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Loladze A, Rodrigues F, Petroli CD, Muñoz-Zavala C, Naranjo S, Vicente FS, Gerard B, Montesinos-Lopez OA, Crossa J, Martini JW. Multispectral and thermal infrared data, visual scores for severity of common rust symptoms, and genotypic single nucleotide polymorphism data of three F2-derived biparental doubled-haploid maize populations. Data Brief 2024; 54:110300. [PMID: 38586147 PMCID: PMC10997887 DOI: 10.1016/j.dib.2024.110300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 04/09/2024] Open
Abstract
Three F2-derived biparental doubled haploid (DH) maize populations were generated for genetic mapping of resistance to common rust. Each of the three populations has the same susceptible parent, but a different resistance donor parent. Population 1 and 3 consist of 320 lines each, population 2 consists of 260 lines. The DH lines were evaluated for their susceptibility to common rust in two years and with two replications in each year. For phenotyping, a visual score (VS) for susceptibility was assigned. Additionally, unmanned aerial vehicle (UAV) derived multispectral and thermal infrared data was recorded and combined in different vegetation indices ("remote sensing", RS). The DH lines were genotyped with the DarTseq method, to obtain data on single nucleotide polymorphisms (SNPs). After quality control, 9051 markers remained. Missing values were "imputed" by the empirical mean of the marker scores of the respective locus. We used the data for comparison of genome-wide association studies and genomic prediction when based on different phenotyping methods, that is either VS or RS data. The data may be interesting for reuse for instance for benchmarking genomic prediction models, for phytopathological studies addressing common rust, or for specifications of vegetation indices.
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Affiliation(s)
| | | | - Cesar D. Petroli
- International Maize and Wheat Improvement Center – CIMMYT, Mexico
| | | | - Sergio Naranjo
- International Maize and Wheat Improvement Center – CIMMYT, Mexico
| | | | - Bruno Gerard
- International Maize and Wheat Improvement Center – CIMMYT, Mexico
- College of Agriculture and Environmental Sciences (CAES), University Mohammed VI Polytechnic (UM6P), Ben Guerir, Morocco
| | | | - Jose Crossa
- International Maize and Wheat Improvement Center – CIMMYT, Mexico
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Ikram M, Rauf A, Rao MJ, Maqsood MFK, Bakhsh MZM, Ullah M, Batool M, Mehran M, Tahira M. CRISPR-Cas9 based molecular breeding in crop plants: a review. Mol Biol Rep 2024; 51:227. [PMID: 38281301 DOI: 10.1007/s11033-023-09086-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/30/2023] [Indexed: 01/30/2024]
Abstract
Traditional crop breeding techniques are not quickly boosting yields to fulfill the expanding population needs. Long crop lifespans hinder the ability of plant breeding to develop superior crop varieties. Due to the arduous crossing, selecting, and challenging processes, it can take decades to establish new varieties with desired agronomic traits. Develop new plant varieties instantly to reduce hunger and improve food security. As a result of the adoption of conventional agricultural techniques, crop genetic diversity has decreased over time. Several traditional and molecular techniques, such as genetic selection, mutant breeding, somaclonal variation, genome-wide association studies, and others, have improved agronomic traits associated with agricultural plant productivity, quality, and resistance to biotic and abiotic stresses. In addition, modern genome editing approaches based on programmable nucleases, CRISPR, and Cas9 proteins have escorted an exciting new era of plant breeding. Plant breeders and scientists worldwide rely on cutting-edge techniques like quick breeding, genome editing tools, and high-throughput phenotyping to boost crop breeding output. This review compiles discoveries in numerous areas of crop breeding, such as using genome editing tools to accelerate the breeding process and create yearly crop generations with the desired features, to describe the shift from conventional to modern plant breeding techniques.
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Affiliation(s)
- Muhammad Ikram
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Abdul Rauf
- National Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan, 430070, Hubei, China
| | - Muhammad Junaid Rao
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, 100 Daxue Rd., Nanning, 530004, China.
| | | | | | - Maaz Ullah
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Maria Batool
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Muhammad Mehran
- Key Laboratory of Arable Land Conservation, Huazhong Agricultural University, Ministry of Agriculture, Wuhan, 430070, China
| | - Maryam Tahira
- National Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan, 430070, Hubei, China
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Mróz T, Shafiee S, Crossa J, Montesinos-Lopez OA, Lillemo M. Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat. Mol Breed 2024; 44:5. [PMID: 38230361 PMCID: PMC10789716 DOI: 10.1007/s11032-024-01449-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/08/2024] [Indexed: 01/18/2024]
Abstract
With abundant available genomic data, genomic selection has become routine in many plant breeding programs. Multispectral data captured by UAVs showed potential for grain yield (GY) prediction in many plant species using machine learning; however, the possibilities of utilizing this data to augment genomic prediction models still need to be explored. We collected high-throughput phenotyping (HTP) multispectral data in a genotyped multi-environment large-scale field trial using two cost-effective cameras to fill this gap. We tested back to back the prediction ability of GY prediction models, including genomic (G matrix), multispectral-derived (M matrix), and environmental (E matrix) relationships using best linear unbiased predictor (BLUP) methodology in single and multi-environment scenarios. We discovered that M allows for GY prediction comparable to the G matrix and that models using both G and M matrices show superior accuracies and errors compared with G or M alone, both in single and multi-environment scenarios. We showed that the M matrix is not entirely environment-specific, and the genotypic relationships become more robust with more data capture sessions over the season. We discovered that the optimal time for data capture occurs during grain filling and that camera bands with the highest heritability are important for GY prediction using the M matrix. We showcased that GY prediction can be performed using only an RGB camera, and even a single data capture session can yield valuable data for GY prediction. This study contributes to a better understanding of multispectral data and its relationships. It provides a flexible framework for improving GS protocols without significant investments or software customization. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-024-01449-w.
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Affiliation(s)
- Tomasz Mróz
- Department of Plant Sciences, Norwegian University of Life Sciences, NO-1432 Ås, Norway
| | - Sahameh Shafiee
- Department of Plant Sciences, Norwegian University of Life Sciences, NO-1432 Ås, Norway
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico Veracruz, CP 52640 Texcoco, Edo. de Mexico Mexico
- Colegio de Postgraduados, CP 56230 Montecillos, Edo. de Mexico Mexico
| | | | - Morten Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences, NO-1432 Ås, Norway
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Daviet B, Fournier C, Cabrera-Bosquet L, Simonneau T, Cafier M, Romieu C. Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images. Plant Methods 2023; 19:146. [PMID: 38098093 PMCID: PMC10720176 DOI: 10.1186/s13007-023-01125-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. RESULTS First, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. CONCLUSIONS We successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images acquired in laboratory conditions. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one cultivar, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.
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Affiliation(s)
- Benoit Daviet
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | | | - Maxence Cafier
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Charles Romieu
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
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Barreto Ortiz J, Hirsch CN, Ehlke NJ, Watkins E. SpykProps: an imaging pipeline to quantify architecture in unilateral grass inflorescences. Plant Methods 2023; 19:125. [PMID: 37957737 PMCID: PMC10644492 DOI: 10.1186/s13007-023-01104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/06/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND Inflorescence properties such length, spikelet number, and their spatial distribution across the rachis, are fundamental indicators of seed productivity in grasses and have been a target of selection throughout domestication and crop improvement. However, quantifying such complex morphology is laborious, time-consuming, and commonly limited to human-perceived traits. These limitations can be exacerbated by unfavorable trait correlations between inflorescence architecture and seed yield that can be unconsciously selected for. Computer vision offers an alternative to conventional phenotyping, enabling higher throughput and reducing subjectivity. These approaches provide valuable insights into the determinants of seed yield, and thus, aid breeding decisions. RESULTS Here, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences, that was developed and tested on images of perennial grass (Lolium perenne L.) spikes. SpykProps is able to rapidly and accurately identify spikes (RMSE < 1), estimate their length (R2 = 0.96), and number of spikelets (R2 = 0.61). It also quantifies color and shape from hundreds of interacting descriptors that are accurate predictors of architectural and agronomic traits such as seed yield potential (R2 = 0.94), rachis weight (R2 = 0.83), and seed shattering (R2 = 0.85). CONCLUSIONS SpykProps is an open-source platform to characterize inflorescence architecture in a wide range of grasses. This imaging tool generates conventional and latent traits that can be used to better characterize developmental and agronomic traits associated with inflorescence architecture, and has applications in fields that include breeding, physiology, evolution, and development biology.
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Affiliation(s)
- Joan Barreto Ortiz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
- Department of Horticultural Science, University of Minnesota, St. Paul, MN, 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Nancy Jo Ehlke
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Eric Watkins
- Department of Horticultural Science, University of Minnesota, St. Paul, MN, 55108, USA.
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Anshori MF, Dirpan A, Sitaresmi T, Rossi R, Farid M, Hairmansis A, Sapta Purwoko B, Suwarno WB, Nugraha Y. An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review. Heliyon 2023; 9:e21650. [PMID: 38027954 PMCID: PMC10660044 DOI: 10.1016/j.heliyon.2023.e21650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.
