High-Throughput Phenotyping in Soybean

Singh, Asheesh K.; Sarkar, Soumik; Ganapathysubramanian, Baskar; Schapaugh, William T.; Miguez, Fernando E.; Carley, Clayton N.; Carroll, Matthew E.; Chiozza, Mariana; Chiteri, Kevin O.; Falk, Kevin G.; Jones, Sarah; Jubery, Talukder Z.; Mirnezami, Seyed Vahid; Nagasubramanian, Koushik; Parmley, Kyle A.; Rairdin, Ashlyn; Shook, Johnathon M.; Laan, Liza Van der; Young, Therin J.; Zhang, Jiaoping

doi:10.1007/978-3-030-73734-4_7

Cited by 21 publications

(13 citation statements)

References 164 publications

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“…Continued development of high throughput phenotyping capabilities of root traits and machine learning with explainability (Gangopadhyay et al., 2019; A. K. Singh et al., 2021) will enable the acquisition of formerly unknown relationships, as shown in this work. Additional work is needed to parse out the genetic controls between nodule count and individual nodule size to unlock the selection potential for breeders to maximize N and C use efficiency in soybeans across diverse genotype × environment interactions and climates (Krause et al., 2022; Shook et al., 2021a).…”

Section: Discussionmentioning

confidence: 96%

Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean

et al. 2022

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The symbiotic relationship between soybean [Glycine max L. (Merr.)] roots and bacteria (Bradyrhizobium japonicum) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N 2 ) is fixed into bio-available ammonia (NH 3 ) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non-taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non-taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant-Bradyrhizobium relationship and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.

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Section: Discussionmentioning

confidence: 96%

Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean

et al. 2022

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“…Plant phenotypic fingerprints serve as a novel opportunity to offer a diverse advantage to the future of high throughput phenotyping serving as a useful tool for data curation, cultivar selection, evaluation, and additional experimentation. Integration of canopy fingerprints with machine learning models can further advance the field of phenomics and cyber-agricultural systems ( Singh et al, 2016 ; Singh et al, 2018 ; Singh A. K. et al., 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…Canopy traits have traditionally focused on 2-dimensional (2D) features, which is useful in certain instances, for example, canopy coverage ( Purcell, 2000 ), which has frequently been collected with drone high throughput phenotyping ( Guo et al., 2021 ). With the advent of high-throughput crop and plant phenotyping ( Araus and Cairns, 2014 ; Yang et al., 2020 ; Guo et al., 2021 ; Jubery et al., 2021 ; Singh A. K. et al, 2021 ; Singh D. P. et al., 2021 ), plant scientists have been able to conduct large scale and time-series investigations on canopy coverage. Additionally, researchers have shown automated or semi-automated extraction of canopy traits; for example, canopy features, including height, shape, color, and texture, can be used for plant stress and disease assessment, estimating total biomass, leaf chlorophyll, and leaf nitrogen ( Shiraiwa and Sinclair, 1993 ; Hunt et al., 2005 ; Pydipati et al, 2006 ; Jubery et al., 2016 ; Bai et al., 2018 ; Parmley et al., 2019 ; Parmley et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

et al. 2023

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Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis. We extend feature extraction from 3D point cloud data to various additional features, which we denote as ‘canopy fingerprints’. This is motivated by the successful application of the fingerprint concept for molecular fingerprints in chemistry applications and acoustic fingerprints in sound engineering applications. We developed an end-to-end pipeline to generate canopy fingerprints of a three-dimensional point cloud of soybean [Glycine max (L.) Merr.] canopies grown in hill plots captured by a terrestrial laser scanner (TLS). The pipeline includes noise removal, registration, and plot extraction, followed by the canopy fingerprint generation. The canopy fingerprints are generated by splitting the data into multiple sub-canopy scale components and extracting sub-canopy scale geometric features. The generated canopy fingerprints are interpretable and can assist in identifying patterns in a database of canopies, querying similar canopies, or identifying canopies with a certain shape. The framework can be extended to other modalities (for instance, hyperspectral point clouds) and tuned to find the most informative fingerprint representation for downstream tasks. These canopy fingerprints can aid in the utilization of canopy traits at previously unutilized scales, and therefore have applications in plant breeding and resilient crop production.

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“…Recently, several researchers reported HTP for yield‐ and canopy‐related traits in soybean using UAS (Singh, Singh, Sarkar, et al., 2021). These traits include biomass (Toda et al., 2021), canopy coverage and light interception (LI) (Maimaitijiang et al., 2020), canopy cover and canopy height (Borra‐Serrano et al., 2020), plant density (Habibi et al., 2021), plant height (Luo et al., 2021), and leaf wilting classification (Zhou, Zhou, et al., 2020).…”

Section: Soybeanmentioning

confidence: 99%

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.

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High-Throughput Phenotyping in Soybean

Cited by 21 publications

References 164 publications

Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean

Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

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