scholarly journals High-throughput phenotyping in cotton: a review

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Irish Lorraine B. PABUAYON ◽  
Yazhou SUN ◽  
Wenxuan GUO ◽  
Glen L. RITCHIE
Keyword(s):  
High Throughput ◽  
Cropping Systems ◽  
Three Dimensional ◽  
Growth Yield ◽  
Analytical Techniques ◽  
Phenotypic Traits ◽  
Technological Advances ◽  
Recent Developments ◽  
Potential Applications ◽  
High Throughput Phenotyping

Abstract Recent technological advances in cotton (Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis. High-throughput phenotyping (HTP) is a non-destructive and rapid approach of monitoring and measuring multiple phenotypic traits related to the growth, yield, and adaptation to biotic or abiotic stress. Researchers have conducted extensive experiments on HTP and developed techniques including spectral, fluorescence, thermal, and three-dimensional imaging to measure the morphological, physiological, and pathological resistance traits of cotton. In addition, ground-based and aerial-based platforms were also developed to aid in the implementation of these HTP systems. This review paper highlights the techniques and recent developments for HTP in cotton, reviews the potential applications according to morphological and physiological traits of cotton, and compares the advantages and limitations of these HTP systems when used in cotton cropping systems. Overall, the use of HTP has generated many opportunities to accurately and efficiently measure and analyze diverse traits of cotton. However, because of its relative novelty, HTP has some limitations that constrains the ability to take full advantage of what it can offer. These challenges need to be addressed to increase the accuracy and utility of HTP, which can be done by integrating analytical techniques for big data and continuous advances in imaging.

Shape changing and self-reconfiguring robots

2015 ◽  
Vol 42 (4) ◽  
pp. 290-295 ◽  
Author(s):  
Robert Bogue
Keyword(s):  
Early Stage ◽  
Modular Robots ◽  
Content Type ◽  
New Era ◽  
Technological Advances ◽  
True Shape ◽  
Recent Developments ◽  
Potential Applications ◽  
Practical Reality ◽  
Selection Of

Purpose – This article aims to provide details of recent developments in robots that can change shape and self-reconfigure. Design/methodology/approach – Following an introduction, this article first describes some recent developments in shape-changing materials and then considers a selection of shape-changing robots. It then discusses self-reconfiguring robots and describes a unique self-unfolding robot. Finally, concluding comments are drawn. Findings – This article shows that research into true shape-changing robots is still at an early stage and several very different strategies are being studied. Novel materials are expected to play a key role in many designs and potential applications include search and rescue, health care and surveillance. Self-reconfiguring modular robots are at a more advanced stage and while many can reconfigure to adopt varying shapes and gaits, the ability to accomplish differing tasks in manufacturing is still some way in the future. Overall, the various classes of shape-changing robots being studied represent a move towards a new era in robotic capabilities, but despite many recent technological advances, considerable further work is required before these become a practical reality. Originality/value – This article provides an insight into recent technological advances in shape-changing and self-reconfiguring robots.

Nanoscience of Metal Silicate-Based Pigments

2014 ◽  
Vol 1618 ◽  
pp. 161-166
Author(s):  
Tina T. Salguero ◽  
Darrah Johnson-McDaniel ◽  
Christopher A. Barrett ◽  
Asma Sharafi ◽  
Richard Weimar ◽  
...  
Keyword(s):  
Near Infrared ◽  
Three Dimensional ◽  
Analytical Techniques ◽  
X Ray Diffraction ◽  
Alkali Earth ◽  
Egyptian Blue ◽  
Infrared Luminescence ◽  
Metal Silicate ◽  
Potential Applications ◽  
Jet Printing

ABSTRACTThe colored component of several important ancient pigments, including Egyptian blue and Han blue, are based on alkali earth copper tetrasilicate materials. In recent work, we have found that these layered materials can be chemically exfoliated into their constituent monolayers to provide alkali earth copper tetrasilicate nanosheets—defined by nanometer thickness and lateral dimensions that are on the order of several microns. The facile exfoliation of these materials into nanosheets is especially surprising in view of their long history on artifacts under a variety of environmental conditions, and we have examined the issue of whether archaeological samples are affected by this exfoliation mechanism. We have characterized the properties of these nanosheets by an array of analytical techniques, including powder x-ray diffraction, photoluminescence measurements, and Raman spectroscopy. In all cases, we observe differences between nanosheet and bulk samples that originate from the loss of coupling between layers when going from three-dimensional to two- dimensional structures. Both CaCuSi4O10 nanosheets (derived from Egyptian blue) and BaCuSi4O10 nanosheets (derived from Han blue) have strong near-infrared luminescence properties like their bulk counterparts, yet they are amenable to modern solution processing methods. We have demonstrated ink jet printing with CaCuSi4O10 nanosheet inks, as well as the fabrication of nanosheet-based papers. Potential applications for these materials include NIR-based biomedical imaging and security inks.

