Literature DB >> 28738313

High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field.

Nadia Shakoor1, Scott Lee1, Todd C Mockler2.   

Abstract

Effective implementation of technology that facilitates accurate and high-throughput screening of thousands of field-grown lines is critical for accelerating crop improvement and breeding strategies for higher yield and disease tolerance. Progress in the development of field-based high throughput phenotyping methods has advanced considerably in the last 10 years through technological progress in sensor development and high-performance computing. Here, we review recent advances in high throughput field phenotyping technologies designed to inform the genetics of quantitative traits, including crop yield and disease tolerance. Successful application of phenotyping platforms to advance crop breeding and identify and monitor disease requires: (1) high resolution of imaging and environmental sensors; (2) quality data products that facilitate computer vision, machine learning and GIS; (3) capacity infrastructure for data management and analysis; and (4) automated environmental data collection. Accelerated breeding for agriculturally relevant crop traits is key to the development of improved varieties and is critically dependent on high-resolution, high-throughput field-scale phenotyping technologies that can efficiently discriminate better performing lines within a larger population and across multiple environments.
Copyright © 2017. Published by Elsevier Ltd.

Mesh:

Year:  2017        PMID: 28738313     DOI: 10.1016/j.pbi.2017.05.006

Source DB:  PubMed          Journal:  Curr Opin Plant Biol        ISSN: 1369-5266            Impact factor:   7.834


  45 in total

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2.  DeepPod: a convolutional neural network based quantification of fruit number in Arabidopsis.

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Review 3.  Neglected treasures in the wild - legume wild relatives in food security and human health.

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4.  Joint linkage and association mapping of complex traits in shrub willow (Salix purpurea L.).

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Review 5.  Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.

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Journal:  J Adv Res       Date:  2021-05-12       Impact factor: 10.479

Review 6.  Breeding crops to feed 10 billion.

Authors:  Lee T Hickey; Amber N Hafeez; Hannah Robinson; Scott A Jackson; Soraya C M Leal-Bertioli; Mark Tester; Caixia Gao; Ian D Godwin; Ben J Hayes; Brande B H Wulff
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7.  Enviromics in breeding: applications and perspectives on envirotypic-assisted selection.

Authors:  Rafael T Resende; Hans-Peter Piepho; Guilherme J M Rosa; Orzenil B Silva-Junior; Fabyano F E Silva; Marcos Deon V de Resende; Dario Grattapaglia
Journal:  Theor Appl Genet       Date:  2020-09-22       Impact factor: 5.699

8.  High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat.

Authors:  Fátima Camarillo-Castillo; Trevis D Huggins; Suchismita Mondal; Matthew P Reynolds; Michael Tilley; Dirk B Hays
Journal:  Plant Methods       Date:  2021-06-07       Impact factor: 4.993

Review 9.  Review: Application of Artificial Intelligence in Phenomics.

Authors:  Shona Nabwire; Hyun-Kwon Suh; Moon S Kim; Insuck Baek; Byoung-Kwan Cho
Journal:  Sensors (Basel)       Date:  2021-06-25       Impact factor: 3.576

10.  Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.

Authors:  Mariela Fernández-Campos; Yu-Ting Huang; Mohammad R Jahanshahi; Tao Wang; Jian Jin; Darcy E P Telenko; Carlos Góngora-Canul; C D Cruz
Journal:  Front Plant Sci       Date:  2021-06-17       Impact factor: 5.753

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