Literature DB >> 18944921

Microcomputer-Based Quantification of Maize Streak Virus Symptoms in Zea mays.

D P Martin, E P Rybicki.   

Abstract

ABSTRACT We investigated the use of computer-assisted image analysis techniques for the objective quantification of maize streak virus (MSV) symptoms in Zea mays. We compared independent duplicate evaluations of chlorotic lesion areas occurring on MSV-infected leaves using visual assessment, a commercial image analysis system, and a custom image analysis system employing software developed in our laboratory. Relative to visual assessments of disease severity, computer-assisted image analysis employing both the commercial and custom systems provided significant enhancements in the accuracy and precision of chlorotic area estimations. The commercial image analysis system afforded no significant improvement in precision or accuracy over the custom system. An important advantage of examining images using the custom-written software was that the software permitted a high degree of analysis automation. Digitized images of maize leaves could be automatically analyzed by the custom software five times faster than, and with the same precision and accuracy as, when the same images were analyzed with the commercial software. Because of the flexibility of the image analysis techniques described, they should be applicable to the measurement of symptom severity in other plant host-pathogen combinations.

Entities:  

Year:  1998        PMID: 18944921     DOI: 10.1094/PHYTO.1998.88.5.422

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  9 in total

1.  Extensive recombination-induced disruption of genetic interactions is highly deleterious but can be partially reversed by small numbers of secondary recombination events.

Authors:  Adérito L Monjane; Darren P Martin; Francisco Lakay; Brejnev M Muhire; Daniel Pande; Arvind Varsani; Gordon Harkins; Dionne N Shepherd; Edward P Rybicki
Journal:  J Virol       Date:  2014-04-30       Impact factor: 5.103

2.  Rapid host adaptation by extensive recombination.

Authors:  Eric van der Walt; Edward P Rybicki; Arvind Varsani; J E Polston; Rosalind Billharz; Lara Donaldson; Adérito L Monjane; Darren P Martin
Journal:  J Gen Virol       Date:  2009-03       Impact factor: 3.891

3.  Recombination hotspots and host susceptibility modulate the adaptive value of recombination during maize streak virus evolution.

Authors:  Adérito L Monjane; Eric van der Walt; Arvind Varsani; Edward P Rybicki; Darren P Martin
Journal:  BMC Evol Biol       Date:  2011-12-02       Impact factor: 3.260

4.  Smart sensor for real-time quantification of common symptoms present in unhealthy plants.

Authors:  Luis M Contreras-Medina; Roque A Osornio-Rios; Irineo Torres-Pacheco; Rene de J Romero-Troncoso; Ramon G Guevara-González; Jesus R Millan-Almaraz
Journal:  Sensors (Basel)       Date:  2012-01-11       Impact factor: 3.576

5.  A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework.

Authors:  Bifta Sama Bari; Md Nahidul Islam; Mamunur Rashid; Md Jahid Hasan; Mohd Azraai Mohd Razman; Rabiu Muazu Musa; Ahmad Fakhri Ab Nasir; Anwar P P Abdul Majeed
Journal:  PeerJ Comput Sci       Date:  2021-04-07

Review 6.  Maize streak virus research in Africa: an end or a crossroad.

Authors:  Mary Emeraghi; Enoch G Achigan-Dako; Chibuzo N C Nwaoguala; Happiness Oselebe
Journal:  Theor Appl Genet       Date:  2021-07-26       Impact factor: 5.699

Review 7.  Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology.

Authors:  Kuo-Szu Chiang; Clive H Bock
Journal:  Trop Plant Pathol       Date:  2021-07-13       Impact factor: 2.404

Review 8.  Digital image processing techniques for detecting, quantifying and classifying plant diseases.

Authors:  Jayme Garcia Arnal Barbedo
Journal:  Springerplus       Date:  2013-12-07

9.  Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis.

Authors:  Bo Li; Michelle T Hulin; Philip Brain; John W Mansfield; Robert W Jackson; Richard J Harrison
Journal:  Plant Methods       Date:  2015-12-24       Impact factor: 4.993

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.