Machine Learning for olive phenology prediction and base temperature optimisation

Fecha: 03.06.2020


Abstract

Several methods based on regression techniques are used for the prediction of phenological phases in modern olive growing. This study collects phenological observations and agrometeorological data for several Italian provinces. The aim of the analysis was to provide a geographically tailored value for the base temperature, i.e., the most important parameter in determining the Growing Degree Days (GDD). Machine learning methods were compared to optimize phenological predictions and base temperature for heat unit accumulation. The use of low base temperature resulted in better model prediction, which has added value under a warming climate scenario.

BIB_text

@Article {
title = {Machine Learning for olive phenology prediction and base temperature optimisation},
pages = {9119611},
keywds = {
Phenophase, olive phenology modeling, BBCH scale, machine learning, base temperature
}
abstract = {

Several methods based on regression techniques are used for the prediction of phenological phases in modern olive growing. This study collects phenological observations and agrometeorological data for several Italian provinces. The aim of the analysis was to provide a geographically tailored value for the base temperature, i.e., the most important parameter in determining the Growing Degree Days (GDD). Machine learning methods were compared to optimize phenological predictions and base temperature for heat unit accumulation. The use of low base temperature resulted in better model prediction, which has added value under a warming climate scenario.


}
isbn = {978-172812171-0},
date = {2020-06-03},
}
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