High-Throughput and Precision Phenotyping for Cereal Breeding Programs

2013 ◽  
pp. 341-374 ◽  
Author(s):  
Boddupalli M. Prasanna ◽  
Jose L. Araus ◽  
Jose Crossa ◽  
Jill E. Cairns ◽  
Natalia Palacios ◽  
...  
Keyword(s):  
High Throughput ◽  
Breeding Programs ◽  
Cereal Breeding ◽  
Precision Phenotyping

scholarly journals Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Tang ◽  
Atit Parajuli ◽  
Chunpeng James Chen ◽  
Yang Hu ◽  
Samuel Revolinski ◽  
...  
Keyword(s):  
Prediction Model ◽  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Selection Process ◽  
Forage Legume ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Fully Automatic ◽  
Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

scholarly journals Investigation of Genetic Relationships within Miscanthus using SNP Markers Identified using SLAF-Seq

2021 ◽  
Author(s):  
ZHIYONG Chen ◽  
Yancen He ◽  
Yasir Iqbal ◽  
Yanlan Shi ◽  
Hongmei Huang ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Genetic Relationships ◽  
Female Parent ◽  
Snp Markers ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Breeding Programs ◽  
Sequencing Method ◽  
Specific Locus

Abstract Background: Miscanthus, which is a leading dedicated-energy grass in Europe and in parts of Asia, is expected to play a key role in the development of the future bioeconomy. However, due to its complex genetic background, it is difficult to investigate phylogenetic relationships and the evolution of gene function in this genus. Here, we investigated 50 Miscanthus germplasms: 1 female parent (M. lutarioriparius), 30 candidate male parents (M. lutarioriparius, M. sinensis, and M. sacchariflorus), and 19 offspring. We used high-throughput Specific-Locus Amplified Fragment sequencing (SLAF-seq) to identify informative single nucleotide polymorphisms (SNPs) in all germplasms.Results: We identified 800,081 SLAF tags, of which 160,368 were polymorphic. Each tag was 264–364 bp long. The obtained SNPs were used to investigate genetic relationships within Miscanthus. We constructed a phylogenetic tree of the 50 germplasms using the obtained SNPs, and found that the germplasms fell into two clades: one clade of M. sinensis only and one clade that included the offspring, M. lutarioriparius, and M. sacchariflorus. Genetic cluster analysis indicated that M. lutarioriparius germplasm C3 was the most likely male parent of the offspring.Conclusions: As a high-throughput sequencing method, SLAF-seq can be used to identify informative SNPs in Miscanthus germplasms and to rapidly characterize genetic relationships within this genus. Our results will support the development of breeding programs utilizing Miscanthus cultivars with elite biomass- or fiber-production potential.

scholarly journals Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

2020 ◽  
Vol 12 (6) ◽  
pp. 998 ◽  
Author(s):  
GyuJin Jang ◽  
Jaeyoung Kim ◽  
Ju-Kyung Yu ◽  
Hak-Jin Kim ◽  
Yoonha Kim ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
New Technologies ◽  
Cost Effective ◽  
New Wave ◽  
Breeding Programs ◽  
Modern Agriculture ◽  
Aerial Vehicle ◽  
High Throughput Phenotyping

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

scholarly journals Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

2019 ◽  
Vol 11 (3) ◽  
pp. 329 ◽  
Author(s):  
Gustavo Lobos ◽  
Alejandro Escobar-Opazo ◽  
Félix Estrada ◽  
Sebastián Romero-Bravo ◽  
Miguel Garriga ◽  
...  
Keyword(s):  
Electron Transport ◽  
Environmental Changes ◽  
Electron Transport Rate ◽  
Transport Rate ◽  
Physiological Traits ◽  
Plant Phenotyping ◽  
Spectral Signature ◽  
Wavelength Selection ◽  
Breeding Programs ◽  
Cereal Breeding

To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance. During the last few decades, high-throughput plant phenotyping and phenomics have been developed primarily in cereal breeding programs. In this study, plant reflectance, at the level of the leaf, was used to assess several physiological traits in five Vaccinium spp. cultivars growing under four controlled conditions (no-stress, water deficit, heat stress, and combined stress). Two modeling methodologies [Multiple Linear Regression (MLR) and Partial Least Squares (PLS)] with or without (W/O) prior wavelength selection (multicollinearity, genetic algorithms, or in combination) were considered. PLS generated better estimates than MLR, although prior wavelength selection improved MLR predictions. When data from the environments were combined, PLS W/O gave the best assessment for most of the traits, while in individual environments, the results varied according to the trait and methodology considered. The highest validation predictions were obtained for chlorophyll a/b (R2Val ≤ 0.87), maximum electron transport rate (R2Val ≤ 0.60), and the irradiance at which the electron transport rate is saturated (R2Val ≤ 0.59). The results of this study, the first to model modulated chlorophyll fluorescence by reflectance, confirming the potential for implementing this tool in blueberry breeding programs, at least for the estimation of a number of important physiological traits. Additionally, the differential effects of the environment on the spectral signature of each cultivar shows this tool could be directly used to assess their tolerance to specific environments.

