Phenotyping: New Windows into the Plant for Breeders

Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

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Using Plant Phenomics to Exploit the Gains of Genomics

Agronomy ◽

10.3390/agronomy9030126 ◽

2019 ◽

Vol 9 (3) ◽

pp. 126 ◽

Cited By ~ 10

Author(s):

Aditya Pratap ◽

Sanjeev Gupta ◽

Ramakrishnan Nair ◽

S. Gupta ◽

Roland Schafleitner ◽

...

Keyword(s):

Complex Traits ◽

Low Cost ◽

Crop Improvement ◽

Minimal Invasive ◽

Plant Phenotyping ◽

Great Promise ◽

Genetic Dissection ◽

Plant Structure ◽

Resource Limited ◽

And Function

Agricultural scientists face the dual challenge of breeding input-responsive, widely adoptable and climate-resilient varieties of crop plants and developing such varieties at a faster pace. Integrating the gains of genomics with modern-day phenomics will lead to increased breeding efficiency which in turn offers great promise to develop such varieties rapidly. Plant phenotyping techniques have impressively evolved during the last two decades. The low-cost, automated and semi-automated methods for data acquisition, storage and analysis are now available which allow precise quantitative analysis of plant structure and function; and genetic dissection of complex traits. Appropriate plant types can now be quickly developed that respond favorably to low input and resource-limited environments and address the challenges of subsistence agriculture. The present review focuses on the need of systematic, rapid, minimal invasive and low-cost plant phenotyping. It also discusses its evolution to modern day high throughput phenotyping (HTP), traits amenable to HTP, integration of HTP with genomics and the scope of utilizing these tools for crop improvement.

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GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply

Functional Plant Biology ◽

10.1071/fp16128 ◽

2017 ◽

Vol 44 (1) ◽

pp. 76 ◽

Cited By ~ 22

Author(s):

Tania Gioia ◽

Anna Galinski ◽

Henning Lenz ◽

Carmen Müller ◽

Jonas Lentz ◽

...

Keyword(s):

High Throughput ◽

Phenotypic Diversity ◽

Crop Improvement ◽

Root Traits ◽

Root Systems ◽

Non Invasive ◽

Large Sets ◽

High Throughput Phenotyping ◽

Improvement Programs ◽

Root Phenotyping

New techniques and approaches have been developed for root phenotyping recently; however, rapid and repeatable non-invasive root phenotyping remains challenging. Here, we present GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system (4 plants min–1) based on flat germination paper. GrowScreen-PaGe allows the acquisition of time series of the developing root systems of 500 plants, thereby enabling to quantify short-term variations in root system. The choice of germination paper was found to be crucial and paper ☓ root interaction should be considered when comparing data from different studies on germination paper. The system is suitable for phenotyping dicot and monocot plant species. The potential of the system for high-throughput phenotyping was shown by investigating phenotypic diversity of root traits in a collection of 180 rapeseed accessions and of 52 barley genotypes grown under control and nutrient-starved conditions. Most traits showed a large variation linked to both genotype and treatment. In general, root length traits contributed more than shape and branching related traits in separating the genotypes. Overall, results showed that GrowScreen-PaGe will be a powerful resource to investigate root systems and root plasticity of large sets of plants and to explore the molecular and genetic root traits of various species including for crop improvement programs.

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Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

Frontiers in Plant Science ◽

10.3389/fpls.2021.780250 ◽

2022 ◽

Vol 12 ◽

Author(s):

Jana Ebersbach ◽

Nazifa Azam Khan ◽

Ian McQuillan ◽

Erin E. Higgins ◽

Kyla Horner ◽

...

Keyword(s):

Machine Learning ◽

Image Processing ◽

Drought Stress ◽

High Throughput ◽

Complex Traits ◽

Phenotypic Diversity ◽

Crop Improvement ◽

Breeding Population ◽

Supervised Machine Learning ◽

Oilseed Crop

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

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Plot size matters: interference from intergenotypic competition in plant phenotyping studies

Functional Plant Biology ◽

10.1071/fp13177 ◽

2014 ◽

Vol 41 (2) ◽

pp. 107 ◽

Cited By ~ 56

Author(s):

Greg J. Rebetzke ◽

Ralph (Tony) A. Fischer ◽

Anthony F. van Herwaarden ◽

Dave G. Bonnett ◽

Karine Chenu ◽

...

