Treating stochasticity of olive-fruit fly's outbreaks via machine learning algorithms

Abstract Olive fruit fly trap measurements are used as one of the indicators for olive grove infestation, and therefore, as a consultation tool on spraying parameters. In this paper, machine learning techniques are used to predict the next olive fruit fly trap measurement, given input knowledge of previous trap measurements as well as an attribute that acts as a correlation model between the temperature and the development of a pest’s population, known as the Degree Day model. This is the first time the Degree Day model is utilized as input in classification algorithms for the prediction of olive fruit fly trap measurements. Various classification algorithms are employed and applied to different environmental settings, in extensive comparative experiments, in order to detect the impact of the latter on olive fruit fly population prediction.

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