Environmental Impact on Predicting Olive Fruit Fly Population Using Trap Measurements

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 as input environmental parameters and knowledge of previous 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.

Combining favorability modeling with collaborative geo‐visual analysis to improve agricultural pest management

“…The OFF population's development is greatly influenced by the seasonal development of its main host, the cultivated olive (Zalom et al, 2009) and climatic factors, especially temperature and relative humidity (Kalamatianos, Kermanidis, Avlonitis, & Karydis, 2016). In optimum temperature conditions (20-30°C), a complete generation cycle takes about 30-35 days (Rice, 2000;Zalom et al, 2009).…”

Collaborative geovisual analytics

“…The OFF population development is greatly influenced by the seasonal development of its primary host, the cultivated olive tree [172] and climatic factors, especially temperature and relative humidity [77]. Under optimal temperature conditions (20°to 30°C), a complete generation cycle takes about 30 to 35 days [129,172].…”