Keyword(s):

Complex Traits ◽

Plant Phenotyping ◽

Controlled Environment ◽

Canopy Development ◽

Plot Size ◽

Field Assessment ◽

Intergenotypic Competition ◽

Growing Conditions ◽

Competition For Resources ◽

Commercial Field

Genetic and physiological studies often comprise genotypes diverse in vigour, size and flowering time. This can make the phenotyping of complex traits challenging, particularly those associated with canopy development, biomass and yield, as the environment of one genotype can be influenced by a neighbouring genotype. Limited seed and space may encourage field assessment in single, spaced rows or in small, unbordered plots, whereas the convenience of a controlled environment or greenhouse makes pot studies tempting. However, the relevance of such growing conditions to commercial field-grown crops is unclear and often doubtful. Competition for water, light and nutrients necessary for canopy growth will be variable where immediate neighbours are genetically different, particularly under stress conditions, where competition for resources and influence on productivity is greatest. Small hills and rod-rows maximise the potential for intergenotypic competition that is not relevant to a crop’s performance in monocultures. Response to resource availability will typically vary among diverse genotypes to alter genotype ranking and reduce heritability for all growth-related traits, with the possible exception of harvest index. Validation of pot experiments to performance in canopies in the field is essential, whereas the planting of multirow plots and the simple exclusion of plot borders at harvest will increase experimental precision and confidence in genotype performance in target environments.

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Association Mapping for Improvement of Quantitative Traits in Plant Breeding Populations

Nepal Journal of Biotechnology ◽

10.3126/njb.v2i1.5686 ◽

1970 ◽

Vol 2 (1) ◽

pp. 72-89

Author(s):

Umesh R Rosyara ◽

Bal K Joshi

Keyword(s):

Association Mapping ◽

Plant Breeding ◽

Fine Mapping ◽

Quantitative Trait ◽

Human Genetics ◽

Crop Improvement ◽

Nucleotide Polymorphisms ◽

Breeding Populations ◽

Validation Of Results ◽

Mapping Populations

DNA-based molecular markers have been extensively utilized for mapping of genes and quantitative trait loci (QTL) of interest based on linkage analysis in mapping populations. This is in contrast to human genetics that use of linkage disequilibrium (LD)-based mapping for fine mapping of QTLs using single nucleotide polymorphisms. LD based association mapping (AM) has promise to be used in plants. Possible use of such approach may be for fine mapping of genes / QTLs, identifying favorable alleles for marker aided selection and cross validation of results from linkage mapping for precise location of genes / QTLs of interest. In the present review, we discuss different mapping populations, approaches, prospects and limitations of using association mapping in plant breeding populations. This is expected to create awareness in plant breeders in use of AM in crop improvement activities.Key words: Association mapping; plant breeding; DNA marker; quantitative trait lociDOI: http://dx.doi.org/10.3126/njb.v2i1.5686 Nepal Journal of Biotechnology Jan.2012, Vol.2(1): 72-89

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A global analysis of CNVs in Chinese indigenous fine-wool sheep populations using whole-genome resequencing

10.21203/rs.2.16764/v4 ◽

2021 ◽

Author(s):

Chao Yuan ◽

Zengkui Lu ◽

Tingting Guo ◽

Yaojing Yue ◽

Xijun Wang ◽

...

Keyword(s):

Genetic Variation ◽

Complex Traits ◽

Phenotypic Diversity ◽

Average Length ◽

Global Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Nutrient Metabolism ◽

Relaxin Family

Abstract Background Copy number variation (CNV) is an important source of genetic variation that has a significant influence on phenotypic diversity, economically important traits and the evolution of livestock species. In this study, the genome-wide CNV distribution characteristics of 32 fine-wool sheep from three breeds were analyzed using resequencing.Results A total of 1,747,604 CNVs were detected in this study, and 7,228 CNV regions (CNVR) were obtained after merging overlapping CNVs; these regions accounted for 2.17% of the sheep reference genome. The average length of the CNVRs was 4,307.17 bp. “Deletion” events took place more frequently than “duplication” or “both” events. The CNVRs obtained overlapped with previously reported sheep CNVRs to variable extents (4.39%–55.46%). Functional enrichment analysis showed that the CNVR-harboring genes were mainly involved in sensory perception systems, nutrient metabolism processes, and growth and development processes. Furthermore, 1,855 of the CNVRs were associated with 166 quantitative trait loci (QTL), including milk QTLs, carcass QTLs, and health-related QTLs, among others. In addition, the 32 fine-wool sheep were divided into horned and polled groups to analyze for the selective sweep of CNVRs, and it was found that the relaxin family peptide receptor 2 (RXFP2) gene was strongly influenced by selection.Conclusions In summary, we constructed a genomic CNV map for Chinese indigenous fine-wool sheep using resequencing, thereby providing a valuable genetic variation resource for sheep genome research, which will contribute to the study of complex traits in sheep.

