Mycelial growth assessment by digital image analysis in R software environment

This study was aimed at developing a script in R to assess fungal growth in Petri dishes using computer vision. The script was developed to aid studies in agricultural phytopathology. The fungi used were Elsinoe ampelina, Fusarium oxysporum and Fusarium verticillioides. The images were analyzed with...

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Autores principales:	Oliveira da Silva,Fernanda, Terumi Itako,Adriana, Tolentino Júnior,Joao Batista
Lenguaje:	English
Publicado:	Universidad de Tarapacá. Facultad de Ciencias Agronómicas 2017
Materias:	software computer vision EBImage mycelial growth
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-34292017000100002
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spelling	oai:scielo:S0718-342920170001000022017-08-28Mycelial growth assessment by digital image analysis in R software environmentOliveira da Silva,FernandaTerumi Itako,AdrianaTolentino Júnior,Joao Batista software computer vision EBImage mycelial growth This study was aimed at developing a script in R to assess fungal growth in Petri dishes using computer vision. The script was developed to aid studies in agricultural phytopathology. The fungi used were Elsinoe ampelina, Fusarium oxysporum and Fusarium verticillioides. The images were analyzed with R and the EBImage package. The command computeFeatures.shape was used to calculate the area and the mean diameter of the colony and the label in square pixels. The script was run in a loop to automate the analysis of all images in sequence. The script developed in R with the package EBImage was able to recognize and measure the fungus colonies examined. Simultaneously with photographing, the mean diameter of colonies was obtained by calculating the arithmetic mean of the diameter measured with two straight perpendicular lines using a ruler. Using the mean diameter, the area of the colony was calculated as a circle. The coefficient of determination and the root mean square error were calculated using the measurements and the values obtained with the program. Based on the coefficient of determination, the measurements obtained with the software are similar to those made using a ruler. The script reduces the image analysis time and increases accuracy, especially for non-uniform colonies.info:eu-repo/semantics/openAccessUniversidad de Tarapacá. Facultad de Ciencias AgronómicasIdesia (Arica) v.35 n.1 20172017-03-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-34292017000100002en10.4067/S0718-34292017005000001
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	software computer vision EBImage mycelial growth
spellingShingle	software computer vision EBImage mycelial growth Oliveira da Silva,Fernanda Terumi Itako,Adriana Tolentino Júnior,Joao Batista Mycelial growth assessment by digital image analysis in R software environment
description	This study was aimed at developing a script in R to assess fungal growth in Petri dishes using computer vision. The script was developed to aid studies in agricultural phytopathology. The fungi used were Elsinoe ampelina, Fusarium oxysporum and Fusarium verticillioides. The images were analyzed with R and the EBImage package. The command computeFeatures.shape was used to calculate the area and the mean diameter of the colony and the label in square pixels. The script was run in a loop to automate the analysis of all images in sequence. The script developed in R with the package EBImage was able to recognize and measure the fungus colonies examined. Simultaneously with photographing, the mean diameter of colonies was obtained by calculating the arithmetic mean of the diameter measured with two straight perpendicular lines using a ruler. Using the mean diameter, the area of the colony was calculated as a circle. The coefficient of determination and the root mean square error were calculated using the measurements and the values obtained with the program. Based on the coefficient of determination, the measurements obtained with the software are similar to those made using a ruler. The script reduces the image analysis time and increases accuracy, especially for non-uniform colonies.
author	Oliveira da Silva,Fernanda Terumi Itako,Adriana Tolentino Júnior,Joao Batista
author_facet	Oliveira da Silva,Fernanda Terumi Itako,Adriana Tolentino Júnior,Joao Batista
author_sort	Oliveira da Silva,Fernanda
title	Mycelial growth assessment by digital image analysis in R software environment
title_short	Mycelial growth assessment by digital image analysis in R software environment
title_full	Mycelial growth assessment by digital image analysis in R software environment
title_fullStr	Mycelial growth assessment by digital image analysis in R software environment
title_full_unstemmed	Mycelial growth assessment by digital image analysis in R software environment
title_sort	mycelial growth assessment by digital image analysis in r software environment
publisher	Universidad de Tarapacá. Facultad de Ciencias Agronómicas
publishDate	2017
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-34292017000100002
work_keys_str_mv	AT oliveiradasilvafernanda mycelialgrowthassessmentbydigitalimageanalysisinrsoftwareenvironment AT terumiitakoadriana mycelialgrowthassessmentbydigitalimageanalysisinrsoftwareenvironment AT tolentinojuniorjoaobatista mycelialgrowthassessmentbydigitalimageanalysisinrsoftwareenvironment
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Mycelial growth assessment by digital image analysis in R software environment

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