Literature Review on Fuzzy Expert System in Agriculture
Sonal Dubey1, R. K. Pandey2, S. S. Gautam3
1Ms.Sonal Dubey, Ph.D Scholar,Faculty of Science & Environment, Mahatma Gandhi Chitrakoot Gramodaya Vishwa Vidyalaya, Chitrakoot Satna, India.
2Dr. R.K. Pandey, University Institute of Computer Science & Application, Rani Durgavati Vishwa Vidyalaya, Jabalpur, India.
3Dr.S.S. Gautam, Faculty of Science & Environment, Mahatma Gandhi Chitrakoot Gramodaya Vishwa Vidyalaya, Chitrakoot Satna, India.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 289-291 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1186112612/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Agriculture constitutes the backbone of the Indian economy. Farmer need advance expert knowledge to take decision during land preparation, sowing, fertilizer management, irrigation management, integrated pest management, storage etc. for higher crop production. Expert systems are being used in agriculture which assists the farmers to make right decisions. Expert systems for pest management and crop protection constitute a very significant class of agricultural expert systems. Knowledge of entomology, plant pathology, nematology, weeds and nutritional disorders and various number of techniques used are included in integrated pest management and crop protection. Uncertainty is confronted during time of sowing, weed management, diagnosis of insect, disease and nutritional disorders, storage, marketing of the produce etc. This uncertainty is compounded by the fact that many agricultural decisionmaking activities are often vague or based on intuition. Fuzzy logic is used to handle imprecision, vagueness and insufficient knowledge. Fuzzy logic lets expert systems perform optimally with uncertain or ambiguous data and knowledge. Fuzzy expert systems use fuzzy logic instead of classical Boolean logic. They are oriented towards numerical processing The paper presents a review of various fuzzy expert systems in agriculture over the last two decades.
Keywords: Agriculture, Integrated Pest management, fuzzy expert system, rules.