Click to open the HelpDesk interface
AECE - Front page banner



JCR Impact Factor: 1.102
JCR 5-Year IF: 0.734
Issues per year: 4
Current issue: Feb 2021
Next issue: May 2021
Avg review time: 53 days


Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


1,605,903 unique visits
Since November 1, 2009

Robots online now


SCImago Journal & Country Rank


Anycast DNS Hosting

 Volume 21 (2021)
     »   Issue 1 / 2021
 Volume 20 (2020)
     »   Issue 4 / 2020
     »   Issue 3 / 2020
     »   Issue 2 / 2020
     »   Issue 1 / 2020
 Volume 19 (2019)
     »   Issue 4 / 2019
     »   Issue 3 / 2019
     »   Issue 2 / 2019
     »   Issue 1 / 2019
 Volume 18 (2018)
     »   Issue 4 / 2018
     »   Issue 3 / 2018
     »   Issue 2 / 2018
     »   Issue 1 / 2018
 Volume 17 (2017)
     »   Issue 4 / 2017
     »   Issue 3 / 2017
     »   Issue 2 / 2017
     »   Issue 1 / 2017
  View all issues  


Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
Issue 2/2018



Release of the v3 version of AECE Journal website. We moved to a new server and implemented the latest cryptographic protocols to assure better compatibility with the most recent browsers. Our website accepts now only TLS 1.2 and TLS 1.3 secure connections.

Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

Starting on the 15th of December 2019 all paper authors are required to enter their SCOPUS IDs. You may use the free SCOPUS ID lookup form to find yours in case you don't remember it.

Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

Read More »


  2/2020 - 11

A Vision Based Crop Monitoring System Using Segmentation Techniques

KRISHNASWAMY RANGARAJAN, A. See more information about KRISHNASWAMY RANGARAJAN, A. on SCOPUS See more information about KRISHNASWAMY RANGARAJAN, A. on IEEExplore See more information about KRISHNASWAMY RANGARAJAN, A. on Web of Science, PURUSHOTHAMAN, R. See more information about PURUSHOTHAMAN, R. on SCOPUS See more information about PURUSHOTHAMAN, R. on SCOPUS See more information about PURUSHOTHAMAN, R. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,807 KB) | Citation | Downloads: 158 | Views: 587

Author keywords
agricultural engineering, crops, image processing, foldscope, image segmentation

References keywords
plant(21), phenotyping(10), vision(7), rosette(6), plants(6), leaf(6), tsaftaris(5), segmentation(5), detection(4), arabidopsis(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 89 - 100
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02011
Web of Science Accession Number: 000537943500011
SCOPUS ID: 85087448073

Quick view
Full text preview
The characterization of health status for a plant using a non-destructive method is one of the challenging problems. In this study, the number of leaves and discoloration properties have been estimated using the images obtained from nine saplings of Solanum melongena (eggplant or brinjal) grown in the laboratory. The images were obtained using a mobile phone camera fitted on an automated device. A particle wave algorithm and contour grow technique was used for the segmentation of leaves which resulted in a segmentation accuracy of 89%. The defective percentage was estimated based on which saplings were ranked. Validation of healthy and defective regions was done by applying linear regression analysis on the estimated Normalized Green Red Difference Index (NGRDI) from images obtained using an automated device and a Foldscope (new paper-based microscope). The analysis resulted in R squared value and Least Mean Square Error (LMSE) of 0.86 and 0.1 respectively.

References | Cited By  «-- Click to see who has cited this paper

[1] D. S. Gupta and Y. Ibaraki, "Plant Image Analysis Fundamentals and Applications", Boca Raton, US, 2015.

[2] C. Coresta, "A Scale for coding growth stages in tobacco crops", 2009. [Online] Available: Temporary on-line reference link removed - see the PDF document

[3] L. Li, Q. Zhang, D. Huang, "A review of imaging techniques for plant phenotyping", Sens, vol.14, pp. 20078-20111, 2014.
[CrossRef] [Web of Science Times Cited 362] [SCOPUS Times Cited 423]

[4] N. Fahlgren, M. A. Gehan, I. Baxter, "Lights, camera, action: high-throughput plant phenotyping is ready for a close-up", Curr. Opin. Plant Biol., vol. 24, pp. 93-99, 2015.
[CrossRef] [Web of Science Times Cited 285] [SCOPUS Times Cited 308]

[5] K. R. Aravind, P. Raja, M. Perez-Ruiz, "Task-based agricultural mobile robots in arable farming: A review", Span. J. Agric. Res., vol. 15, no. 1, Article ID e02R01, 2017.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 29]

[6] A. C. Eysenberg, S. Seitner, U. Guldener, S. Koemeda, J. Jez, M. Colombini, A. Djamei, "The ‘PhenoBox', a flexible, automated, open-source plant phenotyping solution", N Phytol., vol. 219, pp. 808-823, 2018.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 18]

[7] C. Granier, L. Aguirrezabal, K. Chenu, S. J. Cookson, M. Dauzat, P. Hamrad, J. J. Thioux, G. Rolland, S. Bouchier-Combaud, A. Lebaudy, B. Muller, T. Simonneau, F. Tardieu, "PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water déficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit", N Phytol., vol. 169, no. 3, pp. 623-635, 2006.
[CrossRef] [Web of Science Times Cited 323] [SCOPUS Times Cited 358]

