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FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 58 days
Avg accept to publ: 60 days
APC: 300 EUR


PUBLISHER

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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Application of the Voltage Control Technique and MPPT of Stand-alone PV System with Storage, HIVZIEFENDIC, J., VUIC, L., LALE, S., SARIC, M.
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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

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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: 891 | Views: 2,090

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.


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Cited-By SCOPUS

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Cited-By CrossRef

[1] An automated solid waste detection using the optimized YOLO model for riverine management, Zailan, Nur Athirah, Azizan, Muhammad Mokhzaini, Hasikin, Khairunnisa, Mohd Khairuddin, Anis Salwa, Khairuddin, Uswah, Frontiers in Public Health, ISSN 2296-2565, Issue , 2022.
Digital Object Identifier: 10.3389/fpubh.2022.907280
[CrossRef]

[2] An Image Analysis of River-Floating Waste Materials by Using Deep Learning Techniques, Nunkhaw, Maiyatat, Miyamoto, Hitoshi, Water, ISSN 2073-4441, Issue 10, Volume 16, 2024.
Digital Object Identifier: 10.3390/w16101373
[CrossRef]

[3] Deep learning implementation of image segmentation in agricultural applications: a comprehensive review, Lei, Lian, Yang, Qiliang, Yang, Ling, Shen, Tao, Wang, Ruoxi, Fu, Chengbiao, Artificial Intelligence Review, ISSN 1573-7462, Issue 6, Volume 57, 2024.
Digital Object Identifier: 10.1007/s10462-024-10775-6
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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