Agricultural Engineering/Agronomy, Central Iowa, and BioCentury Research Farms

Semi-Automated Feature Extraction from RGB Images for Sorghum Panicle Architecture

Authors: Seyed Vahid Mirnezami (Iowa State University) , Baskar Ganapathysubramanian (Iowa State University) , Yan Zhou (Iowa State University) , Aaron Kusmec (Iowa State University) , Qi Fu (Iowa State University) , Srikant Srinivasan (Iowa State University) , Lakshmi Attigala (Iowa State University) , Maria Salas-Fernandez (Iowa State University) , Patrick Schnable (Iowa State University)

  • Semi-Automated Feature Extraction from RGB Images for Sorghum Panicle Architecture

    Agricultural Engineering/Agronomy, Central Iowa, and BioCentury Research Farms

    Semi-Automated Feature Extraction from RGB Images for Sorghum Panicle Architecture

    Authors: , , , , , , , ,

Abstract

Because structural variation in the inflorescence architecture of cereal crops can influence yield, it is of interest to identify a means to better evaluate inflorescence architecture across genotypes and thereby facilitate breeding efforts. Manual collection of inflorescence phenotypes can be time consuming and can be technically challenging for some traits. For these reasons, a semiautomated phenotyping pipeline, TIM (Toolkit for Inflorescence Measurement), was developed and used to extract trait data from sorghum panicles.

How to Cite:

Mirnezami, S. & Ganapathysubramanian, B. & Zhou, Y. & Kusmec, A. & Fu, Q. & Srinivasan, S. & Attigala, L. & Salas-Fernandez, M. & Schnable, P., (2019) “Semi-Automated Feature Extraction from RGB Images for Sorghum Panicle Architecture”, Iowa State University Research and Demonstration Farms Progress Reports 2018(1).

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Published on
26 Apr 2019
Peer Reviewed
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