This study introduces a scalable and cost-effective detection approach for plant disease detection in the context of Smart Agriculture exploiting Time Series Classification (TSC) algorithms to extract diagnostic information from Sentinel-2 spectral time series. The proposed approach achieves results consistent with or surpassing those of studies exploiting Unmanned Aerial Vehicle (UAV) data, ground data and commercial satellite data, while ensuring cost-effective and high-frequency monitoring.
A Scalable and Cost-Effective Approach for Disease Detection in Smart Agriculture: the case of Xylella Fastidiosa / Vanacore, A.; Ciardiello, A.; Izzo, A.; Auricchio, G. P.; Uccello, L.. - (2025), pp. 724-729. ( 2025 Conference of the 12th Scientific Meeting of the Statistics for the Evaluation and Quality of Services Group of the Italian Statistical Society (SVQS) IES 2025 - Innovation & Society: Statistics and Data Science for Evaluation and Quality BRIXEN - BRESSANONE -ITALY June 25-27, 2025).
A Scalable and Cost-Effective Approach for Disease Detection in Smart Agriculture: the case of Xylella Fastidiosa
Vanacore, A.
Primo
;Ciardiello, A.Secondo
;
2025
Abstract
This study introduces a scalable and cost-effective detection approach for plant disease detection in the context of Smart Agriculture exploiting Time Series Classification (TSC) algorithms to extract diagnostic information from Sentinel-2 spectral time series. The proposed approach achieves results consistent with or surpassing those of studies exploiting Unmanned Aerial Vehicle (UAV) data, ground data and commercial satellite data, while ensuring cost-effective and high-frequency monitoring.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
