CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Review of Fuzzy Rule Promotion Techniques in Agriculture Information System

Published on October 2011 by Lokesh Jain, Harish Kumar, R. K. Singla
journal_cover_thumbnail

IP Multimedia Communications
Foundation of Computer Science USA
IPMC - Number 1
October 2011
Authors: Lokesh Jain, Harish Kumar, R. K. Singla
713525f9-b955-4ddb-88a8-bba432a9bc11

Lokesh Jain, Harish Kumar, R. K. Singla . A Review of Fuzzy Rule Promotion Techniques in Agriculture Information System. IP Multimedia Communications. IPMC, 1 (October 2011), 55-60.

@article{
author = { Lokesh Jain, Harish Kumar, R. K. Singla },
title = { A Review of Fuzzy Rule Promotion Techniques in Agriculture Information System },
journal = { IP Multimedia Communications },
issue_date = { October 2011 },
volume = { IPMC },
number = { 1 },
month = { October },
year = { 2011 },
issn = 0975-8887,
pages = { 55-60 },
numpages = 6,
url = { /specialissues/ipmc/number1/3749-ipmc013/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 IP Multimedia Communications
%A Lokesh Jain
%A Harish Kumar
%A R. K. Singla
%T A Review of Fuzzy Rule Promotion Techniques in Agriculture Information System
%J IP Multimedia Communications
%@ 0975-8887
%V IPMC
%N 1
%P 55-60
%D 2011
%I International Journal of Computer Applications
Abstract

Integration of soft computing techniques in the development of agricultural expert information systems, decision support systems etc. to predict the response of the agricultural output parameters with reference to the input information to the system has helped a lot of farm stakeholders where the expertise is not available. One of the soft computing techniques is fuzzy logic. This paper provides the review of the fuzzy rule promotion methodology as applied to oilseeds diseases diagnosis system. The methodology of the system has been discussed and drawbacks in the web based intelligent diseases diagnosis system and the rule promotion methodology has also been presented.

