scholarly journals Aerial high‐throughput phenotyping enables indirect selection for grain yield at the early generation, seed‐limited stages in breeding programs

Crop Science ◽  
2020 ◽  
Vol 60 (6) ◽  
pp. 3096-3114 ◽  
Author(s):  
Margaret R. Krause ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Ravi P. Singh ◽  
Francisco Pinto ◽  
...  
Keyword(s):  
Grain Yield ◽  
High Throughput ◽  
Indirect Selection ◽  
Early Generation ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Selection For

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.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage

2019 ◽  
Vol 132 (6) ◽  
pp. 1705-1720 ◽  
Author(s):  
Jin Sun ◽  
Jesse A. Poland ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Philomin Juliana ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
High Throughput ◽  
Early Stage ◽  
Wheat Grain ◽  
High Throughput Phenotyping ◽  
Selection For

scholarly journals Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

2016 ◽  
Vol 6 (9) ◽  
pp. 2799-2808 ◽  
Author(s):  
Jessica Rutkoski ◽  
Jesse Poland ◽  
Suchismita Mondal ◽  
Enrique Autrique ◽  
Lorena González Pérez ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
High Throughput ◽  
Vegetation Indices ◽  
Canopy Temperature ◽  
Improve Accuracy ◽  
High Throughput Phenotyping ◽  
Selection For

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 Early generation selection strategy for yield and yield components in white oat

2005 ◽  
Vol 62 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Giovani Benin ◽  
Fernando Irajá Félix de Carvalho ◽  
Antônio Costa de Oliveira ◽  
Claudir Lorencetti ◽  
Igor Pires Valério ◽  
...  
Keyword(s):  
Grain Yield ◽  
Indirect Selection ◽  
Grain Weight ◽  
Yield Potential ◽  
High Yield ◽  
Direct Selection ◽  
Selection Strategy ◽  
Breeding Method ◽  
Growing Seasons ◽  
Selection For

Several studies have searched for higher efficiency on plant selection in generations bearing high frequency of heterozygotes. This work aims to compare the response of direct selection for grain yield, indirect selection through average grain weight and combined selection for higher yield potential and average grain weight of oat plants (Avena sativa L.), using the honeycomb breeding method. These strategies were applied in the growing seasons of 2001 and 2002 in F3 and F4 populations, respectively, in the crosses UPF 18 CTC 5, OR 2 <FONT FACE=Symbol>´</FONT> UPF 7 and OR 2 <FONT FACE=Symbol>´</FONT> UPF 18. The ten best genetic combinations obtained for each cross and selection strategy were evaluated in greenhouse yield trials. Selection of plants with higher yield and average grain weight might be performed on early generations with high levels of heterozygosis. The direct selection for grain yield and indirect selection for average grain weight enabled to increase the average of characters under selection. However, genotypes obtained through direct selection presented lower average grain weight and those obtained through the indirect selection presented lower yield potential. Selection strategies must be run simultaneously to combine in only one genotype high yield potential and large grain weight, enabling maximum genetic gain for both characters.

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 Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

2020 ◽  
Vol 12 (3) ◽  
pp. 574 ◽  
Author(s):  
Yuncai Hu ◽  
Samuel Knapp ◽  
Urs Schmidhalter
Keyword(s):  
Grain Yield ◽  
Plant Breeding ◽  
High Throughput ◽  
Complex Traits ◽  
New Technologies ◽  
Early Selection ◽  
Spectral Indices ◽  
Wheat Genotypes ◽  
High Throughput Phenotyping ◽  
Spectral Sensing

Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques

2014 ◽  
Vol 41 (3) ◽  
pp. 227 ◽  
Author(s):  
Sebastian Kipp ◽  
Bodo Mistele ◽  
Urs Schmidhalter
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
High Throughput ◽  
Protein Concentration ◽  
Partial Least Square ◽  
Large Field ◽  
Grain Protein ◽  
Stay Green ◽  
Grain Protein Concentration ◽  
High Throughput Phenotyping

Yield and grain protein concentration (GPC) represent crucial factors in the global agricultural wheat (Triticum aestivum L.) production and are predominantly determined via carbon and nitrogen metabolism, respectively. The maintenance of green leaf area and the onset of senescence (Osen) are expected to be involved in both C and N accumulation and their translocation into grains. The aim of this study was to identify stay-green and early senescence phenotypes in a field experiment of 50 certified winter wheat cultivars and to investigate the relationships among Osen, yield and GPC. Colour measurements on flag leaves were conducted to determine Osen for 20 cultivars and partial least square regression models were used to calculate Osen for the remaining 30 cultivars based on passive spectral reflectance measurements as a high-throughput phenotyping technique for all varieties. Using this method, stay-green and early senescence phenotypes could be clearly differentiated. A significant negative relationship between Osen and grain yield (r2 = 0.81) was observed. By contrast, GPC showed a significant positive relationship to Osen (r2 = 0.48). In conclusion, the high-throughput character of our proposed phenotyping method should help improve the detection of such traits in large field trials as well as help us reach a better understanding of the consequences of the timing of senescence on yield.

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