System to Predict Diseases in Vineyards and Olive Groves using Data Mining and Geolocation



In recent years, producers have complained about the disease attacks in their crops, due in large part to the weather conditions that lead to heavy losses. Information and communication technology in agriculture offers a wide range of solutions to some agricultural challenges. This technology that allows progress of the agricultural sector can increase the productivity and profitability of a farm. This paper intends to propose a System to predict diseases in Vineyards and Olive Groves using data mining and geolocation. Grapevine Downy Mildew, Powdery Mildew, Peacock Spot and Olive Anthracnose are the diseases used to test system because they are diseases that cause large losses in production that result in very small profits and large economic losses. The system captures and stores climatic, environmental data as well as data of the producers and their properties. The data collected by the system is used to predict diseases using data mining. We choose Random Forest algorithm provided by Weka, an open source system that provides a collection of visualization tools and algorithms for data analysis and predictive modelling, to calculate the probability of diseases occurrence. The main objective of the system is to help producers in a preventive way so that there is less loss in the production of such agricultural crops.


Data Mining, Geolocation, Agriculture, CropDiseases.




13th International Conference on Software Technologies, July 2018

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