High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging

ABSTRACTMaize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds – primarily recently off patent lines – were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.

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High-Throughput Phenotyping of Indirect Traits for Early-Stage Selection in Sugarcane Breeding

Remote Sensing ◽

10.3390/rs11242952 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2952 ◽

Cited By ~ 7

Author(s):

Sijesh Natarajan ◽

Jayampathi Basnayake ◽

Xianming Wei ◽

Prakash Lakshmanan

Keyword(s):

High Throughput ◽

Early Stage ◽

Selection Index ◽

Canopy Cover ◽

Indirect Selection ◽

Canopy Height ◽

Pure Stand ◽

Single Row ◽

Sugarcane Breeding ◽

High Throughput Phenotyping

One of the major limitations for sugarcane genetic improvement is the low heritability of yield in the early stages of breeding, mainly due to confounding inter-plot competition effects. In this study, we investigate an indirect selection index (Si), developed based on traits correlated to yield (indirect traits) that were measured using an unmanned aerial vehicle (UAV), to improve clonal assessment in early stages of sugarcane breeding. A single-row early-stage clonal assessment trial, involving 2134 progenies derived from 245 crosses, and a multi-row experiment representative of pure-stand conditions, with an unrelated population of 40 genotypes, were used in this study. Both experiments were screened at several stages using visual, multispectral, and thermal sensors mounted on a UAV for indirect traits, including canopy cover, canopy height, canopy temperature, and normalised difference vegetation index (NDVI). To construct the indirect selection index, phenotypic and genotypic variance-covariances were estimated in the single-row and multi-row experiment, respectively. Clonal selection from the indirect selection index was compared to single-row yield-based selection. Ground observations of stalk number and plant height at six months after planting made from a subset of 75 clones within the single-row experiment were highly correlated to canopy cover (rg = 0.72) and canopy height (rg = 0.69), respectively. The indirect traits had high heritability and strong genetic correlation with cane yield in both the single-row and multi-row experiments. Only 45% of the clones were common between the indirect selection index and single-row yield based selection, and the expected efficiency of correlated response to selection for pure-stand yield based on indirect traits (44%–73%) was higher than that based on single-row yield (45%). These results highlight the potential of high-throughput phenotyping of indirect traits combined in an indirect selection index for improving early-stage clonal selections in sugarcane breeding.

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Towards high-throughput phenotyping of complex patterned behaviors: Lessons from mouse self-grooming

PsycEXTRA Dataset ◽

10.1037/e516242012-034 ◽

2011 ◽

Author(s):

E. Kyzar ◽

S. Gaikwad ◽

M. Pham ◽

J. Green ◽

A. Roth ◽

...

Keyword(s):

High Throughput ◽

High Throughput Phenotyping

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High-throughput phenotyping: breaking through the bottleneck in future crop breeding

The Crop Journal ◽

10.1016/j.cj.2021.03.015 ◽

2021 ◽

Author(s):

Peng Song ◽

Jinglu Wang ◽

Xinyu Guo ◽

Wanneng Yang ◽

Chunjiang Zhao

Keyword(s):

High Throughput ◽

Crop Breeding ◽

High Throughput Phenotyping

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Automating Uniformity Trials to Optimize Precision of Agronomic Field Trials

Agronomy ◽

10.3390/agronomy11061254 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1254

Author(s):

Marcus Jones ◽

Marin Harbur ◽

Ken J. Moore

Keyword(s):

Web Application ◽

Unit Area ◽

Field Trials ◽

State University ◽

Iowa State University ◽

Plot Size ◽

Maize Field ◽

Process Uniformity ◽

Field Plots ◽

Uniformity Trials

Plot size has an important impact on variation among plots in agronomic field trials, but is rarely considered during the design process. Uniformity trials can inform a researcher about underlying variance, but are seldom used due to their laborious nature. The objective of this research was to describe variation in maize field trials among field plots of varying size and develop a tool to optimize field-trial design using uniformity-trial statistics. Six uniformity trials were conducted in 2015–2016 in conjunction with Iowa State University and WinField United. All six uniformity trials exhibited a negative asymptotic relationship between variance and plot size. Variance per unit area was reduced over 50% with plots 41.8 m2 in size and over 75% when using a plot size >111.5 m2 compared to a 13.9 m2 plot. Plot shape within a fixed plot size did not influence variance. The data illustrated fewer replicates were needed as plot size increased, since larger plots reduced variability. Use of a Shiny web application is demonstrated that allows a researcher to upload a yield map and consider uniformity-trial statistics to inform plot size and replicate decisions.

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Modelling temporal genetic and spatio‐temporal residual effects for high‐throughput phenotyping data

Australian & New Zealand Journal of Statistics ◽

10.1111/anzs.12336 ◽

2021 ◽

Author(s):

A. P. Verbyla ◽

J. De Faveri ◽

D. M. Deery ◽

G. J. Rebetzke

Keyword(s):

High Throughput ◽

Residual Effects ◽

High Throughput Phenotyping ◽

Spatio Temporal

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High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

International Journal of Molecular Sciences ◽

10.3390/ijms22158266 ◽

2021 ◽

Vol 22 (15) ◽

pp. 8266

Author(s):

Minsu Kim ◽

Chaewon Lee ◽

Subin Hong ◽

Song Lim Kim ◽

Jeong-Ho Baek ◽

...

Keyword(s):

Drought Stress ◽

High Throughput ◽

Large Scale ◽

Crop Yields ◽

Plant Traits ◽

Rapid Development ◽

Plant Responses ◽

Phenotypic Data ◽

Agricultural Technologies ◽

High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

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High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

Journal of Forestry Research ◽

10.1007/s11676-021-01360-6 ◽

2021 ◽

Author(s):

Marcus Vinicius Vieira Borges ◽

Janielle de Oliveira Garcia ◽

Tays Silva Batista ◽

Alexsandra Nogueira Martins Silva ◽

Fabio Henrique Rojo Baio ◽

...

Keyword(s):

Neural Networks ◽

High Throughput ◽

Mean Squared Error ◽

Vegetation Indices ◽

Plant Size ◽

Eucalyptus Species ◽

Forest Inventories ◽

Spectral Bands ◽

High Throughput Phenotyping ◽

Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

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