References
  1. Information System, http://en.wikipedia.org/wiki/Information_systems. Last seen on June 15, 2011.
  2. Information and communication technologies, http://en.wikipedia.org/ wiki/Information_and_communication_technologies. Last seen on June 15, 2011.
  3. ICT in Agriculture, http://en.wikipedia.org/wiki/ICT_in_agriculture. Last seen on June 15, 2011.
  4. Juan Jose Gonzalez de la Rosa, Agustin Aguera Perez, Jose Carlos Palomares Salas, Jose Gabriel Ramiro Leo and Antonio Moreno Muñoz, “A novel inference method for local wind conditions using genetic fuzzy systems”, Journal of Renewable Energy, Volume 36, 2011, pp 1747-1753.
  5. Konstantinos P. Ferentinos and Theodore A. Tsiligiridis, “Adaptive design optimization of wireless sensor networks using genetic algorithms”, Journal of Computer Networks Volume 51, 2007, pp 1031–1051.
  6. Milad Fathi, Mohebbat Mohebbi and Seyed Mohammad Ali Razavi, “Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit”, Journal of Food Bioprocess Technology, DOI 10.1007/s11947-009-0222-y.
  7. A. Moghaddamnia , M. Ghafari Gousheh, J. Piri, S. Amin and D. Han, “Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques”, Journal of Advances in Water Resources, Volume 32, 2009, pp 88–97.
  8. Zadeh LA, “Fuzzy Sets”, Journal of Information Control, Volume 8, 1965, pp 338–353.
  9. Ghiaus C, “Fuzzy model and control of a fan-coil”, Journal of Energy and Buildings, Voulme 33, 2001, pp 545–551
  10. Jantzen J, “Tuning of a Fuzzy PID Controller”, Technical Report no 98-H 871 (fpid), Sep. 30, 1998, Technical University of Denmark, Department of Automation, Lyngby, Denmark.
  11. Rumelhart, D.E. and McClelland, J.L., “Parallel Distributed Processing: Explorations in the Microstructures of Cognition”, vol. I., 1986, MIT Press, Cambridge, MA.
  12. T.V. Reshmidevi, T.I. Eldho and R. Jana, “A GIS-integrated fuzzy rule-based inference system for land suitability evaluation in agricultural watersheds”, Jounal of Agricultural Systems, Volume 101, 2009, pp 101–109.
  13. P. Scherer, K. Lehmann, O. Schmidt and B. Demirel, “Application of a Fuzzy Logic Control System for Continuous Anaerobic Digestion of Low Buffered, Acidic Energy Crops as Mono-Substrate”, Journal of Biotechnology and Bioengineering, Volume 102, Issue 3, February 15, 2009, pp 736-748.
  14. P. Javadi Kia, A. Tabatabaee Far, M. Omid, R. Alimardani and L. Naderloo, “Intelligent Control Based Fuzzy Logic for Automation of Greenhouse Irrigation System and Evaluation in Relation to Conventional Systems”, World Applied Sciences Journal, volume 6, Issue 1, 2009, pp 16-23.
  15. Elpiniki I. Papageorgiou, Athanasios Markinos and Theofanis Gemptos, “Application of fuzzy cognitive maps for cotton yield management in precision farming”, Journal of Expert Systems with Applications Volume 36, 2009, pp 12399–12413.
  16. T. Rajaram and Ashutosh Das, “Modeling of interactions among sustainability components of an agro-ecosystem using local knowledge through cognitive mapping and fuzzy inference system”, Journal of Expert Systems with Applications, Volume 37, 2010, pp 1734–1744.
  17. Diego O. Ferraro, “Fuzzy knowledge-based model for soil condition assessment in Argentinean cropping systems”, Journal of Environmental Modelling & Software, Volume 24, 2009, pp 359–370.
  18. Dinesh K. Sharma and R. K. Jana, “Fuzzy goal programming based genetic algorithm approach to nutrient management for rice crop planning”, International Journal of Production Economics, Volume 121, 2009, pp 224–232.
  19. Biswajeet Pradhan, Saro Lee and Manfred F. Buchroithner, “Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping” Journal of Appllied Geomatrology, Volume 1, 2009, pp 3–15.
  20. Mahmoud Omid, Majid Lashgari, Hossein Mobli, Reza Alimardani, Saeid Mohtasebi and Reza Hesamifard, "Design of fuzzy logic control system incorporating human expert knowledge for combine harvester", Journal of Expert Systems with Applications, Volume 37, 2010, 7080–7085.
  21. Tayfun Cayand and Fatih Iscan, “Fuzzy expert system for land reallocation in land consolidation”, Journal of Expert Systems with Applications, Volume 38, 2011, pp 11055–11071.
  22. Nevcihan Duru, Funda Dokmen, M Mucella Canbay and Cengiz Kurtulus, “Soil productivity analysis based on a fuzzy logic system”, Society of Chemical Industry, journal of Science of food and agriculture volume 90, Issue 13, October 2010, pp 2220-2227.
  23. E. Kramer, D. Cavero, E. Stamer and J. Krieter, “Mastitis and lameness detection in dairy cows by application of fuzzy logic”, Journal of Livestock Science, volume 125, 2009, pp 92–96.
  24. J.A.E.B. Janssen, M.S. Krol, R.M.J. Schielen, A.Y. Hoekstra and J. L. de Kok, “Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models”, Journal of Ecological Modelling, volume 221, 2010, pp. 1245–1251.
  25. S. M. Mazloumzadeh, M. Shamsi and H. Nezamabadi-pour, “Fuzzy logic to classify date palm trees based on some physical properties related to precision agriculture”, Journal of Precision Agriculture, Volume 11, 2010, pp 258–273.
  26. Peng Xiaohong, Mo Zhi, Xiao Laisheng and Liu Guodong, “A Water-saving Irrigation System Based on Fuzzy Control Technology and Wireless Sensor Network”. 5th International conference on wireless communications, network and mobile computing, 2009, Beijing, China, 24-26 September 2009, pp 1-4.
  27. Andrew Chiou and Xinghuo Yu, “Remote Sensing in Decision Support Systems: Using Fuzzy Post Adjustment in Localisation of Weed Prediction”, Symposium on Computational Intelligence for Sensor Networks, Melbourne Australia, 3-6 Dec 2007, pp 533-538.
  28. Tang Huili, Ye Jiyao, Zhou Lianqing and Shi Zhou, “Agriculture Disease Diagnosis Expert System Based on Knowledge and Fuzzy Reasoning: A Case Study of Flower”, Sixth International Conference on Fuzzy Systems and Knowledge Discovery, Tianjion, China, 14-16 August 2009, pp 39-43.
  29. Savita Kolhe, Raj Kamal, Harvinder S. Saini and G.K. Gupta , “A web-based intelligent disease-diagnosis system using a new fuzzy-logic based approach for drawing the inferences in crops”, Journal of Computers and Electronics in Agriculture, year 2011, volume 76, pp 16–27
Index Terms

Computer Science
Information Sciences

Keywords

Agricultural information system fuzzy logic rule promotion methodology expert systems disease diagnosis