Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods

IntroductionControl of walnut diseases and pests requires the mapping of the extent of contamination within possible shortest time. Therefore, it is necessary to develop systems to detect and determine the prevalence and location of contamination for researchers and gardeners. Image processing has b...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	A Yousefvand, J Amiri Parian
Formato:	article
Lenguaje:	EN FA
Publicado:	Ferdowsi University of Mashhad 2021
Materias:	color features image processing morphology features walnut tree Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/d94dae6d530e45668147241fe12883f7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d94dae6d530e45668147241fe12883f7
record_format	dspace
spelling	oai:doaj.org-article:d94dae6d530e45668147241fe12883f72021-11-14T06:40:21ZAnthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods2228-68292423-394310.22067/jam.v11i2.82558https://doaj.org/article/d94dae6d530e45668147241fe12883f72021-09-01T00:00:00Zhttps://jame.um.ac.ir/article_34797_dc35d156764e5a912e54c6ee2d367db5.pdfhttps://doaj.org/toc/2228-6829https://doaj.org/toc/2423-3943IntroductionControl of walnut diseases and pests requires the mapping of the extent of contamination within possible shortest time. Therefore, it is necessary to develop systems to detect and determine the prevalence and location of contamination for researchers and gardeners. Image processing has been proposed as an approach to determine the extent and type of damage to different products in farms and gardens. The aim of this study was to design an algorithm based on the processing of walnut leaf images under natural light conditions in order to provide a rapid and non-destructive detection of diseases for the protection of trees using imaging methods. In this research, the possibility of detecting Anthracnose disease was investigated by processing walnut leaf images. The disease was detected using in situ images taken from the leaves to provide the basis for designing application software on smart mechatronic systems. Materials and MethodsImages of leaves on walnut trees were taken under outdoor light conditions. Color and morphological properties extracted from the images were used to detect the pest on the leaves. Gnomonia leptostyla disease diagnostic algorithm was based on process of color and morphological characteristics, leaves background and disease-stained spots. The range of changes in R, G, and B indices was obtained in histograms and then two-dimensional spaces were analyzed statistically on GR, GB, and BR planes. All points from these regions were used as statistical samples, for which bivariate regressions of GR, GB, and BR were obtained as y = b0 + b1x. Segments containing anthracnose spots from the leaves were segregated by extracting the coordinates of the points in each side on the RGB color space cube. Finally, anthracnose content was detected based on the number of spots detected by the algorithms. The percentage of contamination was used to determine the amount of contamination in each imaged area.Results and DiscussionExamination of the colored spaces indicated that the domain of the anthracnose color components on the GR side has nothing in common with the color components of the leaves. The analysis of color space data revealed that the leaves and anthracnose were more distinguishable on the GB and RB sides, respectively. According to the histogram of the HSV color space, anthracnose spots were isolated from the leaves by determining the H range. In the evaluation of the proposed method for diagnosis of anthracnose, the infection severity calculated by the algorithm with the true infection intensity. T-test results for comparing the mean of the two infection intensity samples showed no significant differences between the two groups at 1% probability level. ConclusionsThe evaluation of the proposed method showed a 98% segregation accuracy for G. leptostyla detection method. Based on the results, the proposed method for detecting anthracnose spots is suitable for determining the contamination severity in the imaged areas.A YousefvandJ Amiri ParianFerdowsi University of Mashhadarticlecolor featuresimage processingmorphology featureswalnut treeAgriculture (General)S1-972Engineering (General). Civil engineering (General)TA1-2040ENFAJournal of Agricultural Machinery, Vol 11, Iss 2, Pp 317-329 (2021)
institution	DOAJ
collection	DOAJ
language	EN FA
topic	color features image processing morphology features walnut tree Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	color features image processing morphology features walnut tree Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040 A Yousefvand J Amiri Parian Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods
description	IntroductionControl of walnut diseases and pests requires the mapping of the extent of contamination within possible shortest time. Therefore, it is necessary to develop systems to detect and determine the prevalence and location of contamination for researchers and gardeners. Image processing has been proposed as an approach to determine the extent and type of damage to different products in farms and gardens. The aim of this study was to design an algorithm based on the processing of walnut leaf images under natural light conditions in order to provide a rapid and non-destructive detection of diseases for the protection of trees using imaging methods. In this research, the possibility of detecting Anthracnose disease was investigated by processing walnut leaf images. The disease was detected using in situ images taken from the leaves to provide the basis for designing application software on smart mechatronic systems. Materials and MethodsImages of leaves on walnut trees were taken under outdoor light conditions. Color and morphological properties extracted from the images were used to detect the pest on the leaves. Gnomonia leptostyla disease diagnostic algorithm was based on process of color and morphological characteristics, leaves background and disease-stained spots. The range of changes in R, G, and B indices was obtained in histograms and then two-dimensional spaces were analyzed statistically on GR, GB, and BR planes. All points from these regions were used as statistical samples, for which bivariate regressions of GR, GB, and BR were obtained as y = b0 + b1x. Segments containing anthracnose spots from the leaves were segregated by extracting the coordinates of the points in each side on the RGB color space cube. Finally, anthracnose content was detected based on the number of spots detected by the algorithms. The percentage of contamination was used to determine the amount of contamination in each imaged area.Results and DiscussionExamination of the colored spaces indicated that the domain of the anthracnose color components on the GR side has nothing in common with the color components of the leaves. The analysis of color space data revealed that the leaves and anthracnose were more distinguishable on the GB and RB sides, respectively. According to the histogram of the HSV color space, anthracnose spots were isolated from the leaves by determining the H range. In the evaluation of the proposed method for diagnosis of anthracnose, the infection severity calculated by the algorithm with the true infection intensity. T-test results for comparing the mean of the two infection intensity samples showed no significant differences between the two groups at 1% probability level. ConclusionsThe evaluation of the proposed method showed a 98% segregation accuracy for G. leptostyla detection method. Based on the results, the proposed method for detecting anthracnose spots is suitable for determining the contamination severity in the imaged areas.
format	article
author	A Yousefvand J Amiri Parian
author_facet	A Yousefvand J Amiri Parian
author_sort	A Yousefvand
title	Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods
title_short	Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods
title_full	Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods
title_fullStr	Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods
title_full_unstemmed	Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods
title_sort	anthracnose detection on walnut tree leaves using outdoor image processing methods
publisher	Ferdowsi University of Mashhad
publishDate	2021
url	https://doaj.org/article/d94dae6d530e45668147241fe12883f7
work_keys_str_mv	AT ayousefvand anthracnosedetectiononwalnuttreeleavesusingoutdoorimageprocessingmethods AT jamiriparian anthracnosedetectiononwalnuttreeleavesusingoutdoorimageprocessingmethods
_version_	1718429869358972928

Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods

Ejemplares similares