scholarly journals Novel 3D Imaging Systems for High-Throughput Phenotyping of Plants

2021 ◽  
Vol 13 (11) ◽  
pp. 2113
Author(s):  
Tian Gao ◽  
Feiyu Zhu ◽  
Puneet Paul ◽  
Jaspreet Sandhu ◽  
Henry Akrofi Doku ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Throughput ◽  
Imaging System ◽  
Point Clouds ◽  
Imaging Systems ◽  
Phenotypic Traits ◽  
Reconstruction Algorithms ◽  
High Quality ◽  
3D Point Clouds ◽  
High Throughput Phenotyping

The use of 3D plant models for high-throughput phenotyping is increasingly becoming a preferred method for many plant science researchers. Numerous camera-based imaging systems and reconstruction algorithms have been developed for the 3D reconstruction of plants. However, it is still challenging to build an imaging system with high-quality results at a low cost. Useful comparative information for existing imaging systems and their improvements is also limited, making it challenging for researchers to make data-based selections. The objective of this study is to explore the possible solutions to address these issues. We introduce two novel systems for plants of various sizes, as well as a pipeline to generate high-quality 3D point clouds and meshes. The higher accuracy and efficiency of the proposed systems make it a potentially valuable tool for enhancing high-throughput phenotyping by integrating 3D traits for increased resolution and measuring traits that are not amenable to 2D imaging approaches. The study shows that the phenotype traits derived from the 3D models are highly correlated with manually measured phenotypic traits (R2 > 0.91). Moreover, we present a systematic analysis of different settings of the imaging systems and a comparison with the traditional system, which provide recommendations for plant scientists to improve the accuracy of 3D construction. In summary, our proposed imaging systems are suggested for 3D reconstruction of plants. Moreover, the analysis results of the different settings in this paper can be used for designing new customized imaging systems and improving their accuracy.

scholarly journals Voxel Carving Based 3D Reconstruction of Sorghum Identifies Genetic Determinants of Radiation Interception Efficiency

2020 ◽  
Author(s):  
Mathieu Gaillard ◽  
Chenyong Miao ◽  
James C. Schnable ◽  
Bedrich Benes
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Three Dimensional ◽  
Light Interception ◽  
Dimensional Structure ◽  
Agricultural Crop ◽  
Canopy Architecture ◽  
Stage Of Development ◽  
Component Traits ◽  
High Throughput Phenotyping

Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and radiation use efficiency of agricultural crop canopies will be essential to meeting growing needs for food. Canopy architecture is inherently 3D, but many approaches to measuring canopy architecture component traits treat the canopy as a two dimensional structure in order to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct three dimensional representations of maize and sorghum from a small number of RGB photos. This approach was employed to generate three dimensional reconstructions of a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of both known and previously unreported loci controlling light interception efficiency in sorghum. The approach described here is generalizable and scalable and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. For future datasets we propose a set of best practices to increase the accuracy of three dimensional reconstructions.

scholarly journals Robust colony recognition for high-throughput growth analysis from suboptimal low-magnification brightfield micrographs

2018 ◽  
Author(s):  
Yevgeniy Plavskin ◽  
Shuang Li ◽  
Naomi Ziv ◽  
Sasha F. Levy ◽  
Mark L. Siegal
Keyword(s):  
High Throughput ◽  
Time Lapse ◽  
Manual Curation ◽  
Technological Advances ◽  
Wide Range ◽  
Colony Recognition ◽  
Gradient Based ◽  
High Throughput Phenotyping ◽  
High Resolution Images ◽  
Object Center

AbstractNew technological advances have enabled high-throughput phenotyping at the single-cell level. However, analyzing the large amount of data automatically and accurately is a great challenge. Currently available software achieves cell and colony tracking through the use of either manual curation of images, which is time consuming, or high-resolution images requiring specialized microscopy setups or fluorescence, which limits applicability and results in greatly decreased experimental throughput. Here we introduce a new algorithm, Processing Images Easily (PIE), that automatically tracks colonies of the yeast Saccharomyces cerevisiae in low-magnification brightfield images by combining adaptive object-center detection with gradient-based object-outline detection. We tested the performance of PIE on low-magnification brightfield time-lapse images. PIE recognizes colony outlines very robustly and accurately across a wide range of image brightnesses and focal depths. We show that PIE allows for unbiased and precise measurement of growth rates in a large number (>90,000) of microcolonies in a single time-lapse experiment.

