A Type-2 Fuzzy Clustering and Quantum Optimization Approach for Crops Image Segmentation

Automatic detection of crop yield ripeness is a tedious task because of the presence of various intensities of color in crops. One of the solutions to this problem is the monitoring of those crops by performing segmentation operations. This operation can help to distinguish the ripe and non-ripe reg...

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Published in:	International journal of fuzzy systems Vol. 23; no. 3; pp. 615 - 629
Main Authors:	Huang, Yo-Ping, Singh, Pritpal, Kuo, Wen-Lin, Chu, Hung-Chi
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2021 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Cluster analysis Clustering Color imagery Computational Intelligence Computer vision Crop yield Engineering Fuzzy sets Image segmentation Management Science Memberships Methods Operations Research Optimization Pattern recognition Researchers Set theory Support vector machines Vector quantization Modified quantum optimization algorithm (MQOA) Image segmentation means clustering Fuzzy set
ISSN:	1562-2479, 2199-3211
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Abstract	Automatic detection of crop yield ripeness is a tedious task because of the presence of various intensities of color in crops. One of the solutions to this problem is the monitoring of those crops by performing segmentation operations. This operation can help to distinguish the ripe and non-ripe regions among the crop images. For this purpose, this study presents a new hybrid crop image segmentation method utilizing type-2 fuzzy set (T2FS), K -means clustering algorithm, and modified quantum optimization algorithm (MQOA). The proposed method fully utilizes the indispensable qualities of these three techniques by (a) using T2FS to represent each color component of crop images in terms of secondary memberships, (b) applying K -means clustering algorithm to extract the similar features from the set of type-2 entropy values obtained from the secondary memberships, and (c) exploiting MQOA to optimize the distance function used in K -means clustering algorithm to obtain the optimal clusters. The performance of the proposed method is assessed based on the experiments carried out on color images of cherry tomatoes. Evidence of experimental results suggests that the proposed method produces extremely effective segmented images relative to those well-known color image segmentation methods available in the literature of pattern recognition and computer vision domains.
AbstractList	Automatic detection of crop yield ripeness is a tedious task because of the presence of various intensities of color in crops. One of the solutions to this problem is the monitoring of those crops by performing segmentation operations. This operation can help to distinguish the ripe and non-ripe regions among the crop images. For this purpose, this study presents a new hybrid crop image segmentation method utilizing type-2 fuzzy set (T2FS), K-means clustering algorithm, and modified quantum optimization algorithm (MQOA). The proposed method fully utilizes the indispensable qualities of these three techniques by (a) using T2FS to represent each color component of crop images in terms of secondary memberships, (b) applying K-means clustering algorithm to extract the similar features from the set of type-2 entropy values obtained from the secondary memberships, and (c) exploiting MQOA to optimize the distance function used in K-means clustering algorithm to obtain the optimal clusters. The performance of the proposed method is assessed based on the experiments carried out on color images of cherry tomatoes. Evidence of experimental results suggests that the proposed method produces extremely effective segmented images relative to those well-known color image segmentation methods available in the literature of pattern recognition and computer vision domains. Automatic detection of crop yield ripeness is a tedious task because of the presence of various intensities of color in crops. One of the solutions to this problem is the monitoring of those crops by performing segmentation operations. This operation can help to distinguish the ripe and non-ripe regions among the crop images. For this purpose, this study presents a new hybrid crop image segmentation method utilizing type-2 fuzzy set (T2FS), K -means clustering algorithm, and modified quantum optimization algorithm (MQOA). The proposed method fully utilizes the indispensable qualities of these three techniques by (a) using T2FS to represent each color component of crop images in terms of secondary memberships, (b) applying K -means clustering algorithm to extract the similar features from the set of type-2 entropy values obtained from the secondary memberships, and (c) exploiting MQOA to optimize the distance function used in K -means clustering algorithm to obtain the optimal clusters. The performance of the proposed method is assessed based on the experiments carried out on color images of cherry tomatoes. Evidence of experimental results suggests that the proposed method produces extremely effective segmented images relative to those well-known color image segmentation methods available in the literature of pattern recognition and computer vision domains.
Author	Huang, Yo-Ping Chu, Hung-Chi Kuo, Wen-Lin Singh, Pritpal
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Division, Taiwan Agriculture Research Institute (June 2020). https://www.tari.gov.tw/english/. – reference: CastilloOSanchezMAGonzalezCIMartinezGEReview of recent type-2 fuzzy image processing applicationsInformation2017897118 – reference: MiguelLDSantosHSesma-SaraMBedregalBJurioABustinceHHagrasHType-2 fuzzy entropy setsIEEE Trans. Fuzzy Syst.2017254993100510.1109/TFUZZ.2016.2593497 – reference: LiKTanZAn improved flower pollination optimizer algorithm for multilevel image thresholdingIEEE Access2019716557116558210.1109/ACCESS.2019.2953494 – reference: AbbasgholipourMOmidMKeyhaniAMohtasebiSColor image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditionsExpert Syst. Appl.20113843671367810.1016/j.eswa.2010.09.023 – reference: SinghPHuangY-PA new hybrid time series forecasting model based on the neutrosophic set and quantum optimization algorithmComput. 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Snippet	Automatic detection of crop yield ripeness is a tedious task because of the presence of various intensities of color in crops. One of the solutions to this...
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SubjectTerms	Algorithms Artificial Intelligence Cluster analysis Clustering Color imagery Computational Intelligence Computer vision Crop yield Engineering Fuzzy sets Image segmentation Management Science Memberships Methods Operations Research Optimization Pattern recognition Researchers Set theory Support vector machines Vector quantization
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Title	A Type-2 Fuzzy Clustering and Quantum Optimization Approach for Crops Image Segmentation
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