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Leaf Disease Diagnosis using Online and Batch Backpropagation neural network

A.T. Sapkal1 , U.V. Kulkarni2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 362-366, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.362366

Online published on Jun 30, 2018

Copyright © A.T. Sapkal, U.V. Kulkarni . 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: A.T. Sapkal, U.V. Kulkarni, “Leaf Disease Diagnosis using Online and Batch Backpropagation neural network,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.362-366, 2018.

MLA Style Citation: A.T. Sapkal, U.V. Kulkarni "Leaf Disease Diagnosis using Online and Batch Backpropagation neural network." International Journal of Computer Sciences and Engineering 6.6 (2018): 362-366.

APA Style Citation: A.T. Sapkal, U.V. Kulkarni, (2018). Leaf Disease Diagnosis using Online and Batch Backpropagation neural network. International Journal of Computer Sciences and Engineering, 6(6), 362-366.

BibTex Style Citation:
@article{Sapkal_2018,
author = {A.T. Sapkal, U.V. Kulkarni},
title = {Leaf Disease Diagnosis using Online and Batch Backpropagation neural network},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2018},
volume = {6},
Issue = {6},
month = {6},
year = {2018},
issn = {2347-2693},
pages = {362-366},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2188},
doi = {https://doi.org/10.26438/ijcse/v6i6.362366}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i6.362366}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2188
TI - Leaf Disease Diagnosis using Online and Batch Backpropagation neural network
T2 - International Journal of Computer Sciences and Engineering
AU - A.T. Sapkal, U.V. Kulkarni
PY - 2018
DA - 2018/06/30
PB - IJCSE, Indore, INDIA
SP - 362-366
IS - 6
VL - 6
SN - 2347-2693
ER -

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Abstract

Productivity of the crops is affected due to diseases. Traditional disease diagnosis system is very time consuming in which pathologist carried out experimentation in the laboratory. Hence it is needed to produce the system which diagnosis the disease accurately and fast with the help of the technology. Identifying disease of crops in its early stage is a major challenge in front of researchers. Many machine learning algorithms and image processing techniques are applied to efficiently identify the disease based on the symptoms that appeared on the leaves. In this paper, the infected leaf is segmented using the Kmeans clustering algorithm and further the 12 texture features are extracted from the segmented image. The backpropagation (BP) algorithm is used for identifying the disease. Here two versions of the backpropagation i.e. online BP and batch BP are used. The Pomegranate infected leaf image database is used for the experimentation purpose. It is observed that online BP performance is better as compared to the batch BP.

Key-Words / Index Term

Leaf disease, Agriculture,Backpropagation neural network

References

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