Original papers: A web-based intelligent disease-diagnosis system using a new fuzzy-logic based approach for drawing the inferences in crops

Authors:
Savita Kolhe;Raj Kamal;Harvinder S. Saini;G. K. Gupta
Affiliations:
Computer Applications, Directorate of Soybean Research (ICAR), Khandwa Road, Indore 452017, Madhya Pradesh, India;School of Computer Sciences and Electronics, Devi Ahilya University, Khandwa Road, Indore 452017, Madhya Pradesh, India;Guru Nanak Institutions, Hyderabad, Andhra Pradesh, India;Plant Pathology, Directorate of Soybean Research (ICAR), Khandwa Road, Indore 452017, Madhya Pradesh, India
Venue:
Computers and Electronics in Agriculture
Year:
2011

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Abstract

This paper suggests a new approach for providing intelligence in the system for diagnosis of diseases of the oilseed-crops. It reports the development of a web-based intelligent disease diagnosis system (WIDDS). The WIDDS is based on a new fuzzy logic approach. The approach is based on use of a rule-promotion methodology. This approach enables the drawing of inferences with the enhanced intelligence. The WIDDS also incorporates new features that improve the presently existing expert systems. The new features are (i) object-oriented (O-O) inference model, (ii) dynamic knowledge base creation strategy. The dynamically promoted rules are derived from those diagnosis sessions, which resulted in successful decisions. This enables more efficient decision-making in the future sessions, (iii) audio-visual-graphical user interface using text-to-speech (TTS) conversion tools. The WIDDS results in decreasing not only the number of interactive question-answer sessions with the clients but also leads to acceptable diagnosis. Further, the inferences are drawn faster compared to the traditional approach, which is the expert based reasoning method. The suggested WIDDS, which is based on rule-promotion approach, has been tested for three oilseeds crops - soybean, groundnut and rapeseed-mustard.