Literature DB >> 32638865

High-throughput phenotyping of brachiaria grass seeds using free access tool for analyzing X-ray images.

AndrÉ D DE Medeiros1, LaÉrcio J DA Silva1, MÁrcio D Pereira2, Ariadne M S Oliveira1, Denise C F S Dias1.   

Abstract

New approaches based on image analysis can assist in phenotyping of biological characteristics, serving as support for decision-making in modern agribusiness. The aim of this study was to propose a method of high-throughput phenotyping of free access for processing of 2D X-ray images of brachiaria grass (Brachiaria ruziziensis cv. Ruziziensis) seeds, as well as correlate the parameters linked to the physiological potential of the seeds. The study was carried out by means of automated analysis of X-ray images of seeds in which a macro, called PhenoXray, was developed, responsible for digital image processing, for which a series of descriptors were obtained. After the X-ray analysis, a germination test was performed on the seeds and, from this, variables related to the physiological quality of the seeds were obtained. The use of the macro PhenoXray allowed large-scale phenotyping of seed X-rays in a simple, rapid, robust, and totally free manner. This study confirmed that the methodology is efficient for obtaining morphometric data and tissue integrity data in Brachiaria ruziziensis seeds and that parameters such as relative density, integrated density, and seed filling are closely related to the physiological attributes of seed quality.

Entities:  

Year:  2020        PMID: 32638865     DOI: 10.1590/0001-3765202020190209

Source DB:  PubMed          Journal:  An Acad Bras Cienc        ISSN: 0001-3765            Impact factor:   1.753


  2 in total

Review 1.  Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.

Authors:  Qinlin Xiao; Xiulin Bai; Chu Zhang; Yong He
Journal:  J Adv Res       Date:  2021-05-12       Impact factor: 10.479

2.  Classification of Watermelon Seeds Using Morphological Patterns of X-ray Imaging: A Comparison of Conventional Machine Learning and Deep Learning.

Authors:  Mohammed Raju Ahmed; Jannat Yasmin; Eunsung Park; Geonwoo Kim; Moon S Kim; Collins Wakholi; Changyeun Mo; Byoung-Kwan Cho
Journal:  Sensors (Basel)       Date:  2020-11-26       Impact factor: 3.576

  2 in total

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