Literature DB >> 33597957

SeedExtractor: An Open-Source GUI for Seed Image Analysis.

Feiyu Zhu1, Puneet Paul2, Waseem Hussain2, Kyle Wallman2, Balpreet K Dhatt2, Jaspreet Sandhu2, Larissa Irvin2, Gota Morota3, Hongfeng Yu1, Harkamal Walia2.   

Abstract

Accurate measurement of seed size parameters is essential for both breeding efforts aimed at enhancing yields and basic research focused on discovering genetic components that regulate seed size. To address this need, we have developed an open-source graphical user interface (GUI) software, SeedExtractor that determines seed size and shape (including area, perimeter, length, width, circularity, and centroid), and seed color with capability to process a large number of images in a time-efficient manner. In this context, our application takes ∼2 s for analyzing an image, i.e., significantly less compared to the other tools. As this software is open-source, it can be modified by users to serve more specific needs. The adaptability of SeedExtractor was demonstrated by analyzing scanned seeds from multiple crops. We further validated the utility of this application by analyzing mature-rice seeds from 231 accessions in Rice Diversity Panel 1. The derived seed-size traits, such as seed length, width, were used for genome-wide association analysis. We identified known loci for regulating seed length (GS3) and width (qSW5/GW5) in rice, which demonstrates the accuracy of this application to extract seed phenotypes and accelerate trait discovery. In summary, we present a publicly available application that can be used to determine key yield-related traits in crops.
Copyright © 2021 Zhu, Paul, Hussain, Wallman, Dhatt, Sandhu, Irvin, Morota, Yu and Walia.

Entities:  

Keywords:  GWAS; genome wide analysis; image analysis; rice; seed color; seed size

Year:  2021        PMID: 33597957      PMCID: PMC7882627          DOI: 10.3389/fpls.2020.581546

Source DB:  PubMed          Journal:  Front Plant Sci        ISSN: 1664-462X            Impact factor:   5.753


  1 in total

1.  HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds.

Authors:  Tian Gao; Anil Kumar Nalini Chandran; Puneet Paul; Harkamal Walia; Hongfeng Yu
Journal:  Sensors (Basel)       Date:  2021-12-08       Impact factor: 3.576

  1 in total

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