[8] M. Jansen, F. Gilmer, B. Biskup, K. A. Nagel, U. Rascher, A. Fischbach, S. Briem, G. Dreissen, S. Tittmann, S. Braun, I. D. Jaeger, M. Metzlaff, U. Schurr, H. Scharr, A. Walter, "Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants", Funct. Plant Biol., vol. 36, pp. 902-914, 2009.
[CrossRef] [Web of Science Times Cited 168] [SCOPUS Times Cited 187]

[9] M. Augustin, Y. Haxhimusa, W. Busch, W. G. Kropatsch, "Image-based phenotyping of the mature Arabidopsis shoot system", Computer Vision - ECCV 2014 Workshops, pp. 231-246, 2014.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 8]

[10] I. Janusch, W. G. Kropatsch, W. Busch, D. Ristova, "Representing Roots on the Basis of Reeb Graphs in Plant Phenotyping", Computer Vision - ECCV 2014 Workshops, pp. 75-88, 2014.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]

[11] J. M. Pape, and C. Klukas, "3-D Histogram-based segmentation and leaf detection for Rosette plants", Computer Vision - ECCV 2014 Workshops, Zurich, Switzerland, pp. 61-74, 2014.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 17]

[12] H. Scharr, M. Minervini, A. P. French, C. Klukas, D. M. Kramer, X. Liu, I. Luengo, J. M. Pape, G. Polder, D. Vukadinovic, X. Yin, S. A. Tsaftaris, "Leaf segmentation in plant phenotyping: a collation study", Mach. Vis. Appl., vol. 27, pp. 585-606, 2016.
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 118]

[13] M. M. Linow, J. Wilhelm, C. Briese, T. Wojciechwoski, U. Schurr, F. Fiorani, "Plant screen mobile: an open-source mobile device app for plant trait analysis", Plant Methods., vol. 15, no. 2, 2019.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 5]

[14] R. Ispriyan, I. Grigoriev, W. Z. Castell, A. R. Schaffner, "A segmentation procedure using color features applied to images of Arabidopsis thaliana", Funct. Plant Biol., vol. 40, pp. 1065-1075, 2013.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 9]

[15] X. Yin, X. Liu, J. Chen, D. M. Kramer, "Multi-leaf alignment from fluorescence plant images", IEEE Winter Conference on Application of Computer Vision, pp. 437-444, 2014.
[CrossRef] [SCOPUS Times Cited 18]

[16] C. Xia, L. Wang, B. K. Chung, J. M. Lee, "In situ 3D segmentation of individual plant leaves using a RGB-D camera for agricultural automation", Sens.,vol. 15, pp. 20463-20479, 2015.
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 44]

[17] A. Dobrescu, L. C. T. Scorza, S. A. Tsaftaris, A. J. McCormick, "A "Do-It-Yourself" phenotyping system: measuring growth and morphology throughout the diel cycle in rosette shaped plants", Plant Method., vol.13, Article ID. 95, 2017.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 16]

[18] A. Dobrescu, M. V. Giuffrida, S. A. Tsaftaris, "Leveraging multiple datasets for deep leaf counting", IEEE International Conference on Computer Vision Workshop, pp. 4321-4328, 2017.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 28]

[19] M. Minervini, M. V. Giffrida, P. Perata, S. A. Tsaftaris, "Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-plants", Plant J., vol. 90, pp. 204-216, 2017.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 40]

[20] P. Sodhi, S. Vijayarangan, D. Wettergreen, "In-field segmentation and identification of plant structures using 3D imaging", IEEE International Conference on Intelligent Robots and Systems, 2017.
[CrossRef] [SCOPUS Times Cited 21]

[21] M. V. Giuffrida, M. Minervini, S. A. Tsaftaris, "Learning to count leaves in rosette plants", British Machine Vision Conference, 2015.

[22] J. Ubbens, M. Cieslak, P. Prusinkiewicz, I. Stavness, "The use of plant models in deep learning: an application to leaf counting in rosette plants", Plant Methods., vol. 14, no. 6, 2018.
[CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 81]

[23] K. A. Vakilian, J. Massah, "A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops", Comput. Electron. Agric., vol. 139, pp. 153-163, 2017.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 18]

[24] D. Story, M. Kacira, "Design and implementation of a computer vision-guided greenhouse crop diagnostics system", Mach. Vis. Appl., vol. 26, pp. 496-506, 2015.
[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 30]

[25] N. Schor, A. Bechar, T. Ignat, A. Dombrovsky, Y. Elad, S. Bermann, "Robotic disease detection in greenhouses: Combined detection of powdery mildew and tomato spotted wilt virus", IEEE Robot. Autom. Lett., vol. 1, no. 1, pp. 354-360, 2016.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 45]

[26] E. Kiani, T. Mamedov, "Identification of plant disease infection using soft-computing application to modern botany", Proced. Comput. Sci., vol. 120, pp. 893-900, 2017.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 14]

[27] J. S. Cybulski, J. Clements, M. Prakash, "Foldscope: Origami-based paper microscope", PLoS ONE, vol. 9, no. 6, Article ID e98781, 2014.
[CrossRef] [Web of Science Times Cited 142] [SCOPUS Times Cited 155]

[28] K. Prabhakara, W. D. Hively, G. W. McCarty, "Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States", Int. J. Appl. Earth Obs. Geoinf., vol. 39, pp. 88-102, 2015.
[CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 87]

References Weight

Web of Science® Citations for all references: 1,784 TCR
SCOPUS® Citations for all references: 2,079 TCR

Web of Science® Average Citations per reference: 62 ACR
SCOPUS® Average Citations per reference: 72 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-04-17 23:08 in 160 seconds.

Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2021
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.

Website loading speed and performance optimization powered by: