua NH, Ram RJ. Portable Raman leaf-clip sensor for rapid detection of plant stress. Sci Rep 2020; 10:20206. [PMID: 33214575 PMCID: PMC7677326 DOI: 10.1038/s41598-020-76485-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/26/2020] [Indexed: 12/18/2022] Open
Abstract
Precision agriculture requires new technologies for rapid diagnosis of plant stresses, such as nutrient deficiency and drought, before the onset of visible symptoms and subsequent yield loss. Here, we demonstrate a portable Raman probe that clips around a leaf for rapid, in vivo spectral analysis of plant metabolites including carotenoids and nitrates. We use the leaf-clip Raman sensor for early diagnosis of nitrogen deficiency of the model plant Arabidopsis thaliana as well as two important vegetable crops, Pak Choi (Brassica rapa chinensis) and Choy Sum (Brassica rapa var. parachinensis). In vivo measurements using the portable leaf-clip Raman sensor under full-light growth conditions were consistent with those obtained with a benchtop Raman spectrometer measurements on leaf-sections under laboratory conditions. The portable leaf-clip Raman sensor offers farmers and plant scientists a new precision agriculture tool for early diagnosis and real-time monitoring of plant stresses in field conditions.
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Affiliation(s)
- Shilpi Gupta
- Disruptive & Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, 1 Create Way, #03-06/07/8 Research Wing, Singapore, 138602, Singapore
| | - Chung Hao Huang
- Temasek Life Science Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore
| | - Gajendra Pratap Singh
- Disruptive & Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, 1 Create Way, #03-06/07/8 Research Wing, Singapore, 138602, Singapore
| | - Bong Soo Park
- Temasek Life Science Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore
| | - Nam-Hai Chua
- Disruptive & Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, 1 Create Way, #03-06/07/8 Research Wing, Singapore, 138602, Singapore.
- Temasek Life Science Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore.
| | - Rajeev J Ram
- Disruptive & Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, 1 Create Way, #03-06/07/8 Research Wing, Singapore, 138602, Singapore.
- Research Laboratory of Electronics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 36-491, Cambridge, MA, 02139, USA.
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