Machine learning assisted remote forestry health assessment: a comprehensive state of the art review
Date Issued
2023-06
Author(s) USM
Auat, Fernando
Estrada, J.S.
DOI
10.3389/fpls.2023.1139232
Abstract
Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems ha
