Identification of Black Mold Disease in Tomato using Fuzzy Inference System
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 4
Abstract
Tomato is most commonly grown vegetable in all over the world. Tomato is used in many ways as a constituent such as sauces, pickles, salads, and drinks etc[1]. Tomatoes get easily infected as they are susceptible to temperature. It can be infected through many diseases like bacteria, virus, and fungus etc[2]. Black mold disease is caused by the fungus alternaria alternata. This fungus generally attacks to ripe tomatoes as well as those who are kept free in moisture also. Symptoms of black mold disease are tiny brown color spot on the surface which get turn into big hollow, deep set on lesion of tomato. The aim of this paper is to analyze the work of different researchers who have applied the fuzzy inference system (fis) for disease detection. This research work also includes the result of hybrid approach of backpropagation neural network (bpnn) and genetic algorithm (ga) which has been applied for detection of black mold disease in tomato. The result of this hybrid approach can be improved by applying the fuzzy inference system classifier. This research paper gives a general approach for detection of black mold disease in tomato using fuzzy inference system classifier.
Authors and Affiliations
Mamta Yadav
Keywords
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- EP ID EP23954
- DOI http://doi.org/10.22214/ijraset.2017.4248
- Views 304
- Downloads 9
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