Currently, plant phenological phases are determined through models based on historical data series and advanced measurement tools such as satellite imaging, which are not always accurate. This study explores innovatively the relationship between Volatile Organic Compounds emitted by tomato plants and their phenological phases using machine learning algorithms. Supervised models, particularly k-NN, achieved an accuracy of 96.8% in identifying phases. These results highlight the potential of sensors and AI to accurately monitor crop development
Correlation of gaseous emissions with phenological phases in tomato crops
Tamisari Melissa
Primo
;Tavaglione Emanuela;Francesco Tralli;Matteo Valt;Barbara FabbriUltimo
2025
Abstract
Currently, plant phenological phases are determined through models based on historical data series and advanced measurement tools such as satellite imaging, which are not always accurate. This study explores innovatively the relationship between Volatile Organic Compounds emitted by tomato plants and their phenological phases using machine learning algorithms. Supervised models, particularly k-NN, achieved an accuracy of 96.8% in identifying phases. These results highlight the potential of sensors and AI to accurately monitor crop developmentFile in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
