Staff View: Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves

Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves

Segmentation of diseased symptom regions in images of plant leaves is a crucial stage in the application of machine learning for plant diseases detection. This process also known as Region of Interest (ROI) segmentation involves separating purely color variant symptom lesions from surrounding green...

Full description

Saved in:

Bibliographic Details
Main Authors:	Abdu, A. M., Mohd. Mokji, M., Sheikh, U. U., Khalil, K.
Format:	Conference or Workshop Item
Published:	2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/90254/ http://dx.doi.org/10.1109/CSPA.2019.8696049
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.utm.90254
record_format	eprints
spelling	my.utm.902542021-03-30T07:48:47Z http://eprints.utm.my/id/eprint/90254/ Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves Abdu, A. M. Mohd. Mokji, M. Sheikh, U. U. Khalil, K. TK Electrical engineering. Electronics Nuclear engineering Segmentation of diseased symptom regions in images of plant leaves is a crucial stage in the application of machine learning for plant diseases detection. This process also known as Region of Interest (ROI) segmentation involves separating purely color variant symptom lesions from surrounding green tissue from which discriminant features are later extracted. However, investigations have shown that vivid anatomy of a disease symptom progression right from inception to manifestation through which finer disease characterization dissimilarity features can be fostered are not captured in a segmented ROI. Furthermore, the typical ROI segmentation process is often plagued by challenges ranging from intrinsic factors such as image capture conditions to extrinsic factors such as disease anatomy where symptoms fade into healthy green tissue the separation boundary to become impalpable. This adds further complexity to the process or produce erroneous result. This research proposes an automatic extended region of interest (EROI) segmentation to incorporate symptom progression information by extending the border region to cover some part of healthy tissue using color homogeneity thresholding. To produce a ground truth, the typical ROI segmentation alongside a reduced ROI were implemented on a well-known PlantVillage dataset from which separate textural and color features were extracted and used to build a linear classifier. A comparison between the classification results further reinforced the advantages of the proposed approach for dissimilarity features extraction. Through this research, finer characterization features can be extracted for the classification and severity estimation of plant diseases. 2019 Conference or Workshop Item PeerReviewed Abdu, A. M. and Mohd. Mokji, M. and Sheikh, U. U. and Khalil, K. (2019) Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves. In: 15th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2019, 8-9 March 2019, Parkroyal Penang Resort Penang, Malaysia. http://dx.doi.org/10.1109/CSPA.2019.8696049
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Abdu, A. M. Mohd. Mokji, M. Sheikh, U. U. Khalil, K. Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves
description	Segmentation of diseased symptom regions in images of plant leaves is a crucial stage in the application of machine learning for plant diseases detection. This process also known as Region of Interest (ROI) segmentation involves separating purely color variant symptom lesions from surrounding green tissue from which discriminant features are later extracted. However, investigations have shown that vivid anatomy of a disease symptom progression right from inception to manifestation through which finer disease characterization dissimilarity features can be fostered are not captured in a segmented ROI. Furthermore, the typical ROI segmentation process is often plagued by challenges ranging from intrinsic factors such as image capture conditions to extrinsic factors such as disease anatomy where symptoms fade into healthy green tissue the separation boundary to become impalpable. This adds further complexity to the process or produce erroneous result. This research proposes an automatic extended region of interest (EROI) segmentation to incorporate symptom progression information by extending the border region to cover some part of healthy tissue using color homogeneity thresholding. To produce a ground truth, the typical ROI segmentation alongside a reduced ROI were implemented on a well-known PlantVillage dataset from which separate textural and color features were extracted and used to build a linear classifier. A comparison between the classification results further reinforced the advantages of the proposed approach for dissimilarity features extraction. Through this research, finer characterization features can be extracted for the classification and severity estimation of plant diseases.
format	Conference or Workshop Item
author	Abdu, A. M. Mohd. Mokji, M. Sheikh, U. U. Khalil, K.
author_facet	Abdu, A. M. Mohd. Mokji, M. Sheikh, U. U. Khalil, K.
author_sort	Abdu, A. M.
title	Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves
title_short	Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves
title_full	Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves
title_fullStr	Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves
title_full_unstemmed	Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves
title_sort	automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves
publishDate	2019
url	http://eprints.utm.my/id/eprint/90254/ http://dx.doi.org/10.1109/CSPA.2019.8696049
_version_	1696976283753250816
score	13.211869

Automatic disease symptoms segmentation optimized for dissimilarity feature extraction in digital photographs of plant leaves

Similar Items