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Affiliation(s)
| | - Andi Dirpan
- Department of Agricultural Technology, Hasanuddin University, Makassar, 90245, Indonesia
- Center of Excellence in Science and Technology on Food Product Diversification, 90245, Makassar, Indonesia
| | - Trias Sitaresmi
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Riccardo Rossi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence (UNIFI), Piazzale delle Cascine 18, 50144, Florence, Italy
| | - Muh Farid
- Department of Agronomy, Hasanuddin University, Makassar, 90245, Indonesia
| | - Aris Hairmansis
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Bambang Sapta Purwoko
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Willy Bayuardi Suwarno
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Yudhistira Nugraha
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
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Ranđelović P, Đorđević V, Miladinović J, Prodanović S, Ćeran M, Vollmann J. High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data. Plant Methods 2023; 19:89. [PMID: 37633921 PMCID: PMC10463513 DOI: 10.1186/s13007-023-01054-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/15/2023] [Indexed: 08/28/2023]
Abstract
BACKGROUND Biomass accumulation as a growth indicator can be significant in achieving high and stable soybean yields. More robust genotypes have a better potential for exploiting available resources such as water or sunlight. Biomass data implemented as a new trait in soybean breeding programs could be beneficial in the selection of varieties that are more competitive against weeds and have better radiation use efficiency. The standard techniques for biomass determination are invasive, inefficient, and restricted to one-time point per plot. Machine learning models (MLMs) based on the multispectral (MS) images were created so as to overcome these issues and provide a non-destructive, fast, and accurate tool for in-season estimation of soybean fresh biomass (FB). The MS photos were taken during two growing seasons of 10 soybean varieties, using six-sensor digital camera mounted on the unmanned aerial vehicle (UAV). For model calibration, canopy cover (CC), plant height (PH), and 31 vegetation index (VI) were extracted from the images and used as predictors in the random forest (RF) and partial least squares regression (PLSR) algorithm. To create a more efficient model, highly correlated VIs were excluded and only the triangular greenness index (TGI) and green chlorophyll index (GCI) remained. RESULTS More precise results with a lower mean absolute error (MAE) were obtained with RF (MAE = 0.17 kg/m2) compared to the PLSR (MAE = 0.20 kg/m2). High accuracy in the prediction of soybean FB was achieved using only four predictors (CC, PH and two VIs). The selected model was additionally tested in a two-year trial on an independent set of soybean genotypes in drought simulation environments. The results showed that soybean grown under drought conditions accumulated less biomass than the control, which was expected due to the limited resources. CONCLUSION The research proved that soybean FB could be successfully predicted using UAV photos and MLM. The filtration of highly correlated variables reduced the final number of predictors, improving the efficiency of remote biomass estimation. The additional testing conducted in the independent environment proved that model is capable to distinguish different values of soybean FB as a consequence of drought. Assessed variability in FB indicates the robustness and effectiveness of the proposed model, as a novel tool for the non-destructive estimation of soybean FB.
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Affiliation(s)
- Predrag Ranđelović
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000, Novi Sad, Serbia.
| | - Vuk Đorđević
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000, Novi Sad, Serbia
| | - Jegor Miladinović
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000, Novi Sad, Serbia
| | - Slaven Prodanović
- Faculty of Agriculture, Department of Genetics, Plant Breeding and Seed Science, University of Belgrade, Nemanjina 6, 11080, Zemun-Belgrade, Serbia
| | - Marina Ćeran
- Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000, Novi Sad, Serbia
| | - Johann Vollmann
- Department of Crop Sciences, Institute of Plant Breeding, University of Natural Resources and Life Sciences, Konrad Lorenz Str. 24, 3430, Vienna, Tulln an der Donau, Austria
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Li F, Tarkington J, Sherlock G. Fit-Seq2.0: An Improved Software for High-Throughput Fitness Measurements Using Pooled Competition Assays. J Mol Evol 2023; 91:334-344. [PMID: 36877292 PMCID: PMC10276102 DOI: 10.1007/s00239-023-10098-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/02/2023] [Indexed: 03/07/2023]
Abstract
The fitness of a genotype is defined as its lifetime reproductive success, with fitness itself being a composite trait likely dependent on many underlying phenotypes. Measuring fitness is important for understanding how alteration of different cellular components affects a cell's ability to reproduce. Here, we describe an improved approach, implemented in Python, for estimating fitness in high throughput via pooled competition assays.
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Affiliation(s)
- Fangfei Li
- Department of Genetics, Stanford University, Stanford, USA
| | | | - Gavin Sherlock
- Department of Genetics, Stanford University, Stanford, USA.
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9
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Schmidt L, Jacobs J, Schmutzer T, Alqudah AM, Sannemann W, Pillen K, Maurer A. Identifying genomic regions determining shoot and root traits related to nitrogen uptake efficiency in a multiparent advanced generation intercross (MAGIC) winter wheat population in a high-throughput phenotyping facility. Plant Sci 2023; 330:111656. [PMID: 36841338 DOI: 10.1016/j.plantsci.2023.111656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 01/17/2023] [Accepted: 02/18/2023] [Indexed: 06/18/2023]
Abstract
In the context of a continuously increasing human population that needs to be fed, with environmental protection in mind, nitrogen use efficiency (NUE) improvement is becoming very important. To understand the natural variation of traits linked to nitrogen uptake efficiency (UPE), one component of NUE, the multiparent advanced generation intercross (MAGIC) winter wheat population WM-800 was phenotyped under two contrasting nitrogen (N) levels in a high-throughput phenotyping facility for six weeks. Three biomass-related, three root-related, and two reflectance-related traits were measured weekly under each treatment. Subsequently, the population was genetically analysed using a total of 13,060 polymorphic haplotypes and singular SNPs for a genome-wide association study (GWAS). In total, we detected 543 quantitative trait loci (QTL) across all time points and traits, which were pooled into 42 stable QTL (sQTL; present in at least three of the six weeks). Besides Rht-B1 and Rht-D1, candidate genes playing a role in gibberellic acid-regulated growth and nitrate transporter genes from the NPF gene family, like NRT 1.1, were linked to sQTL. Two novel sQTL on chromosomes 5 A and 6D showed pleiotropic effects on several traits. The high number of N-specific sQTL indicates that selection for UPE is useful specifically under N-limited conditions.
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Affiliation(s)
- Laura Schmidt
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - John Jacobs
- BASF BBCC Innovation Center Gent, 9052 Gent, Belgium
| | - Thomas Schmutzer
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Ahmad M Alqudah
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany; Biological Science Program, Department of Biological and Environmental Sciences, College of Art and Science, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Wiebke Sannemann
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Klaus Pillen
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Andreas Maurer
- Martin Luther University Halle-Wittenberg, Chair of Plant Breeding, Betty-Heimann-Str. 3, 06120 Halle, Germany.
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Wong CYS, Jones T, McHugh DP, Gilbert ME, Gepts P, Palkovic A, Buckley TN, Magney TS. TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology. Plant Methods 2023; 19:29. [PMID: 36978119 PMCID: PMC10044391 DOI: 10.1186/s13007-023-01001-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF). RESULTS We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions. CONCLUSIONS TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.
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Affiliation(s)
| | - Taylor Jones
- Department of Earth & Environment, Boston University, Boston, MA 02215 USA
| | - Devin P. McHugh
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Matthew E. Gilbert
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Paul Gepts
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Antonia Palkovic
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Thomas N. Buckley
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Troy S. Magney
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
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11
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Gillette S, Yin L, Kianian PMA, Pawlowski WP, Chen C. Corn360: a method for quantification of corn kernels. Plant Methods 2023; 19:23. [PMID: 36894953 PMCID: PMC9996904 DOI: 10.1186/s13007-023-00995-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND The rapidly advancing corn breeding field calls for high-throughput methods to phenotype corn kernel traits to estimate yield and to study their genetic inheritance. Most of the existing methods are reliant on sophisticated setup, expertise in statistical models and programming skills for image capturing and analysis. RESULTS We demonstrated a portable, easily accessible, affordable, panoramic imaging capturing system called Corn360, followed by image analysis using freely available software, to characterize total kernel count and different patterned kernel counts of a corn ear. The software we used did not require programming skills and utilized Artificial Intelligence to train a model and to segment the images of mixed patterned corn ears. For homogeneously patterned corn ears, our results showed accuracies of 93.7% of total kernel count compared to manual counting. Our method allowed to save an average of 3 min 40 s per image. For mixed patterned corn ears, our results showed accuracies of 84.8% or 61.8% of segmented kernel counts. Our method has the potential to greatly decrease counting time per image as the number of images increases. We also demonstrated a case of using Corn360 to count different categories of kernels on a mixed patterned corn ear resulting from a cross of sweet corn and sticky corn and showed that starch:sweet:sticky segregated in a 9:4:3 ratio in its F2 population. CONCLUSIONS The panoramic Corn360 approach enables for a portable low-cost high-throughput kernel quantification. This includes total kernel quantification and quantification of different patterned kernels. This can allow for quick estimate of yield component and for categorization of different patterned kernels to study the inheritance of genes controlling color and texture. We demonstrated that using the samples resulting from a sweet × sticky cross, the starchiness, sweetness and stickiness in this case were controlled by two genes with epistatic effects. Our achieved results indicate Corn360 can be used to effectively quantify corn kernels in a portable and cost-efficient way that is easily accessible with or without programming skills.
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Affiliation(s)
- Samantha Gillette
- School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA
| | - Lu Yin
- School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA
| | - Penny M A Kianian
- PepsiCo Inc., 210 Borlaug Hall, 1991 Upper Buford Circle, St. Paul, MN, 55108, USA
| | | | - Changbin Chen
- School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA.
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12
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Daviet B, Fernandez R, Cabrera-Bosquet L, Pradal C, Fournier C. PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time. Plant Methods 2022; 18:130. [PMID: 36482291 PMCID: PMC9730636 DOI: 10.1186/s13007-022-00961-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
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Affiliation(s)
- Benoit Daviet
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Romain Fernandez
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France
| | | | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France.
- Inria & LIRMM, CNRS, Univ Montpellier, Montpellier, France.
