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Agriculture plant leaf Disease detection using Spatial Gray-level Dependence Matrices

S. Malini1 , T. Venkadeswari2 , T. Ratha Jeyalakshmi3

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-16 , Page no. 43-46, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si16.4346

Online published on May 18, 2019

Copyright © S. Malini, T. Venkadeswari, T. Ratha Jeyalakshmi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S. Malini, T. Venkadeswari, T. Ratha Jeyalakshmi, “Agriculture plant leaf Disease detection using Spatial Gray-level Dependence Matrices,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.43-46, 2019.

MLA Style Citation: S. Malini, T. Venkadeswari, T. Ratha Jeyalakshmi "Agriculture plant leaf Disease detection using Spatial Gray-level Dependence Matrices." International Journal of Computer Sciences and Engineering 07.16 (2019): 43-46.

APA Style Citation: S. Malini, T. Venkadeswari, T. Ratha Jeyalakshmi, (2019). Agriculture plant leaf Disease detection using Spatial Gray-level Dependence Matrices. International Journal of Computer Sciences and Engineering, 07(16), 43-46.

BibTex Style Citation:
@article{Malini_2019,
author = {S. Malini, T. Venkadeswari, T. Ratha Jeyalakshmi},
title = {Agriculture plant leaf Disease detection using Spatial Gray-level Dependence Matrices},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {16},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {43-46},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1275},
doi = {https://doi.org/10.26438/ijcse/v7i16.4346}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i16.4346}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1275
TI - Agriculture plant leaf Disease detection using Spatial Gray-level Dependence Matrices
T2 - International Journal of Computer Sciences and Engineering
AU - S. Malini, T. Venkadeswari, T. Ratha Jeyalakshmi
PY - 2019
DA - 2019/05/18
PB - IJCSE, Indore, INDIA
SP - 43-46
IS - 16
VL - 07
SN - 2347-2693
ER -

           

Abstract

Agriculture is one of the main central divisions of Indian Providence. Indian agriculture segments more than 18 per cent of India’s household product further it helps to provide employment opportunities to 50% of the countries workforce. Fungus, viruses and germs are the main causes that affect the plant leaves. Currently crops face many types of diseases. Damage by the pest is main trait. If good concern is not done in the plants it may lead to grave outcome on vegetation. Discovery of plant illness along with the other routine method in monitoring the plants reduces the drudgery as it ascertains the injection of plants at early stage. Image processing provides better techniques about disease identification. The process has four major processes, initially the color image is transformed to RGB, and then the RGB image is then transferred to HSV for shade generation. After that the green pixels are obtained from the color generation process. The image is analyzed, segmented meaningfully and the texture features are extracted and evaluated from the SGDM matrices.

Key-Words / Index Term

HSI, RGB, Texture, SGDM, Texture, Image

References

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