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Advances in root architectural modeling

Understanding and improving crop root function - Burleigh Dodds Series in Agricultural Science ◽

10.19103/as.2020.0075.02 ◽

2021 ◽

pp. 3-32

Author(s):

Johannes A. Postma ◽

◽

Christopher K. Black ◽

Keyword(s):

Nutrient Uptake ◽

Mycorrhizal Fungi ◽

Plant Nutrition ◽

Root Traits ◽

Plant Phenotyping ◽

Biological Interactions ◽

Root Plasticity ◽

New Developments ◽

Testing Algorithms ◽

Architectural Modeling

Root architectural (RSA) models have become important tools in root research and plant phenotyping for studying root traits, processes, and interactions with the environment. The models have been used to simulate how various root traits and processes influence water and nutrient uptake. At a more technical level, they have been used to develop phenotyping technology, particularly for testing algorithms for segmenting roots. To compute these quantitative estimates regarding plant nutrition and root functioning, much development occurred in the last decade increasing the complexity of the models. This chapter describes first the application of the models to questions in plant biology, breeding, and agronomy, and second the development of the models. It concludes with a small outlook suggesting that models need benchmarking and validation and that new developments are likely to include better descriptions of root plasticity responses and focus on biological interactions among (soil) organisms, including mycorrhizal fungi.

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Simulation of an Autonomous Mobile Robot for LiDAR-Based In-Field Phenotyping and Navigation

Robotics ◽

10.3390/robotics9020046 ◽

2020 ◽

Vol 9 (2) ◽

pp. 46 ◽

Cited By ~ 4

Author(s):

Jawad Iqbal ◽

Rui Xu ◽

Shangpeng Sun ◽

Changying Li

Keyword(s):

Mean Squared Error ◽

Estimation Error ◽

Crop Improvement ◽

Canopy Height ◽

Agricultural Crop ◽

Hybrid Strategy ◽

Agricultural Robots ◽

Research Fields ◽

Promising Tool ◽

Field Phenotyping

The agriculture industry is in need of substantially increasing crop yield to meet growing global demand. Selective breeding programs can accelerate crop improvement but collecting phenotyping data is time- and labor-intensive because of the size of the research fields and the frequency of the work required. Automation could be a promising tool to address this phenotyping bottleneck. This paper presents a Robotic Operating System (ROS)-based mobile field robot that simultaneously navigates through occluded crop rows and performs various phenotyping tasks, such as measuring plant volume and canopy height using a 2D LiDAR in a nodding configuration. The efficacy of the proposed 2D LiDAR configuration for phenotyping is assessed in a high-fidelity simulated agricultural environment in the Gazebo simulator with an ROS-based control framework and compared with standard LiDAR configurations used in agriculture. Using the proposed nodding LiDAR configuration, a strategy for navigation through occluded crop rows is presented. The proposed LiDAR configuration achieved an estimation error of 6.6% and 4% for plot volume and canopy height, respectively, which was comparable to the commonly used LiDAR configurations. The hybrid strategy with GPS waypoint following and LiDAR-based navigation was used to navigate the robot through an agricultural crop field successfully with an root mean squared error of 0.0778 m which was 0.2% of the total traveled distance. The presented robot simulation framework in ROS and optimized LiDAR configuration helped to expedite the development of the agricultural robots, which ultimately will aid in overcoming the phenotyping bottleneck.

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The Dawn of the Age of Multi-Parent MAGIC Populations in Plant Breeding: Novel Powerful Next-Generation Resources for Genetic Analysis and Selection of Recombinant Elite Material

Biology ◽

10.3390/biology9080229 ◽

2020 ◽

Vol 9 (8) ◽

pp. 229 ◽

Cited By ~ 1

Author(s):

Andrea Arrones ◽

Santiago Vilanova ◽

Mariola Plazas ◽

Giulio Mangino ◽

Laura Pascual ◽

...