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13
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Fernandez R, Crabos A, Maillard M, Nacry P, Pradal C. High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings. Plant Methods 2022; 18:127. [PMID: 36457133 PMCID: PMC9714072 DOI: 10.1186/s13007-022-00960-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. RESULTS We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ([Formula: see text] and [Formula: see text] for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ([Formula: see text] for lateral root growth). CONCLUSIONS We designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.
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Affiliation(s)
- Romain Fernandez
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Amandine Crabos
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Morgan Maillard
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Philippe Nacry
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France.
| | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
- Inria & LIRMM, Univ Montpellier, CNRS, Montpellier, France.
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14
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Thoday-Kennedy E, Dimech AM, Joshi S, Daetwyler HD, Hudson D, Spangenberg G, Hayden M, Kant S. An image dataset of diverse safflower (Carthamus tinctorius L.) genotypes for salt response phenotyping. Data Brief 2023; 46:108787. [PMID: 36506801 DOI: 10.1016/j.dib.2022.108787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 11/30/2022] Open
Abstract
This article describes a dataset of high-resolution visible-spectrum images of safflower (Carthamus tinctorius L.) plants obtained from a LemnaTec Scanalyser automated phenomics platform along with the associated image analysis output and manually acquired biomass data. This series contains 1832 images of 200 diverse safflower genotypes, acquired at the Plant Phenomics Victoria, Horsham, Victoria, Australia. Two Prosilica GT RGB (red-green-blue) cameras were used to generate 6576 × 4384 pixel portable network graphic (PNG) images. Safflower genotypes were either subjected to a salt treatment (250 mM NaCl) or grown as a control (0 mM NaCl) and imaged daily from 15 to 36 days after sowing. Each snapshot consists of four images collected at a point in time; one of which is taken from above (top-view) and the remainder from the side at either 0°, 120° or 240°. The dataset also includes analysis output quantifying traits and describing phenotypes, as well as manually collected biomass and leaf ion content data. The usage of the dataset is already demonstrated in Thoday-Kennedy et al. (2021) [1]. This dataset describes the early growth differences of diverse safflower genotypes and identified genotypes tolerant or susceptible to salinity stress. This dataset provides detailed image analysis parameters for phenotyping a large population of safflower that can be used for the training of image-based trait identification pipelines for a wide range of crop species.
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Abstract
Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, generating massive amounts of data, which require an automated analysis to extract meaningful information. Deep learning is a branch of machine learning that has revolutionized many fields of research. Deep learning models are able to extract autonomously the underlying features within the dataset, providing a multi-level representation of the data. Our intention is to show the feasibility and effectiveness of using deep learning and low-cost technology for automated phenotyping. In this methods chapter, we describe how to train a deep neural network to segment leaf images and extract the pixels related to the disease.
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16
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Buchaillot ML, Soba D, Shu T, Liu J, Aranjuelo I, Araus JL, Runion GB, Prior SA, Kefauver SC, Sanz-Saez A. Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models. Planta 2022; 255:93. [PMID: 35325309 PMCID: PMC8948130 DOI: 10.1007/s00425-022-03867-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
MAIN CONCLUSION By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.
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Affiliation(s)
- Ma Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - David Soba
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - Tianchu Shu
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA
| | - Juan Liu
- Industrial Crops Research Institute, Henan Academy of Agricultural Sciences, Henan, China
| | - Iker Aranjuelo
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - G Brett Runion
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Stephen A Prior
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Shawn C Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain.
| | - Alvaro Sanz-Saez
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA.
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17
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Koc A, Odilbekov F, Alamrani M, Henriksson T, Chawade A. Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning. Plant Methods 2022; 18:30. [PMID: 35292072 PMCID: PMC8922805 DOI: 10.1186/s13007-022-00868-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 03/06/2022] [Indexed: 05/13/2023]
Abstract
BACKGROUND High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial. RESULTS The results show that 40-42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of rs = 0.50-0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI). CONCLUSIONS The proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust.
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Affiliation(s)
- Alexander Koc
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, SE-234 22, Lomma, Sweden
| | - Firuz Odilbekov
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, SE-234 22, Lomma, Sweden
- Lantmännen Lantbruk, SE-268 81, Svalöv, Sweden
| | - Marwan Alamrani
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, SE-234 22, Lomma, Sweden
| | | | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, SE-234 22, Lomma, Sweden.
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18
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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19
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Newcomb M, Shakoor N. Nighttime Chlorophyll Fluorescence Imaging of Dark-Adapted Plants Using a Robotic Field Phenotyping Platform. Methods Mol Biol 2022; 2539:213-220. [PMID: 35895206 DOI: 10.1007/978-1-0716-2537-8_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Photosynthetic efficiency is increasingly recognized as an integration of plant responses to dynamic environments, establishing the need for data sets from both field trials and controlled environments. A robotic field scanner phenotyping platform at the University of Arizona is equipped with a high-throughput chlorophyll fluorescence imaging system capable of collecting data on field trials for genetic studies of a photosynthetic trait (Fv/Fm). A description of the fluorescence imaging system is provided in addition to methods for measurements across experimental field plots and a test to determine the impact of variable plant heights. The overall focus is on aspects of field applications of a chlorophyll fluorescence imaging system that differ from analogous systems in controlled environments.
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Affiliation(s)
- Maria Newcomb
- University of Arizona, School of Plant Sciences, Maricopa Agricultural Center, Maricopa, AZ, USA.
| | - Nadia Shakoor
- Donald Danforth Plant Science Center, St. Louis, MO, USA
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20
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Acosta-Gamboa LM, Campbell ZC, Gao F, Babst B, Lorence A. A Novel High-Throughput Phenotyping Hydroponic System for Nitrogen Deficiency Studies in Arabidopsis thaliana. Methods Mol Biol 2022; 2539:19-24. [PMID: 35895192 DOI: 10.1007/978-1-0716-2537-8_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
High-throughput phenotyping enables the temporal detection of subtle changes in plant plasticity and adaptation to different conditions, such as nitrogen deficiency, in an accurate, nondestructive, and unbiased way. Here, we describe a protocol to assess the contribution of nitrogen addition or deprival using an image-based system to analyze plant phenotype. Thousands of images can be captured throughout the life cycle of Arabidopsis, and those images can be used to quantify parameters such as plant growth (area, caliper length, diameter, etc.), in planta chlorophyll fluorescence, and in planta relative water content.
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Affiliation(s)
| | - Zachary C Campbell
- Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, USA
| | - Fei Gao
- Arkansas Forest Resources Center, Division of Agriculture, University of Arkansas System, Monticello, AR, USA
| | - Benjamin Babst
- Arkansas Forest Resources Center, Division of Agriculture, University of Arkansas System, Monticello, AR, USA.
| | - Argelia Lorence
- Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, USA.
- Department of Chemistry and Physics, Arkansas State University, Jonesboro, AR, USA.
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21
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Pandey P, Young S. Design Considerations for In-Field Measurement of Plant Architecture Traits Using Ground-Based Platforms. Methods Mol Biol 2022; 2539:171-190. [PMID: 35895204 DOI: 10.1007/978-1-0716-2537-8_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This work provides a high-level overview of system design considerations for measuring plant architecture traits in row crops using ground-based, mobile platforms. High-throughput phenotyping technologies are commonly deployed in isolated growth chambers or greenhouses; however, there is a need for field-based systems to measure large quantities of plants exposed to natural climates throughout a growing season. High-throughput methods using ground-based mobile systems collect valuable phenotypic information at higher temporal resolutions compared to manual methods (e.g., handheld calipers and measuring sticks). Additionally, the close proximity to plants when using ground-based systems compared to aerial platforms enables plant phenotyping at the organ level. While there is no single best platform for obtaining ground-based plant measurements across crop varieties with different planting configurations, there are a wide range of off-the-shelf systems and sensors that can be integrated to accommodate varying row widths, plant spacing, plant heights, and plot sizes, in addition to emerging commercially available platforms. This chapter will provide an overview of sensor types suitable for phenotyping plant size and shape, as well as provide guidance for deployment with ground-based systems, including push carts or buggies, modified tractors, and robotic platforms.
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Affiliation(s)
- Piyush Pandey
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
| | - Sierra Young
- Department of Civil and Environmental Engineering, Utah State University, Logan, UT, USA.
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22
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Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. Mol Breed 2022; 42:1. [PMID: 37309486 PMCID: PMC10248672 DOI: 10.1007/s11032-021-01272-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.
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Affiliation(s)
- Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| | - Anand Kumar
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 202002 India
| | - Sumandeep Bazzer
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
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23
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Medina-Jimenez K, Arteaga-Vazquez MA, Lorence A. An Automated High-Throughput Phenotyping System for Marchantia polymorpha. Methods Mol Biol 2022; 2539:11-17. [PMID: 35895191 DOI: 10.1007/978-1-0716-2537-8_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
High-throughput phenotyping (HTP) allows automation of fast and precise acquisition and analysis of digital images for the detection of key traits in real time. HTP improves characterization of the growth and development of plants in controlled environments in a nondestructive fashion. Marchantia polymorpha has emerged as a very attractive model for studying the evolution of the physiological, cellular, molecular, and developmental adaptations that enabled plants to conquer their terrestrial environments. The availability of the M. polymorpha genome in combination with a full set of functional genomic tools including genetic transformation, homologous recombination, and genome editing has allowed the inspection of its genome through forward and reverse genetics approaches. The increasing number of mutants has made it possible to perform informative genome-wide analyses to study the phenotypic consequences of gene inactivation. Here we present an HTP protocol for M. polymorpha that will aid current efforts to quantify numerous morphological parameters that can potentially reveal genotype-to-phenotype relationships and relevant connections between individual traits.
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Affiliation(s)
| | - Mario A Arteaga-Vazquez
- Instituto de Biotecnología y Ecología Aplicada (INBIOTECA), Universidad Veracruzana, Xalapa, Mexico.
| | - Argelia Lorence
- Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, USA.
