Abstract: The inflorescence rot (also known as bud rot) is a disease that affects many species of date palm. It is caused by a fungus called Mauginiella scaettae. Pathogenicity is based on various bioclimatic conditions, and these conditions vary greatly from year to year, thus affecting the amount of loss to harvest from the inflorescence rot disease. Quantifying and predicting the effects of many of these bioclimatic conditions is very complex if not impossible. Therefore, we believed it would be beneficial to analyze bioclimatic conditions and develop models to quantify and predict pathogenicity by utilizing several artificial intelligence methods, including fuzzy logic, which is ideal for analyzing uncertain environments. The fuzzy logic model consists of several input variables which characterize the factors influencing the development of inflorescence rot disease (i.e. temperature, rainfall, wind speed, humidity, and the corresponding season). The model will be capable of expanding to other additional factors not contained in this study, but which still have an impact on the degree of pathogenicity of the inflorescence rot disease. Once an appropriate rule base has been established based on actual observed conditions, the model will then be able to predict the rate of proliferation of the inflorescence rot disease as a function of using the input factors for estimating the potential proliferation of the disease affecting date palms. This method may help to reduce the risk of date palms becoming infected with inflorescence rot disease.

Keywords: Date palms, Palm diseases, Factors, Fuzzy logic

Pages: 81-85

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