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Reseach Article

Tea Leaf Diseases Recognition using Neural Network Ensemble

by Bikash Chandra Karmokar, Mohammad Samawat Ullah, Md. Kibria Siddiquee, Kazi Md. Rokibul Alam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 114 - Number 17
Year of Publication: 2015
Authors: Bikash Chandra Karmokar, Mohammad Samawat Ullah, Md. Kibria Siddiquee, Kazi Md. Rokibul Alam
10.5120/20071-1993

Bikash Chandra Karmokar, Mohammad Samawat Ullah, Md. Kibria Siddiquee, Kazi Md. Rokibul Alam . Tea Leaf Diseases Recognition using Neural Network Ensemble. International Journal of Computer Applications. 114, 17 ( March 2015), 27-30. DOI=10.5120/20071-1993

@article{ 10.5120/20071-1993,
author = { Bikash Chandra Karmokar, Mohammad Samawat Ullah, Md. Kibria Siddiquee, Kazi Md. Rokibul Alam },
title = { Tea Leaf Diseases Recognition using Neural Network Ensemble },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 114 },
number = { 17 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 27-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume114/number17/20071-1993/ },
doi = { 10.5120/20071-1993 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:53:02.900914+05:30
%A Bikash Chandra Karmokar
%A Mohammad Samawat Ullah
%A Md. Kibria Siddiquee
%A Kazi Md. Rokibul Alam
%T Tea Leaf Diseases Recognition using Neural Network Ensemble
%J International Journal of Computer Applications
%@ 0975-8887
%V 114
%N 17
%P 27-30
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a tea leaf diseases recognizer (TLDR), an initiative to recognize diseases of the tea leaf. In TLDR, at first the image of the tea leaf is cropped, resized and converted to its threshold value in the image processing. Then feature extraction method is applied. Neural Network Ensemble (NNE) was used for pattern recognition. The extracted features are passed to the ANN along with the disease type and the ANN is trained. When a new image is uploaded into the system the most suitable match is found and the disease is returned. After going through the testing process 91 % of accuracy was found. The proposed solution would support the tea industry of Bangladesh to grow in the global market and also increase its tea production by minimizing the effect of tea leaf diseases.

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Index Terms

Computer Science
Information Sciences

Keywords

Negative correlation learning Feature Extraction Image Processing Tea leaf diseases.