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Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence

This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate the spatiotemporal variability of some winter wheat parameters, including the relative leaf chlorophyll content (RCC), relative water content (...

Whakaahuatanga katoa

I tiakina i:
Ngā kaituhi matua: Gabriele De Carolis, Vincenzo Giannico, Leonardo Costanza, Francesca Ardito, Anna Maria Stellacci, Afwa Thameur, Sergio Ruggieri, Sabina Tangaro, Marcello Mastrorilli, Nicola Sanitate, Simone Pietro Garofalo
Hōputu: Artigo
Reo:Inglês
I whakaputaina: MDPI AG 2025-01-01
Rangatū:Agronomy
Ngā marau:
chlorophyll content
relative water content
dry matter
remote sensing
planet
machine learning
Urunga tuihono:https://www.mdpi.com/2073-4395/15/1/241
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