- Department of Chemistry and Physics, Arkansas State University, Jonesboro, AR, USA.
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Abstract
The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
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Affiliation(s)
- Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL, USA
| | - Malachy T Campbell
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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25
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Runcie DE, Qu J, Cheng H, Crawford L. MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits. Genome Biol 2021; 22:213. [PMID: 34301310 PMCID: PMC8299638 DOI: 10.1186/s13059-021-02416-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 06/23/2021] [Indexed: 12/21/2022] Open
Abstract
Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.
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Affiliation(s)
- Daniel E. Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA USA
| | - Jiayi Qu
- Department of Plant Sciences, University of California Davis, Davis, CA USA
| | - Hao Cheng
- Department of Plant Sciences, University of California Davis, Davis, CA USA
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Camarillo-Castillo F, Huggins TD, Mondal S, Reynolds MP, Tilley M, Hays DB. High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat. Plant Methods 2021; 17:58. [PMID: 34098962 PMCID: PMC8185930 DOI: 10.1186/s13007-021-00759-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 05/26/2021] [Indexed: 05/02/2023]
Abstract
BACKGROUND Epicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density. RESULTS Cuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm-2) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm-2 utilizing ground and aerial canopy reflectance. CONCLUSIONS Overall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.
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Affiliation(s)
- Fátima Camarillo-Castillo
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico, D.F, 06600, Mexico.
| | - Trevis D Huggins
- USDA ARS, Dale Bumper National Rice Research Center, Stuttgart, AR, 72160, USA
| | - Suchismita Mondal
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico, D.F, 06600, Mexico
| | - Matthew P Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico, D.F, 06600, Mexico
| | - Michael Tilley
- Agricultural Research Service, Center for Grain and Animal Health Research, USDA, 1515 College Ave., Manhattan, KS, 66502, USA
| | - Dirk B Hays
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, 77840, USA
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Robles-Zazueta CA, Molero G, Pinto F, Foulkes MJ, Reynolds MP, Murchie EH. Field-based remote sensing models predict radiation use efficiency in wheat. J Exp Bot 2021; 72:3756-3773. [PMID: 33713415 PMCID: PMC8096595 DOI: 10.1093/jxb/erab115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 03/10/2021] [Indexed: 05/08/2023]
Abstract
Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems.
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Affiliation(s)
- Carlos A Robles-Zazueta
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD,UK
- International Maize and Wheat Improvement Center (CIMMYT), carretera Mexico-Veracruz km 45, El Batan, Texcoco, Mexico CP
| | - Gemma Molero
- International Maize and Wheat Improvement Center (CIMMYT), carretera Mexico-Veracruz km 45, El Batan, Texcoco, Mexico CP
- KWS Momont Recherche, 7 rue de Martinval, 59246 Mons-en-Pevele,France
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), carretera Mexico-Veracruz km 45, El Batan, Texcoco, Mexico CP
| | - M John Foulkes
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD,UK
| | - Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), carretera Mexico-Veracruz km 45, El Batan, Texcoco, Mexico CP
| | - Erik H Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD,UK
- Correspondence:
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Galán RJ, Bernal-Vasquez AM, Jebsen C, Piepho HP, Thorwarth P, Steffan P, Gordillo A, Miedaner T. Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material. Theor Appl Genet 2021; 134:1409-1422. [PMID: 33630103 PMCID: PMC8081675 DOI: 10.1007/s00122-021-03779-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/19/2021] [Indexed: 05/15/2023]
Abstract
Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. The demand for sustainable sources of biomass is increasing worldwide. The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye (Secale cereale L.) as a dual-purpose crop. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ([Formula: see text]) and genetic relatedness were compared. Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm-993 nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. HBLUP showed higher prediction abilities (0.41 - 0.61) than GBLUP (0.14 - 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and [Formula: see text]. However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.
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Affiliation(s)
- Rodrigo José Galán
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | | | | | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany
| | - Patrick Thorwarth
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
- KWS SAAT SE, Grimsehlstraße 31, 37574, Einbeck, Germany
| | - Philipp Steffan
- KWS LOCHOW GMBH, Ferdinand-von-Lochow Straße 5, 29303, Bergen, Germany
| | - Andres Gordillo
- KWS LOCHOW GMBH, Ferdinand-von-Lochow Straße 5, 29303, Bergen, Germany
| | - Thomas Miedaner
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany.
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Abstract
Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.
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Affiliation(s)
- Sumit Jangra
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Vrantika Chaudhary
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Ram C. Yadav
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Neelam R. Yadav
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
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30
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Chiodi C, Concheri G, Squartini A, Ravi S, Broccanello C, Moro M, Stevanato P. Quantification of rhizomania virus by automated RNA isolation and PCR based methods in sugar beet. Virusdisease 2021;:1-6. [PMID: 33758773 DOI: 10.1007/s13337-021-00674-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 02/20/2021] [Indexed: 11/05/2022] Open
Abstract
Rhizomania is a grave disease affecting sugar beet (Beta vulgaris L.). It is caused by the Beet Necrotic Yellow Vein Virus (BNYVV), an RNA virus transmitted by the plasmodiophorid vector Polymyxa betae. Genetic resistance to the virus has been accomplished mostly using phenotype-genotype association studies. As yet, the most convenient method to ascertain plant resistance has been the quantification of viral titer in roots through the ELISA test. This method is particularly time-consuming and clashes with the necessities of modern plant breeding. Here, we propose an alternative and successful phenotyping method based on the automatic extraction of the viral RNA from sugar beet roots and its relative and absolute quantification by quantitative real-time PCR (qRT-PCR) and digital PCR (dPCR), respectively. Such a method enables an improved standardization of the study, as well as an accurate quantification of the virus also in those samples presenting low virus titer, with respect to the ELISA test.
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31
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Ferté T, Cossin S, Schaeverbeke T, Barnetche T, Jouhet V, Hejblum BP. Automatic phenotyping of electronical health record: PheVis algorithm. J Biomed Inform 2021; 117:103746. [PMID: 33746080 DOI: 10.1016/j.jbi.2021.103746] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 03/02/2021] [Accepted: 03/05/2021] [Indexed: 11/18/2022]
Abstract
Electronic Health Records (EHRs) often lack reliable annotation of patient medical conditions. Phenorm, an automated unsupervised algorithm to identify patient medical conditions from EHR data, has been developed. PheVis extends PheNorm at the visit resolution. PheVis combines diagnosis codes together with medical concepts extracted from medical notes, incorporating past history in a machine learning approach to provide an interpretable parametric predictor of the occurrence probability for a given medical condition at each visit. PheVis is applied to two real-world use-cases using the datawarehouse of the University Hospital of Bordeaux: i) rheumatoid arthritis, a chronic condition; ii) tuberculosis, an acute condition. Cross-validated AUROC were respectively 0.943 [0.940; 0.945] and 0.987 [0.983; 0.990]. Cross-validated AUPRC were respectively 0.754 [0.744; 0.763] and 0.299 [0.198; 0.403]. PheVis performs well for chronic conditions, though absence of exclusion of past medical history by natural language processing tools limits its performance in French for acute conditions. It achieves significantly better performance than state-of-the-art unsupervised methods especially for chronic diseases.
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Affiliation(s)
- Thomas Ferté
- Bordeaux Hospital University Center, Pôle de santé publique, Service d'information médicale, Unité Informatique et Archivistique Médicales, F-33000 Bordeaux, France; Univ. Bordeaux ISPED, Inserm Bordeaux Population Health Research Center UMR 1219, Inria BSO, team SISTM, F-33000 Bordeaux, France.
| | - Sébastien Cossin
- Bordeaux Hospital University Center, Pôle de santé publique, Service d'information médicale, Unité Informatique et Archivistique Médicales, F-33000 Bordeaux, France; Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, team ERIAS, UMR 1219, F-33000 Bordeaux, France
| | - Thierry Schaeverbeke
- Rheumatology department, FHU ACRONIM, Bordeaux University Hospital, F-33076 Bordeaux, France
| | - Thomas Barnetche
- Rheumatology department, FHU ACRONIM, Bordeaux University Hospital, F-33076 Bordeaux, France
| | - Vianney Jouhet
- Bordeaux Hospital University Center, Pôle de santé publique, Service d'information médicale, Unité Informatique et Archivistique Médicales, F-33000 Bordeaux, France; Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, team ERIAS, UMR 1219, F-33000 Bordeaux, France
| | - Boris P Hejblum
- Univ. Bordeaux ISPED, Inserm Bordeaux Population Health Research Center UMR 1219, Inria BSO, team SISTM, F-33000 Bordeaux, France
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Abstract
Conspicuous intracellular gradients manifest and/or drive intracellular polarity in pollen tubes. However, quantifying these gradients raises multiple technical challenges. Here we present a sensible computational protocol to analyze gradients in growing pollen tubes and to filter nonrepresentative time points. As an example, we use imaging data from pollen tubes expressing a genetically encoded ratiometric Ca2+ probe, Yellow CaMeleon 3.6, from which a kymograph is extracted. The tip of the pollen tube is detected with CHUKNORRIS, our previously published methodology, allowing the reconstruction of the intracellular gradient through time. Statistically confounding time points, such as growth arrest where gradients are highly oscillatory, are filtered out and a mean spatial profile is estimated with a local polynomial regression method. Finally, we estimate the gradient slope by the linear portion of the decay in mean fluorescence, offering a quantitative method to detect phenotypes of gradient steepness, location, intensity, and variability. The data manipulation protocol proposed can be achieved in a simple and efficient manner using the statistical programming language R, opening paths to perform high-throughput spatiotemporal phenotyping of intracellular gradients in apically growing cells.
