TELKOMNIKA Telecommunication, Computing, Electronics and Control
Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation
Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation
Subject
Blast, Blobs, Brown-spot, Color segmentation, Narrow brown-spot
Description
There are three types of paddy leaf diseases that have similar symptoms,
making it difficult for farmers to identify them, namely blast, brown-spot,
and narrow brown-spot. This study aims to identification paddy plant
diseases based on texture analysis of Blobs and color segmentation.
Blobs analysis is used to get the number of objects, area and perimeter.
Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease.
making it difficult for farmers to identify them, namely blast, brown-spot,
and narrow brown-spot. This study aims to identification paddy plant
diseases based on texture analysis of Blobs and color segmentation.
Blobs analysis is used to get the number of objects, area and perimeter.
Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease.
Creator
Alex Wenda, Inggih Permana, Yusmar , Nunik Noviana Kurniawati
Source
DOI: 10.12928/TELKOMNIKA.v18i4.14614
Publisher
Universitas Ahmad Dahlan
Date
August 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Alex Wenda, Inggih Permana, Yusmar , Nunik Noviana Kurniawati, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation,” Repository Horizon University Indonesia, accessed March 14, 2025, https://repository.horizon.ac.id/items/show/3978.
Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation,” Repository Horizon University Indonesia, accessed March 14, 2025, https://repository.horizon.ac.id/items/show/3978.