Fuzzy Technology Design for Early Detection of Diseases in Tobacco Plants

Abstract

Tobacco is an agricultural product that uses leaves to be processed into pesticides, medicines and cigarettes. Tobacco quality is determined by plant maintenance and reduced pest and disease attacks. To avoid these disturbances, control is needed quickly, precisely and accurately so that the tobacco plant disease cannot spread throughout agricultural land. In making fuzzy, diseases and symptoms in tobacco plants are used as a rule base in making a fuzzy expert system. The expert system created in this research is an expert system using the concept of fuzzy logic to diagnose tobacco plant diseases, using the Mamdani inference method and the defuzzification process using the centroid method (firmness value) to get the right conclusions in diagnosing tobacco plant diseases. From the results of Mamdani's design and manual fuzzy calculations, it can be concluded that the design is ready to be further implemented into the required programming language. From the sample calculation results, it was found that damping off disease has a moderate degree of risk with a value of 41.54. With the construction of this system, it will provide easy information for farmers to carry out and find out what symptoms are contracting diseases in tobacco plants.