文章基本信息

标题：A PREDICTION OLIVE DISEASES USING MACHINE LEARNING MODELS, DECISION TREE AND NA�VE BAYES MODELS
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作者：JAFAR DRDSH ; DERAR ELEYAN ; AMNA ELEYAN 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2021
卷号：99
期号：18
语种：English
出版社：Journal of Theoretical and Applied
摘要：Machine Learning Models as Decision Tree and Na�ve Bayes �NB� models are widely used to predict diseases. This paper has used these models to predict olive diseases. It relies on image processing of the olive leaves and predict the type of disease according to the information gathered from different images. Where 2000 images were collected of an olive leaf which is affected by the disease and healthy. The results show that the accuracy of prediction in the decision tree model is 97% and in the NB model it has reached 80%.This idea was inspired by an idea found in disease prediction, such as the Agrobase application, which analyzes and processes images, and then returns to the associated database to analyze and compare the results, and then gives the prediction result.In this research, we focus on the accuracy of prediction and image analysis, where we highlighted the most deadly disease in olives, olive leaf spot disease, so its color was analyzed and open cv was adopted in the Python language.
关键词：Olive Diseases;Machine Learning Models;Decision Tree;Na�ve Bayes models;Olive Spot Diseas