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Optimized fuzzy neural network for Jatropha Curcas plant disease identification

  • Diny Melsye Nurul Fajri
  • , Triando Hamonangan Saragih
  • , Andi Hamdianah
  • , Wayan Firdaus Mahmudy
  • , Yusuf Priyo Anggodo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Jatropha curcas is an important commodity for farmers. The farmers must be aware of the disease caused by pest or virus for the existence and benefits of this plant. The main obstacle is the lack of farmers' knowledge about diseases and a system that utilize plant expert knowledge is needed. This paper proposes Fuzzy Neural Network (FNN) method to identify Jatropha Curcas Disease. To achieve higher accuracy, simulated annealing (SA) is employed to adjust the boundary of membership functions of the FNN. Computational experiments prove that the proposed method produces promising result and the SA is effective to improve the accuracy of the FNS.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages297-304
Number of pages8
ISBN (Electronic)9781538621820
DOIs
Publication statusPublished - 2 Jul 2017
Event2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017 - Batu City, Indonesia
Duration: 24 Nov 201725 Nov 2017

Publication series

NameProceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017
Volume2018-January

Conference

Conference2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017
Country/TerritoryIndonesia
CityBatu City
Period24/11/1725/11/17

Keywords

  • Fuzzy Neural Network
  • Identification
  • Jatropha Curcas
  • Optimization
  • Simulated Annealing

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