nach oben

Neural Computing and Applications

Erschienen in:

26.06.2017 | Original Article

Plant disease leaf image segmentation based on superpixel clustering and EM algorithm

verfasst von: Shanwen Zhang, Zhuhong You, Xiaowei Wu

Erschienen in: Neural Computing and Applications | Sonderheft 2/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Plant disease leaf image segmentation plays an important role in the plant disease detection through leaf symptoms. A novel segmentation method of plant disease leaf image is proposed based on a hybrid clustering. The whole color leaf image is firstly divided into a number of compact and nearly uniform superpixels by superpixel clustering, which can provide useful clustering cues to guide image segmentation to accelerate the convergence speed of the expectation maximization (EM) algorithm, and then, the lesion pixels are quickly and accurately segmented from each superpixel by EM algorithm. The experimental results and the comparison results with similar approaches demonstrate that the proposed method is effective and has high practical value for plant disease detection.

Vorheriger Artikel Optimum section selection procedure for horizontal axis tidal stream turbines

Nächster Artikel Stability of an iterative fractional multi-agent system

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Revathy R, Chennakesavan SA (2015) Threshold based approach for disease spot detection on plant leaf. Trans Eng Sci 3(5):72–75

Li Guanlin M, Zhanhong HC et al (2010) Segmentation of color images of grape disease using K_means clustering algorithm. Trans CSAE 26(2):32–37

Yuan Y, Li M, Liang Q et al (2011) Segmentation method for crop disease leaf images with complex background. Trans CSAE 27(2):208–212

Arivazhagan S, Newlin Shebiah R, Ananthi S et al (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int: CIGR J 15(1):211–217

Gui JS, Hao L, Zhang Q et al (2015) A new method for soybean leaf disease detection based on modified salient regions. Int J Multimed Ubiquitous Eng 10(6):45–52CrossRef

Al-Hiary H, Bani-Ahmad S, Reyalat M et al (2011) Fast and accurate detection and classification of plant diseases. Int J Comput Appl (0975–8887) 17(1):31–38

Yugang R, Jian Z, Miao L et al (2012) Segmentation method for crop disease leaf images based on watershed algorithm. J Comput Appl 32(3):752–755

Kaur R, Kang SS (2015) An enhancement in classifier support vector machine to improve plant disease detection. In: IEEE 3rd international conference on innovation and technology in education (MITE), 135–140

Chaudhary P, Chaudhari AK, Cheeran AN et al (2012) Color transform based approach for disease spot detection on plant leaf. Int J Comput Sci Telecommun 3(6):65–70

10.

Hanping M, Yan cheng Z, Bo H (2008) Segmentation of crop disease leaf images using fuzzy C-means clustering algorithm. Trans CSAE 24(9):136–140

11.

Menukaewjinda A, Kumsawat P, Attakitmongcol K (2008) Grape leaf disease detection from color imagery using hybrid intelligent system. In Proceeding of ECT1-CON 14(17) (pp. 513–516). Krabi

12.

Qin F, Liu D, Sun B et al (2016) Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE 11(12):1–26

13.

Baig Mohammad Md, Naga Srujana R, Jaya Naga Jyothi A et al (2016) Disease identification in plants using k-means clustering and gray scale matrices with svm classifier. Int J Appl Sci, Eng Manage 5(2):84–88

14.

Qin F, Guo J, Lang F (2015) Superpixel segmentation for polarimatric SAR imagery using local iterative clustering. IEEE Geosci Remote Sens Lett 12(1):13–17CrossRef

15.

Achanta R, Shaji A, Smith K et al (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282CrossRef

16.

Chen Q, Yang XB, Chen YH et al (2014) Image segmentation based on superpixels and saliency. Appl Mech Mater 701–702:312–315CrossRef

17.

Che X, Zhang F, Zhang R (2017) Medical image segmentation based on SLIC superpixels model. International Conference on Innovative Optical Health Science. 1024502

18.

Zhaoxia Fu, Wang Liming (2012) Color image segmentation using mixture Gaussian model and EM Algorithm. Commun Comput Inf Sci 346:61–66

19.

Achanta R, Shaji A, Smith K et al (2012) SLIC superpixels compared to state-of the-art superpixel methods. IEEE Trans. Pattern Anal Machine Intell 34(11):2274–2281CrossRef

20.

Bai XD, Cao ZG, Wang Y et al (2013) Crop segmentation from images by morphology modeling in the CIE L*a*b* color space. Comput Electron Agric 99:21–34CrossRef

21.

Wu CFJ (1983) On the convergence properties of the EM Algorithm. Ann Stat 11(1):95–103MathSciNetCrossRefMATH

22.

Einicke GA, Falco G, Malos JT (2010) EM Algorithm state matrix estimation for navigation. IEEE Signal Process Lett 17(5):437–440CrossRef

Titel: Plant disease leaf image segmentation based on superpixel clustering and EM algorithm
verfasst von: Shanwen Zhang
Zhuhong You
Xiaowei Wu
Publikationsdatum: 26.06.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 2/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-3067-8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Sonderheft 2/2019

An experimental demonstration of hybrid fuzzy-fuzzy space-vector control on AC variable speed drives

Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer

Darcy–Forchheimer stretched flow of MHD Maxwell material with heterogeneous and homogeneous reactions

Generalization improvement for regularized least squares classification

The prediction of ultimate pure bending moment of concrete-filled steel tubes by adaptive neuro-fuzzy inference system (ANFIS)

An MLP-based representation of neural tensor networks for the RDF data models