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Affiliation(s)
- Daniel S C Damineli
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA.
| | - Maria Teresa Portes
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - José A Feijó
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
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Abstract
Rice is the most salt-sensitive cereal, suffering yield losses above 50% with soil salinity of 6 dS/m. Thus, understanding the mechanisms of rice salinity tolerance is key to address food security. In this chapter, we provide guidelines to assess rice salinity tolerance using a high-throughput phenotyping platform (HTP) with digital imaging at seedling/early tillering stage and suggest improved analysis methods using stress indices. The protocols described here also include computer scripts for users to improve their experimental design, run genome-wide association studies (GWAS), perform multi-testing corrections, and obtain the Manhattan plots, enabling the identification of loci associated with salinity tolerance. Notably, the computer scripts provided here can be used for any stress or GWAS experiment and independently of HTP.
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Affiliation(s)
- Nadia Al-Tamimi
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Helena Oakey
- School of Agriculture Food and Wine, University of Adelaide, Urrbrae, SA, Australia
| | - Mark Tester
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Sónia Negrão
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland.
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Singh NK, Dutta A, Puccetti G, Croll D. Tackling microbial threats in agriculture with integrative imaging and computational approaches. Comput Struct Biotechnol J 2020; 19:372-383. [PMID: 33489007 PMCID: PMC7787954 DOI: 10.1016/j.csbj.2020.12.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/08/2020] [Accepted: 12/13/2020] [Indexed: 11/29/2022] Open
Abstract
Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.
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Affiliation(s)
- Nikhil Kumar Singh
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, CH-2000 Neuchâtel, Switzerland
| | - Anik Dutta
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, CH-2000 Neuchâtel, Switzerland
- Plant Pathology, Institute of Integrative Biology, ETH Zurich, CH-8092 Zurich, Switzerland
| | - Guido Puccetti
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, CH-2000 Neuchâtel, Switzerland
- Syngenta Crop Protection AG, CH-4332 Stein, Switzerland
| | - Daniel Croll
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, CH-2000 Neuchâtel, Switzerland
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35
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Shabannejad M, Bihamta MR, Majidi-Hervan E, Alipour H, Ebrahimi A. A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat. Plant Methods 2020; 16:146. [PMID: 33292394 PMCID: PMC7607823 DOI: 10.1186/s13007-020-00686-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/15/2020] [Indexed: 05/05/2023]
Abstract
BACKGROUND High-throughput phenotyping and genomic selection accelerate genetic gain in breeding programs by advances in phenotyping and genotyping methods. This study developed a simple, cost-effective high-throughput image analysis pipeline to quantify digital images taken in a panel of 286 Iran bread wheat accessions under terminal drought stress and well-watered conditions. The color proportion of green to yellow (tolerance ratio) and the color proportion of yellow to green (stress ratio) was assessed for each canopy using the pipeline. The estimated tolerance and stress ratios were used as covariates in the genomic prediction models to evaluate the effect of change in canopy color on the improvement of the genomic prediction accuracy of different agronomic traits in wheat. RESULTS The reliability of the high-throughput image analysis pipeline was proved by three to four times of improvement in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covariates in the univariate genomic selection models. The higher prediction accuracies were attained for days to maturity when both tolerance and stress ratios were used as fixed effects in the univariate models. The results of this study indicated that the Bayesian ridge regression and ridge regression-best linear unbiased prediction methods were superior to other genomic prediction methods which were used in this study under terminal drought stress and well-watered conditions, respectively. CONCLUSIONS This study provided a robust, quick, and cost-effective machine learning-enabled image-phenotyping pipeline to improve the genomic prediction accuracy for days to maturity in wheat. The results encouraged the integration of phenomics and genomics in breeding programs.
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Affiliation(s)
- Morteza Shabannejad
- Department of Plant Breeding and Biotechnology, Faculty of Agricultural Sciences and Food Industries, Science and Research Branch, Islamic Azad University, P.O. Box 14515/775, Tehran, Iran
| | - Mohammad-Reza Bihamta
- Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, College of Agriculture and Natural Resources, University of Tehran, P.O. Box 4111, Karaj, Alborz, Iran.
| | - Eslam Majidi-Hervan
- Department of Plant Breeding and Biotechnology, Faculty of Agricultural Sciences and Food Industries, Science and Research Branch, Islamic Azad University, P.O. Box 14515/775, Tehran, Iran
| | - Hadi Alipour
- Department of Plant Production and Genetics, Faculty of Agriculture and Natural Resources, Urmia University, P.O. Box 165, Urmia, Iran
| | - Asa Ebrahimi
- Department of Plant Breeding and Biotechnology, Faculty of Agricultural Sciences and Food Industries, Science and Research Branch, Islamic Azad University, P.O. Box 14515/775, Tehran, Iran
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Wang X, Silva P, Bello NM, Singh D, Evers B, Mondal S, Espinosa FP, Singh RP, Poland J. Improved Accuracy of High-Throughput Phenotyping From Unmanned Aerial Systems by Extracting Traits Directly From Orthorectified Images. Front Plant Sci 2020; 11:587093. [PMID: 33193537 PMCID: PMC7609415 DOI: 10.3389/fpls.2020.587093] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 09/30/2020] [Indexed: 06/08/2023]
Abstract
The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based high-throughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo, probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broad-sense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.
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Affiliation(s)
- Xu Wang
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Paula Silva
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
- Interdepartmental Genetics, Kansas State University, Manhattan, KS, United States
- Instituto Nacional de Investigación Agropecuaria (INIA), Programa de Cultivos de Secano, Estación Experimental La Estanzuela, Colonia del Sacramento, Uruguay
| | - Nora M. Bello
- Department of Statistics, Kansas State University, Manhattan, KS, United States
| | - Daljit Singh
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
- Interdepartmental Genetics, Kansas State University, Manhattan, KS, United States
| | - Byron Evers
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Suchismita Mondal
- Global Wheat Program, International Maize and Wheat Improvement Center, Mexico City, Mexico
| | - Francisco P. Espinosa
- Global Wheat Program, International Maize and Wheat Improvement Center, Mexico City, Mexico
| | - Ravi P. Singh
- Global Wheat Program, International Maize and Wheat Improvement Center, Mexico City, Mexico
| | - Jesse Poland
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
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Brien C, Jewell N, Watts-Williams SJ, Garnett T, Berger B. Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data. Plant Methods 2020; 16:36. [PMID: 32180825 PMCID: PMC7065360 DOI: 10.1186/s13007-020-00577-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 02/27/2020] [Indexed: 05/14/2023]
Abstract
BACKGROUND Non-destructive high-throughput plant phenotyping is becoming increasingly used and various methods for growth analysis have been proposed. Traditional longitudinal or repeated measures analyses that model growth using statistical models are common. However, often the variation in the data is inappropriately modelled, in part because the required models are complicated and difficult to fit. We provide a novel, computationally efficient technique that is based on smoothing and extraction of traits (SET), which we compare with the alternative traditional longitudinal analysis methods. RESULTS The SET-based and longitudinal analyses were applied to a tomato experiment to investigate the effects on plant growth of zinc (Zn) addition and growing plants in soil inoculated with arbuscular mycorrhizal fungi (AMF). Conclusions from the SET-based and longitudinal analyses are similar, although the former analysis results in more significant differences. They showed that added Zn had little effect on plants grown in inoculated soils, but that growth depended on the amount of added Zn for plants grown in uninoculated soils. The longitudinal analysis of the unsmoothed data fitted a mixed model that involved both fixed and random regression modelling with splines, as well as allowing for unequal variances and autocorrelation between time points. CONCLUSIONS A SET-based analysis can be used in any situation in which a traditional longitudinal analysis might be applied, especially when there are many observed time points. Two reasons for deploying the SET-based method are (i) biologically relevant growth parameters are required that parsimoniously describe growth, usually focussing on a small number of intervals, and/or (ii) a computationally efficient method is required for which a valid analysis is easier to achieve, while still capturing the essential features of the exhibited growth dynamics. Also discussed are the statistical models that need to be considered for traditional longitudinal analyses and it is demonstrated that the oft-omitted unequal variances and autocorrelation may be required for a valid longitudinal analysis. With respect to the separate issue of the subjective choice of mathematical growth functions or splines to characterize growth, it is recommended that, for both SET-based and longitudinal analyses, an evidence-based procedure is adopted.
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Affiliation(s)
- Chris Brien
- The Plant Accelerator, Australian Plant Phenomics Facility, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Information Technology and Information Sciences, University of South Australia, GPO Box 2471, Adelaide, SA 5001 Australia
| | - Nathaniel Jewell
- The Plant Accelerator, Australian Plant Phenomics Facility, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
| | | | - Trevor Garnett
- The Plant Accelerator, Australian Plant Phenomics Facility, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
| | - Bettina Berger
- The Plant Accelerator, Australian Plant Phenomics Facility, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
- School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064 Australia
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Frank U, Kublik S, Mayer D, Engel M, Schloter M, Durner J, Gaupels F. A T-DNA mutant screen that combines high-throughput phenotyping with the efficient identification of mutated genes by targeted genome sequencing. BMC Plant Biol 2019; 19:539. [PMID: 31801481 PMCID: PMC6894221 DOI: 10.1186/s12870-019-2162-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 11/26/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Nitrogen dioxide (NO2) triggers hypersensitive response (HR)-like cell death in Arabidopsis thaliana. A high-throughput mutant screen was established to identify genes involved in this type of programmed cell death. RESULTS Altogether 14,282 lines of SALK T-DNA insertion mutants were screened. Growing 1000 pooled mutant lines per tray and simultaneous NO2 fumigation of 4 trays in parallel facilitated high-throughput screening. Candidate mutants were selected based on visible symptoms. Sensitive mutants showed lesions already after fumigation for 1 h with 10 ppm (ppm) NO2 whereas tolerant mutants were hardly damaged even after treatment with 30 ppm NO2. Identification of T-DNA insertion sites by adapter ligation-mediated PCR turned out to be successful but rather time consuming. Therefore, next generation sequencing after T-DNA-specific target enrichment was tested as an alternative screening method. The targeted genome sequencing was highly efficient due to (1.) combination of the pooled DNA from 124 candidate mutants in only two libraries, (2.) successful target enrichment using T-DNA border-specific 70mer probes, and (3.) stringent filtering of the sequencing reads. Seventy mutated genes were identified by at least 3 sequencing reads. Ten corresponding mutants were re-screened of which 8 mutants exhibited NO2-sensitivity or -tolerance confirming that the screen yielded reliable results. Identified candidate genes had published functions in HR, pathogen resistance, and stomata regulation. CONCLUSIONS The presented NO2 dead-or-alive screen combined with next-generation sequencing after T-DNA-specific target enrichment was highly efficient. Two researchers finished the screen within 3 months. Moreover, the target enrichment approach was cost-saving because of the limited number of DNA libraries and sequencing runs required. The experimental design can be easily adapted to other screening approaches e.g. involving high-throughput treatments with abiotic stressors or phytohormones.
