CFP last date
01 January 2025
Call for Paper
February Edition
CAE solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 01 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Property (Land) Registration Management Using Blockchain in Nigeria

A. Festus Osuolale Ojebiyi David-Daniel Seriki Oluwasogo
Random Articles
Reseach Article

Adaptive Neuro Fuzzy Inference System based Tea Leaf Disease Recognition using Color Wavelet Features

by Mustain Billah, Mohammad Badrul Alam Miah, Abu Hanifa, Md. Ruhul Amin
journal cover thumbnail

Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 5
Year of Publication: 2015
Authors: Mustain Billah, Mohammad Badrul Alam Miah, Abu Hanifa, Md. Ruhul Amin
10.5120/cae2015651943

Mustain Billah, Mohammad Badrul Alam Miah, Abu Hanifa, Md. Ruhul Amin . Adaptive Neuro Fuzzy Inference System based Tea Leaf Disease Recognition using Color Wavelet Features. Communications on Applied Electronics. 3, 5 ( November 2015), 1-4. DOI=10.5120/cae2015651943

@article{ 10.5120/cae2015651943,
author = { Mustain Billah, Mohammad Badrul Alam Miah, Abu Hanifa, Md. Ruhul Amin },
title = { Adaptive Neuro Fuzzy Inference System based Tea Leaf Disease Recognition using Color Wavelet Features },
journal = { Communications on Applied Electronics },
issue_date = { November 2015 },
volume = { 3 },
number = { 5 },
month = { November },
year = { 2015 },
issn = { 2394-4714 },
pages = { 1-4 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume3/number5/459-2015651943/ },
doi = { 10.5120/cae2015651943 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:43:28.863932+05:30
%A Mustain Billah
%A Mohammad Badrul Alam Miah
%A Abu Hanifa
%A Md. Ruhul Amin
%T Adaptive Neuro Fuzzy Inference System based Tea Leaf Disease Recognition using Color Wavelet Features
%J Communications on Applied Electronics
%@ 2394-4714
%V 3
%N 5
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Tea, a favourite bevarage in the world is made of tender new leaves of tea plant. So tea plantation is more concerned with tea leaf diseases. Infected leaves may be the source of disease for new leaves reducing the productivity of the plant. In order to reduce tea leaf disease, disease recognition is the initial step. Many techniques have been used for leaf recognition. In this paper, we have proposed a model for recognising tea leaf diseases, which uses color wavelet features and adaptive neuro fuzzy inference system (ANFIS). After processing the images, color wavelet features are extracted and provided to Adaptive Neuro Fuzzy Inference System (ANFIS) along with the disease types. This ANFIS based tea leaf disease recognition system using color wavelet features can recognise the disease of any new leaf image affected by disease accurately.

References
  1. Tinku Acharya, Lina J. Karam, and Francescomaria Marino. Compression of color images based on a 2- dimensional discrete wavelet transform yielding a perceptually lossless image. Google Patents. US Patent 6,154,493.
  2. MS Prasad Babu and B. Srinivasa Rao. Leaves recognition using back propagation neural network-advice for pest and disease control on crops.
  3. Andrea Baraldi and Flavio Parmiggiani. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. 33(2):293–304.
  4. Piotr Dollr, Zhuowen Tu, Pietro Perona, and Serge Belongie. Integral channel features. In BMVC, volume 2, page 5.
  5. Ajay A. Gurjar and Viraj A. Gulhane. Disease detection on cotton leaves by eigenfeature regularization and extraction.
  6. Stavros Karkanis, Dimitris K. Iakovidis, Dimitris E. Maroulis, Dimitris Karras, M. Tzivras, and others. Computer-aided tumor detection in endoscopic video using color wavelet features. 7(3):141–152.
  7. Rakesh Kaundal, Amar S. Kapoor, and Gajendra PS Raghava. Machine learning techniques in disease forecasting: a case study on rice blast prediction. 7(1):485.
  8. George Klir and Bo Yuan. Fuzzy sets and fuzzy logic, volume 4. Prentice Hall New Jersey.
  9. George J. Klir and Bo Yuan. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A. Zadeh. World Scientific Publishing Co., Inc.
  10. Topi Menp and Matti Pietikinen. Classification with color and texture: jointly or separately? 37(8):1629–1640.
  11. Christoph Palm. Color texture classification by integrative co-occurrence matrices. 37(5):965–976.
  12. George Paschos. Perceptually uniform color spaces for color texture analysis: an empirical evaluation. 10(6):932– 937.
  13. Santanu Phadik and Jaya Sil. Rice disease identification using pattern recognition techniques. In Computer and Information Technology, 2008. ICCIT 2008. 11th International Conference on, pages 420–423. IEEE.
  14. Abdulkadir Sengur.Wavelet transform and adaptive neurofuzzy inference system for color texture classification. 34(3):2120–2128.
  15. Alina Surmacka Szczesniak. Classification of textural characteristics. 28(4):385–389.
  16. Yuan Tian, Chunjiang Zhao, Shenglian Lu, and Xinyu Guo. SVM-based multiple classifier system for recognition of wheat leaf diseases. In World Automation Congress (WAC), 2012, pages 189–193. IEEE.
  17. Panagiotis Tzionas, Stelios E. Papadakis, and Dimitris Manolakis. Plant leaves classification based on morphological features and a fuzzy surface selection technique. In Fifth International Conference on Technology and Automation, Thessaloniki, Greece, pages 365–370.
  18. Gert Van de Wouwer, Paul Scheunders, Stefan Livens, and Dirk Van Dyck. Wavelet correlation signatures for color texture characterization. 32(3):443–451.
  19. Gert Van deWouwer, Paul Scheunders, and Dirk Van Dyck. Statistical texture characterization from discrete wavelet representations. 8(4):592–598.
Index Terms

Computer Science
Information Sciences

Keywords

Image processing ANFIS Tea leaf disease disease recognition

Powered by PhDFocusTM