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Analysis of various Plant Disease detection Techniques

Gazzal Thukral1 , Lal Chand2

Section:Review Paper, Product Type: Journal Paper
Volume-7 , Issue-7 , Page no. 308-311, Jul-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i7.308311

Online published on Jul 31, 2019

Copyright © Gazzal Thukral, Lal Chand . 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: Gazzal Thukral, Lal Chand, “Analysis of various Plant Disease detection Techniques,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.308-311, 2019.

MLA Style Citation: Gazzal Thukral, Lal Chand "Analysis of various Plant Disease detection Techniques." International Journal of Computer Sciences and Engineering 7.7 (2019): 308-311.

APA Style Citation: Gazzal Thukral, Lal Chand, (2019). Analysis of various Plant Disease detection Techniques. International Journal of Computer Sciences and Engineering, 7(7), 308-311.

BibTex Style Citation:
@article{Thukral_2019,
author = {Gazzal Thukral, Lal Chand},
title = {Analysis of various Plant Disease detection Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2019},
volume = {7},
Issue = {7},
month = {7},
year = {2019},
issn = {2347-2693},
pages = {308-311},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4766},
doi = {https://doi.org/10.26438/ijcse/v7i7.308311}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i7.308311}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4766
TI - Analysis of various Plant Disease detection Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - Gazzal Thukral, Lal Chand
PY - 2019
DA - 2019/07/31
PB - IJCSE, Indore, INDIA
SP - 308-311
IS - 7
VL - 7
SN - 2347-2693
ER -

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Abstract

The plant disease detection is the approach which is applied to predict disease type from the input image. The plant disease detection has the two phases which are feature extraction and classification. In the previous years, various techniques has been designed for the plant disease detection. The various classifications methods has been designed for the plant disease detection like SVM, decision etc. In this paper, various plant disease detection techniques are reviewed and analyzed in terms of certain parameters

Key-Words / Index Term

Plant Disease detection, SVM, Classification, Feature extraction

References

[1] Ghaiwat Savita N, Arora Parul, “Detection and classification of plant leaf diseases using image processing techniques: a review”, Int J Recent Adv Eng Technol 2014;2(3):2347–812
[2] Dhaygude Sanjay B, Kumbhar Nitin P., “Agricultural plant leaf disease detection using image processing”, Int J Adv Res Electr Electron Instrum Eng 2013;2(1).
[3] Mrunalini R Badnakhe, Deshmukh Prashant R, “An application of K-means clustering and artificial intelligence in pattern recognition for crop diseases”, Int Conf Adv Inf Technol 2011;20. 2011 IPCSIT.
[4] S. B. Lo, H. Chan, J. Lin, H. Li, M. T. Freedman, and S. K. Mun, “Artificial convolution neural network for medical image pattern recognition,” Neural Networks, vol. 8, no. 7-8, pp. 1201–1214, 1995.
[5] O. D. Trier, A. K. Jain, and T. Taxt, “Feature extraction methods for character recognition-a survey,” Pattern Recognition, vol. 29, no. 4, pp. 641–662, 1996.
[6] G. P. Zhang, “Neural networks for classification: a survey,” IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 30, no. 4, pp. 451–462, 2000.
[7] Bashir Sabah, Sharma Navdeep, “Remote area plant disease detection using image processing”, IOSR J Electron Commun Eng 2012;2(6):31–4. ISSN: 2278-2834.
[8] Naikwadi Smita, Amoda Niket, “Advances in image processing for detection of plant diseases”, Int J Appl Innov Eng Manage 2013;2(11).
[9] Patil Sanjay B et al., “Leaf disease severity measurement using image processing”, Int J Eng Technol 2011;3(5):297–301.
[10] Chaudhary Piyush et al., “Color transform based approach for disease spot detection on plant leaf”, Int Comput Sci Telecommun 2012;3(6).
[11] Vijai Singh, A.K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques”, Information Processing in Agriculture 4 (2017) 41 – 4 9
[12] Shunping Ji, Chi Zhang, Anjian Xu, Yun Shi and Yulin Duan, “3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images”, Remote Sens. 2018, 10, 75
[13] Tom Brosch, Lisa Y.W. Tang, Youngjin Yoo, David K.B. Li, Anthony Traboulsee, and Roger Tam, “Deep 3D Convolutional Encoder Networks with Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation”, 2016, IEEE
[14] Guillermo L. Grinblat, Lucas C. Uzal, Mónica G. Larese, Pablo M. Granitto, “Deep learning for plant identification using vein morphological patterns”, Computers and Electronics in Agriculture 127 (2016) 418–424
[15] Jayme Garcia Arnal Barbedo, Luciano Vieira Koenigkan, Thiago Teixeira Santos, “Identifying multiple plant diseases using digital image processing”, Biosystem Engineering 147, 2016, 104-116
[16] Mads Dyrmann, Henrik Karstoft, Henrik Skov Midtiby, “Plant species classification using deep convolutional neural network”, Biosystems Engineering 151, 2016, 72-80
[17] Jiang Lu, Jie Hu, Guannan Zhao, Fenghua Mei, Changshui Zhang, “An in-field automatic wheat disease diagnosis system”, Computers and Electronics in Agriculture 142 (2017) 369–379
[18] Halimatu Sadiyah Abdullahi, Ray E. Sheriff, Fatima Mahieddine, “Convolution Neural Network in Precision Agriculture for Plant Image Recognition and Classification”, 2017, IEEE
[19] Tisen Huang, Rui Yang, Wenshan Huang, Yiqi Huanga, Xi Qiao, “Detecting Sugarcane Borer Diseases Using Support Vector Machine”, 2017, Information Processing in Agriculture
[20] Sue Han Lee, Chee Seng Chan, Simon Joseph Mayo, Paolo Remagnino, “How deep learning extracts and learns leaf features for plant classification”, Pattern Recognition 71 (2017) 1–13