|2/2020 - 11|
A Vision Based Crop Monitoring System Using Segmentation TechniquesKRISHNASWAMY RANGARAJAN, A. , PURUSHOTHAMAN, R.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,807 KB) | Citation | Downloads: 78 | Views: 295|
agricultural engineering, crops, image processing, foldscope, image segmentation
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
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|
| D. S. Gupta and Y. Ibaraki, "Plant Image Analysis Fundamentals and Applications", Boca Raton, US, 2015.
 C. Coresta, "A Scale for coding growth stages in tobacco crops", 2009. [Online] Available: Temporary on-line reference link removed - see the PDF document
 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 306] [SCOPUS Times Cited 367]
 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 254] [SCOPUS Times Cited 274]
 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 19] [SCOPUS Times Cited 24]
 A. C. Eysenberg, S. Seitner, U. Guldener, S. Koemeda, J. Jez, M. Colombini, A. Djamei, "The âPhenoBox', a flexible, automated, open-source plant phenotyping solution", N Phytol., vol. 219, pp. 808-823, 2018.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 14]
 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 dÃ©ficit 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 320] [SCOPUS Times Cited 344]
 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 164] [SCOPUS Times Cited 179]
 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]
 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]
 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 24] [SCOPUS Times Cited 16]
 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 78] [SCOPUS Times Cited 101]
 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 4] [SCOPUS Times Cited 4]
 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]
 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]
 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 31] [SCOPUS Times Cited 40]
 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 12] [SCOPUS Times Cited 12]
 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 17] [SCOPUS Times Cited 20]
 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 27] [SCOPUS Times Cited 33]
 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 13]
 M. V. Giuffrida, M. Minervini, S. A. Tsaftaris, "Learning to count leaves in rosette plants", British Machine Vision Conference, 2015.
 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 51] [SCOPUS Times Cited 59]
 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 14] [SCOPUS Times Cited 15]
 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 23] [SCOPUS Times Cited 27]
 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 20] [SCOPUS Times Cited 32]
 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 4] [SCOPUS Times Cited 8]
 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 123] [SCOPUS Times Cited 137]
 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 49] [SCOPUS Times Cited 57]
Web of Science® Citations for all references: 1,568 TCR
SCOPUS® Citations for all references: 1,813 TCR
Web of Science® Average Citations per reference: 54 ACR
SCOPUS® Average Citations per reference: 63 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 2020-09-24 16:59 in 182 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.
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.