Machine learning assisted remote forestry health assessment: a comprehensive state of the art review.
| Autor: | Estrada JS; Department of Electronic Engineering, Universidad Tecnica Federico, Santamaria, Valparaíso, Chile., Fuentes A; Department of Industrial Engeneering, Universidad Tecnica Federica, Santamaria, Valparaíso, Chile., Reszka P; Faculty on Engineering and Science, Universidad Adolfo Ibáñez, Santiago, Chile., Auat Cheein F; Department of Electronic Engineering, Universidad Tecnica Federico, Santamaria, Valparaíso, Chile. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in plant science [Front Plant Sci] 2023 Jun 02; Vol. 14, pp. 1139232. Date of Electronic Publication: 2023 Jun 02 (Print Publication: 2023). |
| DOI: | 10.3389/fpls.2023.1139232 |
| Abstrakt: | 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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Estrada, Fuentes, Reszka and Auat Cheein.) |
| Databáze: | MEDLINE |
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