High‐Throughput Precision Phenotyping of the Oil Content of Single Seeds of Various Oilseed Crops

Crop Science ◽  
2018 ◽  
Vol 58 (2) ◽  
pp. 670-678 ◽  
Author(s):  
A. E. Melchinger ◽  
J. Böhm ◽  
H. F. Utz ◽  
J. Müller ◽  
S. Munder ◽  
...  
Keyword(s):  
High Throughput ◽  
Oil Content ◽  
Oilseed Crops ◽  
Precision Phenotyping ◽  
Single Seeds

scholarly journals Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

2020 ◽  
Author(s):  
Margaret R. Krause ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Ravi P. Singh ◽  
Francisco Pinto ◽  
...  
Keyword(s):  
Grain Yield ◽  
High Throughput ◽  
Vegetation Indices ◽  
Early Generation ◽  
Breeding Programs ◽  
Breeding Lines ◽  
High Throughput Phenotyping ◽  
Seed Increase ◽  
Yield Trials ◽  
Selection Of

ABSTRACTBreeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

Use of reflectance measurements to estimate early cereal biomass production on sandplain soils

1993 ◽  
Vol 33 (2) ◽  
pp. 179 ◽  
Author(s):  
GA Elliott ◽  
KL Regan
Keyword(s):  
Biomass Production ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Stem Elongation ◽  
Selection Criterion ◽  
Poor Correlation ◽  
Breeding Programs ◽  
Mid Infrared ◽  
Cereal Breeding ◽  
Reflectance Measurements

High early biomass production has been suggested as a useful selection criterion for improving cereal yields in mediterranean environments. Current methods for measuring biomass production in cereal plots involves destructive sampling which is not suitable for routine use by plant breeders where large numbers of samples are to be screened. The measurement of spectral reflectance using ground-based remote sensing techniques has the potential to provide a nondestructive estimate of plant biomass production. This field study investigated the relationship between spectral reflectance and early biomass production in barley and wheat and assessed the feasibility of using spectral reflectance techniques in cereal-breeding programs. Spectral reflectance measurements (0.40-2.5 �m wavelengths) were made before stem elongation for barley and wheat grown in separate experiments at Wongan Hills, Western Australia. Cereal biomass was closely associated with spectral reflectance in the visible (0.44-0.66 �m) and mid infrared (1.50-1.80 �m and 2.00-2.40 �m) regions, but not in the near infrared (0.71-1.10 �m). The poor correlation between reflectance in the near infrared region and biomass production was due to the low amounts of biomass present and the high reflectivity of the sandplain soils at these wavelengths. The close associations in the visible and mid infrared regions provides promise for use in estimation of cereal biomass, but further research is required to refine the technique for use in large scale breeding programs.

scholarly journals Image-Based High-Throughput Phenotyping of Cereals Early Vigor and Weed-Competitiveness Traits

2020 ◽  
Vol 12 (23) ◽  
pp. 3877
Author(s):  
Shlomi Aharon ◽  
Zvi Peleg ◽  
Eli Argaman ◽  
Roi Ben-David ◽  
Ran N. Lati
Keyword(s):  
High Throughput ◽  
Growth Parameters ◽  
Early Growth ◽  
Field Conditions ◽  
Plant Phenotyping ◽  
Weed Competition ◽  
Initial Growth ◽  
Modeling Approaches ◽  
Cereal Breeding ◽  
Early Vigor

Cereals grains are the prime component of the human diet worldwide. To promote food security and sustainability, new approaches to non-chemical weed control are needed. Early vigor cultivars with enhanced weed-competitiveness ability are a potential tool, nonetheless, the introduction of such trait in breeding may be a long and labor-intensive process. Here, two image-driven plant phenotyping methods were evaluated to facilitate effective and accurate selection for early vigor in cereals. For that purpose, two triticale genotypes differentiating in vigor and growth rate early in the season were selected as model plants: X-1010 (high) and Triticale1 (low). Two modeling approaches, 2-D and 3-D, were applied on the plants offering an evaluation of various morphological growth parameters for the triticale canopy development, under controlled and field conditions. The morphological advantage of X-1010 was observed only at the initial growth stages, which was reflected by significantly higher growth parameter values compared to the Triticale1 genotype. Both modeling approaches were sensitive enough to detect phenotypic differences in growth as early as 21 days after sowing. All growth parameters indicated a faster early growth of X-1010. However, the 2-D related parameter [projected shoot area (PSA)] is the most available one that can be extracted via end user-friendly imaging equipment. PSA provided adequate indication for the triticale early growth under weed-competition conditions and for the improved weed-competition ability. The adequate phenotyping ability for early growth and competition was robust under controlled and field conditions. PSA can be extracted from close and remote sensing platforms, thus, facilitate high throughput screening. Overall, the results of this study may improve cereal breeding for early vigor and weed-competitiveness.

scholarly journals High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks

2020 ◽  
Author(s):  
Rodrigo Trevisan ◽  
Osvaldo Pérez ◽  
Nathan Schmitz ◽  
Brian Diers ◽  
Nicolas Martin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Throughput ◽  
Mean Squared Error ◽  
Ground Truth ◽  
Breeding Programs ◽  
Growing Seasons ◽  
Maturity Date ◽  
High Throughput Phenotyping ◽  
Commercial Breeding

Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture used solves limitations of previous research and can be used at scale in commercial breeding programs.

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