Keyword(s):

Genetic Analysis ◽

Plant Breeding ◽

Complex Traits ◽

Genetic Recombination ◽

Phenotypic Diversity ◽

Recombinant Inbred Lines ◽

Plant Genetic Resources ◽

Diallel Cross ◽

Genetic Mosaic ◽

Selection Of

The compelling need to increase global agricultural production requires new breeding approaches that facilitate exploiting the diversity available in the plant genetic resources. Multi-parent advanced generation inter-cross (MAGIC) populations are large sets of recombinant inbred lines (RILs) that are a genetic mosaic of multiple founder parents. MAGIC populations display emerging features over experimental bi-parental and germplasm populations in combining significant levels of genetic recombination, a lack of genetic structure, and high genetic and phenotypic diversity. The development of MAGIC populations can be performed using “funnel” or “diallel” cross-designs, which are of great relevance choosing appropriate parents and defining optimal population sizes. Significant advances in specific software development are facilitating the genetic analysis of the complex genetic constitutions of MAGIC populations. Despite the complexity and the resources required in their development, due to their potential and interest for breeding, the number of MAGIC populations available and under development is continuously growing, with 45 MAGIC populations in different crops being reported here. Though cereals are by far the crop group where more MAGIC populations have been developed, MAGIC populations have also started to become available in other crop groups. The results obtained so far demonstrate that MAGIC populations are a very powerful tool for the dissection of complex traits, as well as a resource for the selection of recombinant elite breeding material and cultivars. In addition, some new MAGIC approaches that can make significant contributions to breeding, such as the development of inter-specific MAGIC populations, the development of MAGIC-like populations in crops where pure lines are not available, and the establishment of strategies for the straightforward incorporation of MAGIC materials in breeding pipelines, have barely been explored. The evidence that is already available indicates that MAGIC populations will play a major role in the coming years in allowing for impressive gains in plant breeding for developing new generations of dramatically improved cultivars.

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Diversity in jackfruit (Artocarpus heterophyllus Lam.): insights into fruit characterization for the identification of superior genotypes

Plant Genetic Resources ◽

10.1017/s1479262120000325 ◽

2020 ◽

pp. 1-9

Author(s):

M.K. Dhakar ◽

Bikash Das ◽

P.K. Sarkar ◽

Vishal Nath ◽

A.K. Singh ◽

...

Keyword(s):

Agricultural Research ◽

Phenotypic Diversity ◽

Crop Improvement ◽

Fruit Size ◽

Selection Procedure ◽

Principal Component ◽

Eastern Region ◽

Artocarpus Heterophyllus ◽

Commercial Cultivation ◽

Wide Range

Abstract Jackfruit (Artocarpus heterophyllus Lam.) is a nutritious crop from the Moraceae family. The current study was undertaken to evaluate the phenotypic diversity of fruit characteristics using a set of 27 standardized fruit descriptors to describe 28 jackfruit genotypes. These data were used to identify the superior jackfruit genotype that could be used for commercial cultivation. The data revealed a wide range of differences among the genotypes for all the traits studied. Cluster analysis classified the genotypes into four major groups that confirmed the wide diversity among them. Principal component analysis (PCA) also revealed that 80.22% of the variability among the jackfruit genotypes was explained by the first five principal components (PCs). Based on the overall results, the Indian Council of Agricultural Research, Research Complex for Eastern Region (ICAR-RCER) JS 6/3 and 10/3 genotypes were found to be the most promising for table purposes (medium fruit size, pulp percentage >50 and total soluble solid (TSS) >20°Brix), whereas the ICAR-RCER JS 7/7 genotype with large fruit size, pulp percentage >50 and TSS >20°Brix was found to be suitable for processing. The coefficient of variation was the least for traits such as TSS (12.56%) and average seed length (13.56%). Hence, priority may also be given to the TSS and seed size when exploring promising genotypes and operating a selection procedure for crop improvement in jackfruit. The information generated under the study forms a potential baseline for fruit breeders to use in selecting genotypes with superior fruit qualities for jackfruit crop improvement programmes in the future.

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