Abstract
Traditionally, the main process for olive fruit fly population monitoring is trap measurements. Although the above procedure is time-consuming, it gives important information about when there is an outbreak of the population and how the insect is spatially distributed in the olive grove. Most studies in the literature are based on the combination of trap and environmental data measurements. Strictly speaking, the dynamics of olive fruit fly population is a complex system affected by a variety of factors. However, the collection of environmental data is costly, and sensor data often require additional processing and cleaning. In order to study the volatility of correlation in trap counts and how it is connected with population outbreaks, a stochastic algorithm, based on a stochastic differential model, is experimentally applied. The results allow us to predict early population outbreaks allowing for more efficient and targeted spraying.
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Acknowledgments
The financial support of the European Union and Greece (Partnership Agreement for the Development Framework 2014–2020), under the Regional Operational Programme Ionian Islands 2014–2020, for the project “Olive Observer” is gratefully acknowledged.
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Kalamatianos, R., Gavras, S., Boubouras, C., Kotinas, D., Avlonitis, M. (2020). Effective Stochastic Algorithm in Disease Prediction. In: Vlamos, P. (eds) GeNeDis 2018. Advances in Experimental Medicine and Biology, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-32622-7_27
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DOI: https://doi.org/10.1007/978-3-030-32622-7_27
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