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

      1 , 1 , 2
      Current opinion in plant biology
      Elsevier BV

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          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.

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          Author and article information

          Journal
          Curr. Opin. Plant Biol.
          Current opinion in plant biology
          Elsevier BV
          1879-0356
          1369-5266
          August 2017
          : 38
          Affiliations
          [1 ] Donald Danforth Plant Science Center, United States.
          [2 ] Donald Danforth Plant Science Center, United States. Electronic address: tmockler@danforthcenter.org.
          Article
          S1369-5266(17)30067-5
          10.1016/j.pbi.2017.05.006
          28738313
          a536c379-96e1-4050-aea9-b9ef5ab23a5c
          History

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