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Affiliation(s)
- Ulrike Frank
- Institute of Biochemical Plant Pathology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Susanne Kublik
- Research Unit for Comparative Microbiome Analysis, Helmholtz Zentrum München, Neuherberg, Germany
| | - Dörte Mayer
- Institute of Biochemical Plant Pathology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Marion Engel
- Scientific Computing Research Unit, Helmholtz Zentrum München, Neuherberg, Germany
| | - Michael Schloter
- Research Unit for Comparative Microbiome Analysis, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jörg Durner
- Institute of Biochemical Plant Pathology, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biochemical Plant Pathology, Technische Universität München, Freising, Germany
| | - Frank Gaupels
- Institute of Biochemical Plant Pathology, Helmholtz Zentrum München, Neuherberg, Germany.
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Moreira FF, Hearst AA, Cherkauer KA, Rainey KM. Improving the efficiency of soybean breeding with high-throughput canopy phenotyping. Plant Methods 2019; 15:139. [PMID: 31827576 PMCID: PMC6862841 DOI: 10.1186/s13007-019-0519-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 11/07/2019] [Indexed: 05/06/2023]
Abstract
BACKGROUND In the early stages of plant breeding programs high-quality phenotypes are still a constraint to improve genetic gain. New field-based high-throughput phenotyping (HTP) platforms have the capacity to rapidly assess thousands of plots in a field with high spatial and temporal resolution, with the potential to measure secondary traits correlated to yield throughout the growing season. These secondary traits may be key to select more time and most efficiently soybean lines with high yield potential. Soybean average canopy coverage (ACC), measured by unmanned aerial systems (UAS), is highly heritable, with a high genetic correlation with yield. The objective of this study was to compare the direct selection for yield with indirect selection using ACC and using ACC as a covariate in the yield prediction model (Yield|ACC) in early stages of soybean breeding. In 2015 and 2016 we grew progeny rows (PR) and collected yield and days to maturity (R8) in a typical way and canopy coverage using a UAS carrying an RGB camera. The best soybean lines were then selected with three parameters, Yield, ACC and Yield|ACC, and advanced to preliminary yield trials (PYT). RESULTS We found that for the PYT in 2016, after adjusting yield for R8, there was no significant difference among the mean performances of the lines selected based on ACC and Yield. In the PYT in 2017 we found that the highest yield mean was from the lines directly selected for yield, but it may be due to environmental constraints in the canopy growth. Our results indicated that PR selection using Yield|ACC selected the most top-ranking lines in advanced yield trials. CONCLUSIONS Our findings emphasize the value of aerial HTP platforms for early stages of plant breeding. Though ACC selection did not result in the best performance lines in the second year of selections, our results indicate that ACC has a role in the effective selection of high-yielding soybean lines.
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Affiliation(s)
- Fabiana Freitas Moreira
- Department of Agronomy, Purdue University, 915 West State Street, West Lafayette, IN 47907 USA
| | - Anthony Ahau Hearst
- Department of Agricultural and Biological Engineering, Purdue University, 225 South University Street, West Lafayette, IN 47907 USA
| | - Keith Aric Cherkauer
- Department of Agricultural and Biological Engineering, Purdue University, 225 South University Street, West Lafayette, IN 47907 USA
| | - Katy Martin Rainey
- Department of Agronomy, Purdue University, 915 West State Street, West Lafayette, IN 47907 USA
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Wang L, Poque S, Valkonen JPT. Phenotyping viral infection in sweetpotato using a high-throughput chlorophyll fluorescence and thermal imaging platform. Plant Methods 2019; 15:116. [PMID: 31649744 PMCID: PMC6805361 DOI: 10.1186/s13007-019-0501-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 10/10/2019] [Indexed: 05/12/2023]
Abstract
BACKGROUND Virus diseases caused by co-infection with Sweet potato feathery mottle virus (SPFMV) and Sweetpotato chlorotic stunt virus (SPCSV) are a severe problem in the production of sweetpotato (Ipomoea batatas L.). Traditional molecular virus detection methods include nucleic acid-based and serological tests. In this study, we aimed to validate the use of a non-destructive imaging-based plant phenotype platform to study plant-virus synergism in sweetpotato by comparing four virus treatments with two healthy controls. RESULTS By monitoring physiological and morphological effects of viral infection in sweetpotato over 29 days, we quantified photosynthetic performance from chlorophyll fluorescence (ChlF) imaging and leaf thermography from thermal infrared (TIR) imaging among sweetpotatoes. Moreover, the differences among different treatments observed from ChlF and TIR imaging were related to virus accumulation and distribution in sweetpotato. These findings were further validated at the molecular level by related gene expression in both photosynthesis and carbon fixation pathways. CONCLUSION Our study validated for the first time the use of ChlF- and TIR-based imaging systems to distinguish the severity of virus diseases related to SPFMV and SPCSV in sweetpotato. In addition, we demonstrated that the operating efficiency of PSII and photochemical quenching were the most sensitive parameters for the quantification of virus effects compared with maximum quantum efficiency, non-photochemical quenching, and leaf temperature.
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Affiliation(s)
- Linping Wang
- Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland
| | - Sylvain Poque
- Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland
| | - Jari P. T. Valkonen
- Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland
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Han L, Yang G, Dai H, Yang H, Xu B, Li H, Long H, Li Z, Yang X, Zhao C. Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform. Plant Methods 2019; 15:57. [PMID: 31149023 PMCID: PMC6537385 DOI: 10.1186/s13007-019-0444-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 05/22/2019] [Indexed: 05/31/2023]
Abstract
BACKGROUND With environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding for new varieties with high yield and strong resistance. However, the traditional phenotypic screening methods have some disadvantages, such as destructive, inefficient, low-dimensional, labor-intensive and cumbersome, which seriously hinder the development of field breeding. Breeders urgently need a high-throughput technique for acquiring and evaluating phenotypic data that can efficiently screen out excellent phenotypic traits from large-scale genotype populations. RESULTS In the present study, we used an unmanned aerial vehicle (UAV) high-throughput phenotyping (HTP) platform to collect RGB and multispectral images for a breeding program and acquired multiple phenotypic components (or traits), such as plant height, normalized difference vegetation index, biomass accumulation, plant-height growth rate, lodging, and leaf color. By implementing self-organizing maps and principal components analysis biplots to establish phenotypic map and similarity, we proposed an UAV-assisted HTP framework for preselecting maize (Zee mays L.) phenotypic components (or traits). CONCLUSIONS This framework gives breeders additional information to allow them to quickly identify and preselect plants that have genotypes conferring desirable phenotypic components out of thousands of field plots. The present study also demonstrates that remote sensing is a powerful tool with which to acquire abundant phenotypic components. By using these rich phenotypic components, breeders should be able to more effectively identify and select superior genotypes.
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Affiliation(s)
- Liang Han
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 2College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong, 037003 China
- 4College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, 100083 China
| | - Guijun Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Huayang Dai
- 4College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, 100083 China
| | - Hao Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Bo Xu
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Heli Li
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Huiling Long
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Zhenhai Li
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Xiaodong Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Chunjiang Zhao
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
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Dreccer MF, Molero G, Rivera-Amado C, John-Bejai C, Wilson Z. Yielding to the image: How phenotyping reproductive growth can assist crop improvement and production. Plant Sci 2019; 282:73-82. [PMID: 31003613 DOI: 10.1016/j.plantsci.2018.06.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 05/11/2018] [Accepted: 06/05/2018] [Indexed: 05/21/2023]
Abstract
Reproductive organs are the main reason we grow and harvest most plant species as crops, yet they receive less attention from phenotyping due to their complexity and inaccessibility for analysis. This review highlights recent progress towards the quantitative high-throughput phenotyping of reproductive development, focusing on three impactful areas that are pivotal for plant breeding and crop production. First, we look at phenotyping phenology, summarizing the indirect and direct approaches that are available. This is essential for analysis of genotype by environment, and to enable effective management interpretation and agronomy and physiological interventions. Second, we look at pollen development and production, in addition to anther characteristics, these are critical points of vulnerability for yield loss when stress occurs before and during flowering, and are of particular interest for hybrid technology development. Third, we elaborate on phenotyping yield components, indirectly or directly during the season, with a numerical or growth related approach and post-harvest processing. Finally, we summarise the opportunities and challenges ahead for phenotyping reproductive growth and their feasibility and impact, with emphasis on plant breeding applications and targeted yield increases.
