Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

Juan Sebastián Estrada, Andrés Fuentes, Pedro Reszka, Fernando Auat Cheein*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)
17 Downloads (Pure)

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 have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.

Original languageEnglish
Article number1139232
JournalFrontiers in Plant Science
Volume14
DOIs
Publication statusPublished - 2 Jun 2023

Keywords

  • forestry health assessment
  • machine learning
  • remote sensing
  • spectral information
  • vision system

ASJC Scopus subject areas

  • Plant Science

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