An Expert System for Diagnosing Tobacco Diseases Using CLIPS
International Journal of Academic Engineering Research (IJAER), 3(3), 12-18, March 2019
7 Pages Posted: 8 May 2019
Date Written: March 2019
Background: Tobacco, is an herbaceous annual or perennial plant in the family Solanaceae grown for its leaves. The tobacco plant has a thick, hairy stem and large, simple leaves which are oval in shape. The tobacco plant produces white, cream, pink or red flowers which grow in large clusters, are tubular in appearance and can reach 3.5-5.5 cm (1,25-2 in) in length. Tobacco may reach 1.2-1.8 m (4-6 ft) in height, Tobacco is one of the most widely abused substances in the world. It is highly addictive. The Centers for Disease Control and Prevention estimates that tobacco causes 6 million deaths per year. This makes tobacco the leading cause of preventable death, Nicotine is the main addictive chemical in tobacco. It causes a rush of adrenaline when absorbed in the bloodstream or inhaled via cigarette smoke. Nicotine also triggers an increase in dopamine. This is sometimes referred to as the brain’s “happy” chemical.
Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease.
Methods: In this paper the design of the proposed Expert System which was produced to help Doctor in diagnosing many of the tobacco diseases such as: Damping off, Frog eye leaf spot, Leaf blight / black shank, Anthracnose, Sore shin, Fusarium wilt, Brown spot, Tobacco mosaic disease, Tobacco ring spot disease, Cucumber mosaic disease, Tobacco leaf curl disease. The proposed expert system presents an overview about tobacco diseases are given, the cause of diseases are outlined. CLIPS and Delphi languages were used for designing and implementing the proposed expert system.
Results: The proposed tobacco diseases diagnosis expert system was evaluated by engineering students and found to be positive.
Keywords: Artificial Intelligence, Expert Systems, Clips, Tobacco Diseases
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