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Affiliation(s)
- M Fernanda Dreccer
- CSIRO Agriculture and Food, 203 Tor Street, Toowoomba, QLD, 4350, Australia.
| | - Gemma Molero
- International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco CP 56130, Mexico
| | - Carolina Rivera-Amado
- International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco CP 56130, Mexico
| | - Carus John-Bejai
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire LE12 5RD, United Kingdom
| | - Zoe Wilson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire LE12 5RD, United Kingdom
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Pshenichnikova TA, Doroshkov AV, Osipova SV, Permyakov AV, Permyakova MD, Efimov VM, Afonnikov DA. Quantitative characteristics of pubescence in wheat (Triticum aestivum L.) are associated with photosynthetic parameters under conditions of normal and limited water supply. Planta 2019; 249:839-847. [PMID: 30446814 DOI: 10.1007/s00425-018-3049-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 11/14/2018] [Indexed: 05/25/2023]
Abstract
Density and length of leaf pubescence are important factors of diversity in the response to water deficiency among wheat genotypes. Many studies evidence an important protective value of leaf hairiness in plants, especially under the conditions of drought, thermal loads and increased solar radiation. However, the physiological and adaptive roles of such traits in cereals, including cultivated plants, have not been sufficiently studied to date. The aim of this work was to study the association of morphological characteristics of leaves with parameters of gas exchange and chlorophyll fluorescence in wheat lines carrying a genetically different leaf hairiness. Isogenic and inter-varietal substitution wheat lines were used, carrying various combinations of dominant and recessive alleles of the known genes. A quantitative assessment of the pubescence was carried out in contrasting watering conditions to establish the physiological role of this trait in adaptation to drought. With the help of a portable system for studying the gas exchange and chlorophyll fluorescence, ten parameters of photosynthesis were studied, as well as morphological features of leaves and shoot biomass. It was found that gas exchange parameters are inversely proportional to the density and length of trichomes. In drought conditions, the trichome density increased and the length of trichomes decreased under the observed decrease in the level of gas exchange. A similar dependence was observed for the level of non-photochemical quenching of chlorophyll fluorescence. Under optimal conditions, the poorly haired cultivars exhibited a higher biomass than the densely haired. However, under water deficiency they significantly reduced the biomass and showed a low value of the tolerance index.
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Affiliation(s)
- Tatyana A Pshenichnikova
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentieva 10, Novosibirsk, 630090, Russia.
| | - Alexey V Doroshkov
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentieva 10, Novosibirsk, 630090, Russia
| | - Svetlana V Osipova
- Siberian Institute of Plant Physiology and Biochemistry SB RAS, P.O. Box 317, Irkutsk, 664033, Russia
- Irkutsk State University, ul. Sukhe-Bator 5, Irkutsk, 664003, Russia
| | - Alexey V Permyakov
- Siberian Institute of Plant Physiology and Biochemistry SB RAS, P.O. Box 317, Irkutsk, 664033, Russia
| | - Marina D Permyakova
- Siberian Institute of Plant Physiology and Biochemistry SB RAS, P.O. Box 317, Irkutsk, 664033, Russia
| | - Vadim M Efimov
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentieva 10, Novosibirsk, 630090, Russia
| | - Dmitry A Afonnikov
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentieva 10, Novosibirsk, 630090, Russia
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Miller ND, Stelpflug SC, Kaeppler SM, Spalding EP. A machine vision platform for measuring imbibition of maize kernels: quantification of genetic effects and correlations with germination. Plant Methods 2018; 14:115. [PMID: 30598691 PMCID: PMC6302439 DOI: 10.1186/s13007-018-0383-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Accepted: 12/14/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND Imbibition (uptake of water by a dry seed) initiates the germination process. An automated method for quantifying imbibition would enable research on the genetic elements that influence the underlying hydraulic and biochemical processes. In the case of crop research, a high throughput imbibition assay could be used to investigate seed quality topics or to improve yield by selecting varieties with superior germination characteristics. RESULTS An electronic force transducer measured imbibition of single maize kernels with very high resolution but low throughput. An image analysis method was devised to achieve high throughput and sufficient resolution. A transparent fixture held 90 maize kernels in contact with water on the imaging window of a flatbed document scanner that produced an image of the kernels automatically every 10 min for 22 h. Custom image analysis software measured the area A of each indexed kernel in each image to produce imbibition time courses. The ultimate change in area (ΔA) ranged from 19.3 to 23.4% in a population of 72 hybrids derived from 9 inbred parents. Kernel area as a function of time was fit well by A t = A f 1 - e - k t where A f is the final kernel area. The swelling coefficient, k, ranged from 0.098 to 0.159 h-1 across the genotypes. The full diallel structure of the population enabled maternal genotype effects to be assessed. In a separate experiment, measurements of kernels of the same 25 inbreds produced in three different years demonstrated that production and storage variables affected imbibition much less than genotype. In a third experiment, measurements of 30 diverse inbred lines showed that k varied inversely with germination time (r = - 0.7) and directly with germination percentage (r = 0.7). CONCLUSIONS Nonspecialized imaging hardware and custom analysis software running on public cyber infrastructure form a low-cost platform for measuring seed imbibition with high resolution and throughput. We measured imbibition of thousands of kernels to determine that genotype influenced imbibition of maize kernels much more than seed production and storage environments. In some hybrids, k depended on which inbred parent was maternal. Quantitative relationships between k and germination traits were discovered.
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Affiliation(s)
- Nathan D. Miller
- Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI 53706 USA
| | - Scott C. Stelpflug
- Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI 53706 USA
| | - Shawn M. Kaeppler
- Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI 53706 USA
| | - Edgar P. Spalding
- Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI 53706 USA
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Roth L, Hund A, Aasen H. PhenoFly Planning Tool: flight planning for high-resolution optical remote sensing with unmanned areal systems. Plant Methods 2018; 14:116. [PMID: 30598692 PMCID: PMC6302310 DOI: 10.1186/s13007-018-0376-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 11/30/2018] [Indexed: 05/19/2023]
Abstract
BACKGROUND Driven by a huge improvement in automation, unmanned areal systems (UAS) are increasingly used for field observations and high-throughput phenotyping. Today, the bottleneck does not lie in the ability to fly a drone anymore, but rather in the appropriate flight planning to capture images with sufficient quality. Proper flight preparation for photography with digital frame cameras should include relevant concepts such as view, sharpness and exposure calculations. Additionally, if mapping areas with UASs, one has to consider concepts related to ground control points (GCPs), viewing geometry and way-point flights. Unfortunately, non of the available flight planning tools covers all these aspects. RESULTS We give an overview of concepts related to flight preparation, present the newly developed open source software PhenoFly Planning Tool, and evaluate other recent flight planning tools. We find that current flight planning and mapping tools strongly focus on vendor-specific solutions and mostly ignore basic photographic properties-our comparison shows, for example, that only two out of thirteen evaluated tools consider motion blur restrictions, and none of them depth of field limits. In contrast, PhenoFly Planning Tool enhances recent sophisticated UAS and autopilot systems with an optical remote sensing workflow that respects photographic concepts. The tool can assist in selecting the right equipment for your needs, experimenting with different flight settings to test the performance of the resulting imagery, preparing the field and GCP setup, and generating a flight path that can be exported as waypoints to be uploaded to an UAS. CONCLUSION By considering the introduced concepts, uncertainty in UAS-based remote sensing and high-throughput phenotyping may be considerably reduced. The presented software PhenoFly Planning Tool (https://shiny.usys.ethz.ch/PhenoFlyPlanningTool) helps users to comprehend and apply these concepts.
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Affiliation(s)
- Lukas Roth
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Andreas Hund
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Helge Aasen
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
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Miart F, Fontaine JX, Pineau C, Demailly H, Thomasset B, Van Wuytswinkel O, Pageau K, Mesnard F. MuSeeQ, a novel supervised image analysis tool for the simultaneous phenotyping of the soluble mucilage and seed morphometric parameters. Plant Methods 2018; 14:112. [PMID: 30568724 PMCID: PMC6297999 DOI: 10.1186/s13007-018-0377-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 11/30/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND The mucilage is a model to study the polysaccharide biosynthesis since it is produced in large amounts and composed of complex polymers. In addition, it is of great economic interest for its technical and nutritional value. A fast method for phenotyping the released mucilage and the seed morphometric parameters will be useful for fundamental, food, pharmaceutical and breeding researches. Current strategies to phenotype soluble mucilage are restricted to visual evaluations or are highly time-consuming. RESULTS Here, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters. Within this context, we combined a biochemical assay and an open-source computer-aided image analysis tool, MuSeeQ. The biochemical assay consists in sowing seeds on an agarose medium containing the dye toluidine blue O, which specifically stains the mucilage once it is released on the gel. The second part of MuSeeQ is a macro developed in ImageJ allowing to quickly extract and analyse 11 morphometric data of seeds and their respective released mucilages. As an example, MuSeeQ was applied on a flax recombinant inbred lines population (previously screened for fatty acids content.) and revealed significant correlations between the soluble mucilage shape and the concentration of some fatty acids, e.g. C16:0 and C18:2. Other fatty acids were also found to correlate with the seed shape parameters, e.g. C18:0 and C18:2. MuSeeQ was then showed to be used for the analysis of other myxospermous species, including Arabidopsis thaliana and Camelina sativa. CONCLUSIONS MuSeeQ is a low-cost and user-friendly method which may be used by breeders and researchers for phenotyping simultaneously seeds of specific cultivars, natural variants or mutants and their respective soluble mucilage area released on a gel. The script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr.
