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

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
 
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Download PDF pdficon (1,807 KB) | Citation | Downloads: 867 | Views: 2,024

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

Abstract
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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

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References Weight

Web of Science® Citations for all references: 2,845 TCR
SCOPUS® Citations for all references: 3,467 TCR

Web of Science® Average Citations per reference: 98 ACR
SCOPUS® Average Citations per reference: 120 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 2024-04-20 05:17 in 150 seconds.




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