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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


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  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: 124 | Views: 451

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

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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 331] [SCOPUS Times Cited 399]

[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 266] [SCOPUS Times Cited 294]

[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 21] [SCOPUS Times Cited 27]

[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 14] [SCOPUS Times Cited 16]

[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 320] [SCOPUS Times Cited 353]

[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 164] [SCOPUS Times Cited 182]

[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 26] [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 88] [SCOPUS Times Cited 107]

[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 5] [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 32] [SCOPUS Times Cited 42]

[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 15] [SCOPUS Times Cited 15]

[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 22] [SCOPUS Times Cited 23]

[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 31] [SCOPUS Times Cited 39]

[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 17]

[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 62] [SCOPUS Times Cited 73]

[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 24] [SCOPUS Times Cited 29]

[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 24] [SCOPUS Times Cited 40]

[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 4] [SCOPUS Times Cited 12]

[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 134] [SCOPUS Times Cited 149]

[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 65] [SCOPUS Times Cited 73]

References Weight

Web of Science® Citations for all references: 1,679 TCR
SCOPUS® Citations for all references: 1,967 TCR

Web of Science® Average Citations per reference: 58 ACR
SCOPUS® Average Citations per reference: 68 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 2021-01-14 00:56 in 165 seconds.

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