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Affiliation(s)
- Fabien Miart
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
- Present Address: Institut Jean-Pierre Bourgin, UMR1318, INRA/AgroParisTech, Saclay Plant Sciences, INRA Centre de Versailles, 78026 Versailles Cedex, France
| | - Jean-Xavier Fontaine
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
| | - Christophe Pineau
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
| | - Hervé Demailly
- Centre de ressources régionales en biologie moléculaire, Bâtiment Serrres-Transfert, rue Dallery, 80039 Amiens Cedex 1, France
| | - Brigitte Thomasset
- Sorbonne Universités, Génie Enzymatique et Cellulaire, UMR CNRS 7025, Université de Technologie de Compiègne, CS 60319, 60203 Compiègne Cedex, France
| | - Olivier Van Wuytswinkel
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
| | - Karine Pageau
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
| | - François Mesnard
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
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Gomez FE, Carvalho G, Shi F, Muliana AH, Rooney WL. High throughput phenotyping of morpho-anatomical stem properties using X-ray computed tomography in sorghum. Plant Methods 2018; 14:59. [PMID: 30008795 PMCID: PMC6043981 DOI: 10.1186/s13007-018-0326-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 07/04/2018] [Indexed: 05/24/2023]
Abstract
BACKGROUND In bioenergy/forage sorghum, morpho-anatomical stem properties are major components affecting standability and juice yield. However, phenotyping these traits is low-throughput, and has been restricted by the lack of a high-throughput phenotyping platforms that can collect both morphological and anatomical stem properties. X-ray computed tomography (CT) offers a potential solution, but studies using this technology in plants have evaluated limited numbers of genotypes with limited throughput. Here we suggest that using a medical CT might overcome sample size limitations when higher resolution is not needed. Thus, the aim of this study was to develop a practical high-throughput phenotyping and image data processing pipeline that extracts stem morpho-anatomical traits faster, more efficiently and on a larger number of samples. RESULTS A medical CT was used to image morpho-anatomical stem properties in sorghum. The platform and image analysis pipeline revealed extensive phenotypic variation for important morpho-anatomical traits in well-characterized sorghum genotypes at suitable repeatability rates. CT estimates were highly predictive of morphological traits and moderately predictive of anatomical traits. The image analysis pipeline also identified genotypes with superior morpho-anatomical traits that were consistent with ground-truth based classification in previous studies. In addition, stem cross section intensity measured by the CT was highly correlated with stem dry-weight density, and can potentially serve as a high-throughput approach to measure stem density in grass stems. CONCLUSIONS The use of CT on a diverse set of sorghum genotypes with a defined platform and image analysis pipeline was effective at predicting traits such as stem length, diameter, and pithiness ratio at the internode level. High-throughput phenotyping of stem traits using CT appears to be useful and feasible for use in an applied breeding program.
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Affiliation(s)
- Francisco E. Gomez
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695 USA
- Department of Soil and Crop Sciences, Texas A&M University, 370 Olsen Blvd, College Station, TX 77843 USA
| | - Geraldo Carvalho
- Department of Soil and Crop Sciences, Texas A&M University, 370 Olsen Blvd, College Station, TX 77843 USA
| | - Fuhao Shi
- Department of Computer Science and Engineering, Texas A&M University, 3112 TAMU, 710 Ross St, College Station, TX 77843 USA
| | - Anastasia H. Muliana
- Department of Mechanical Engineering, Texas A& M University, 401 Joe Routt Blvd, College Station, TX 77843 USA
| | - William L. Rooney
- Department of Soil and Crop Sciences, Texas A&M University, 370 Olsen Blvd, College Station, TX 77843 USA
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Diaz-Garcia L, Schlautman B, Covarrubias-Pazaran G, Maule A, Johnson-Cicalese J, Grygleski E, Vorsa N, Zalapa J. Massive phenotyping of multiple cranberry populations reveals novel QTLs for fruit anthocyanin content and other important chemical traits. Mol Genet Genomics 2018; 293:1379-1392. [PMID: 29967963 DOI: 10.1007/s00438-018-1464-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 06/19/2018] [Indexed: 01/08/2023]
Abstract
Because of its known phytochemical activity and benefits for human health, American cranberry (Vaccinium macrocarpon L.) production and commercialization around the world has gained importance in recent years. Flavonoid compounds as well as the balance of sugars and acids are key quality characteristics of fresh and processed cranberry products. In this study, we identified novel QTL that influence total anthocyanin content (TAcy), titratable acidity (TA), proanthocyanidin content (PAC), Brix, and mean fruit weight (MFW) in cranberry fruits. Using repeated measurements over the fruit ripening period, different QTLs were identified at specific time points that coincide with known chemical changes during fruit development and maturation. Some genetic regions appear to be regulating more than one trait. In addition, we demonstrate the utility of digital imaging as a reliable, inexpensive and high-throughput strategy for the quantification of anthocyanin content in cranberry fruits. Using this imaging approach, we identified a set of QTLs across three different breeding populations which collocated with anthocyanin QTL identified using wet-lab approaches. We demonstrate the use of a high-throughput, reliable and highly accessible imaging strategy for predicting anthocyanin content based on cranberry fruit color, which could have a large impact for both industry and cranberry research.
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Affiliation(s)
- Luis Diaz-Garcia
- Department of Horticulture, University of Wisconsin, Madison, WI, USA. .,Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Aguascalientes, Mexico.
| | | | | | - Andrew Maule
- Department of Horticulture, University of Wisconsin, Madison, WI, USA
| | | | | | - Nicholi Vorsa
- Blueberry and Cranberry Research and Extension Center, Rutgers University, Chatsworth, NJ, USA
| | - Juan Zalapa
- Department of Horticulture, University of Wisconsin, Madison, WI, USA. .,USDA-ARS, Vegetable Crops Research Unit, University of Wisconsin, Madison, WI, USA.
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Gomez FE, Carvalho G, Shi F, Muliana AH, Rooney WL. High throughput phenotyping of morpho-anatomical stem properties using X-ray computed tomography in sorghum. Plant Methods 2018. [PMID: 30008795 DOI: 10.1186/s13007-018-0326-323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
BACKGROUND In bioenergy/forage sorghum, morpho-anatomical stem properties are major components affecting standability and juice yield. However, phenotyping these traits is low-throughput, and has been restricted by the lack of a high-throughput phenotyping platforms that can collect both morphological and anatomical stem properties. X-ray computed tomography (CT) offers a potential solution, but studies using this technology in plants have evaluated limited numbers of genotypes with limited throughput. Here we suggest that using a medical CT might overcome sample size limitations when higher resolution is not needed. Thus, the aim of this study was to develop a practical high-throughput phenotyping and image data processing pipeline that extracts stem morpho-anatomical traits faster, more efficiently and on a larger number of samples. RESULTS A medical CT was used to image morpho-anatomical stem properties in sorghum. The platform and image analysis pipeline revealed extensive phenotypic variation for important morpho-anatomical traits in well-characterized sorghum genotypes at suitable repeatability rates. CT estimates were highly predictive of morphological traits and moderately predictive of anatomical traits. The image analysis pipeline also identified genotypes with superior morpho-anatomical traits that were consistent with ground-truth based classification in previous studies. In addition, stem cross section intensity measured by the CT was highly correlated with stem dry-weight density, and can potentially serve as a high-throughput approach to measure stem density in grass stems. CONCLUSIONS The use of CT on a diverse set of sorghum genotypes with a defined platform and image analysis pipeline was effective at predicting traits such as stem length, diameter, and pithiness ratio at the internode level. High-throughput phenotyping of stem traits using CT appears to be useful and feasible for use in an applied breeding program.
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Affiliation(s)
- Francisco E Gomez
- 1Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695 USA
- 2Department of Soil and Crop Sciences, Texas A&M University, 370 Olsen Blvd, College Station, TX 77843 USA
| | - Geraldo Carvalho
- 2Department of Soil and Crop Sciences, Texas A&M University, 370 Olsen Blvd, College Station, TX 77843 USA
| | - Fuhao Shi
- 3Department of Computer Science and Engineering, Texas A&M University, 3112 TAMU, 710 Ross St, College Station, TX 77843 USA
| | - Anastasia H Muliana
- 4Department of Mechanical Engineering, Texas A& M University, 401 Joe Routt Blvd, College Station, TX 77843 USA
| | - William L Rooney
- 2Department of Soil and Crop Sciences, Texas A&M University, 370 Olsen Blvd, College Station, TX 77843 USA
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Abstract
BACKGROUND Accurate and quantitative phenotypic data in plant breeding programmes is vital in breeding to assess the performance of genotypes and to make selections. Traditional strawberry phenotyping relies on the human eye to assess most external fruit quality attributes, which is time-consuming and subjective. 3D imaging is a promising high-throughput technique that allows multiple external fruit quality attributes to be measured simultaneously. RESULTS A low cost multi-view stereo (MVS) imaging system was developed, which captured data from 360° around a target strawberry fruit. A 3D point cloud of the sample was derived and analysed with custom-developed software to estimate berry height, length, width, volume, calyx size, colour and achene number. Analysis of these traits in 100 fruits showed good concordance with manual assessment methods. CONCLUSION This study demonstrates the feasibility of an MVS based 3D imaging system for the rapid and quantitative phenotyping of seven agronomically important external strawberry traits. With further improvement, this method could be applied in strawberry breeding programmes as a cost effective phenotyping technique.
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Affiliation(s)
- Joe Q. He
- NIAB EMR, New Road, East Malling, ME19 6BJ UK
- University of Reading, Whiteknights, Reading, RG6 6AH UK
| | - Richard J. Harrison
- NIAB EMR, New Road, East Malling, ME19 6BJ UK
- University of Reading, Whiteknights, Reading, RG6 6AH UK
| | - Bo Li
- NIAB EMR, New Road, East Malling, ME19 6BJ UK
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