Machine Learning for olive phenology prediction and base temperature optimisation
| Autor: | S. Marchi, Igor G. Olaizola, D. Guidotti, Noelia Oses, Izar Azpiroz, Marco Quartulli |
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| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
business.industry Phenology Computer science 04 agricultural and veterinary sciences Growing degree-day Machine learning computer.software_genre Base (topology) 01 natural sciences 13. Climate action Heating energy BBCH-scale 040103 agronomy & agriculture Added value 0401 agriculture forestry and fisheries Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | 2020 Global Internet of Things Summit (GIoTS) GIoTS |
| DOI: | 10.1109/giots49054.2020.9119611 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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