Using Data Mining to Predict Diseases in Vineyards and Olive Groves



Currently, the advancements in computer technology allows progress of the agricultural sector. Producers and service providers are exploring the value of information and its importance in increasing the productivity and profitability of a farm. This paper intends to evaluate various classification algorithms of data mining to predict various diseases in vineyards and olive groves. We propose using machine learning to predict diseases based on symptoms and weather data. The accuracy of classification algorithms like Random Forest, IBK, Naïve Bayes and SMO have been compared using Weka Software. Using our proposal, it is expected to reduce the incidence of diseases by more than 75%.


Classification, Data Mining, Weka, Ramdom Forest, IBk, NaiveBayes, SMO


9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, November 2017

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