Ionic liquids in oligosaccharide synthesis: towards mucin-type glycan probes

2010 ◽  
Vol 38 (5) ◽  
pp. 1368-1373 ◽  
Author(s):  
M. Carmen Galan ◽  
Anthony P. Corfield
Keyword(s):  
Ionic Liquids ◽  
Present Article ◽  
High Throughput ◽  
High Sensitivity ◽  
Oligosaccharide Synthesis ◽  
Biological Processes ◽  
Important Class ◽  
High Throughput Analysis ◽  
Recent Developments ◽  
Potential Applications

The present article provides an overview on mucins and their role in biological processes, while aiming to familiarize readers with the current tools available for the synthesis of structurally defined mucin-type glycan probes including the advantages and potential applications of using ionic liquids in the synthesis of this important class of oligosaccharides. Furthermore, we also highlight recent developments in glycoarray technology that can enable high-sensitivity and high-throughput analysis of this important class of protein–carbohydrate interactions.

scholarly journals A UAV-based high-throughput phenotyping approach to assess time-series nitrogen responses and identify traits associated genetic components in maize

2021 ◽  
Author(s):  
Jinliang Yang ◽  
Eric Rodene ◽  
Gen Xu ◽  
Christine Smith ◽  
Yufeng Ge ◽  
...  
Keyword(s):  
Time Series ◽  
High Throughput ◽  
Pearson Correlation ◽  
Phenotypic Trait ◽  
Phenotypic Traits ◽  
Time Points ◽  
Canopy Coverage ◽  
Genetic Components ◽  
Genome Wide ◽  
High Throughput Phenotyping

Advancements in the use of genome-wide markers have provided new opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost-effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time-series manner. In this experiment, 233 inbred lines from the maize diversity panel were grown in a replicated incomplete block under both nitrogen-limited conditions and following conventional agronomic practices. UAV images were collected during different plant developmental stages throughout the growing season. A pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the pipeline, about half a million plot-level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield-related traits collected from the same plots, i.e., Vegetative Index (VEG) vs. leaf nitrogen levels (Pearson correlation coefficient, R = 0.73), Woebbecke index vs. leaf area (R = -0.52), and Visible Atmospherically Resistant Index (VARI) vs. 20 kernel weight --- a yield component trait (R = 0.40). The genome-wide association study was performed using canopy coverage and each of the VIs at each date, resulting in N = 29 unique genomic regions associated with image extracted traits from three or more of the 12 total time points. A candidate gene Zm00001d031997, a maize homolog of the Arabidopsis HCF244 (high chlorophyll fluorescence 244), located underneath the leading SNPs of the canopy coverage associated signals that were repeatedly detected under both nitrogen conditions. The plot-level time-series phenotypic data and the trait-associated genes provide great opportunities to advance plant science and to facilitate plant breeding.

scholarly journals Changes in reflectance of rice seedlings during planthopper feeding as detected by digital camera: Potential applications for high-throughput phenotyping

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0238173
Author(s):  
Finbarr G. Horgan ◽  
Artzai Jauregui ◽  
Ainara Peñalver Cruz ◽  
Eduardo Crisol Martínez ◽  
Carmencita C. Bernal
Keyword(s):  
High Throughput ◽  
Digital Camera ◽  
Rice Seedlings ◽  
Potential Applications ◽  
High Throughput Phenotyping

scholarly journals MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Sheng Wu ◽  
Weiliang Wen ◽  
Yongjian Wang ◽  
Jiangchuan Fan ◽  
Chuanyu Wang ◽  
...  
Keyword(s):  
Data Management ◽  
High Throughput ◽  
Management System ◽  
Low Cost ◽  
Point Clouds ◽  
Data Management System ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
High Throughput Phenotyping ◽  
Multiview Stereo

Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform’s device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R2) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.

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