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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272373 | PMC |
| http://dx.doi.org/10.3389/fpls.2023.1139232 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A prediction model for electrical strength of gaseous medium based on molecular reactivity descriptors and machine learning method.
J Mol Model
January 2025
Hubei Key Laboratory·for High-Efficiency-Utilization of Solar Energy and Operation, Control of Energy-Storage System, Hubei-University of Technology, Wuhan, 430068, China.
Context: Ionization and adsorption in gas discharge are similar to electrophilic and nucleophilic reactions. The molecular descriptors characterizing reactions such as electrostatic potential descriptors are useful in predicting the electrical strength of environmentally friendly gases. In this study, descriptors of 73 molecules are employed for correlation analysis with electrical strength.
View Article and Find Full Text PDFPredicting fall parameters from infant skull fractures using machine learning.
Biomech Model Mechanobiol
January 2025
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limited information about the incident is available in such cases, and witness statements are not always reliable. In this study, we introduce a novel, data-driven approach to predict fall parameters that lead to skull fractures in infants in order to aid in determinations of abusive head trauma.
View Article and Find Full Text PDFRole of immune cell homeostasis in research and treatment response in hepatocellular carcinoma.
Clin Exp Med
January 2025
Department of Thoracic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
Introduction Recently, immune cells within the tumor microenvironment (TME) have become crucial in regulating cancer progression and treatment responses. The dynamic interactions between tumors and immune cells are emerging as a promising strategy to activate the host's immune system against various cancers. The development and progression of hepatocellular carcinoma (HCC) involve complex biological processes, with the role of the TME and tumor phenotypes still not fully understood.
View Article and Find Full Text PDFds-FCRN: three-dimensional dual-stream fully convolutional residual networks and transformer-based global-local feature learning for brain age prediction.
Brain Struct Funct
January 2025
Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing, 100124, China.
The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.
View Article and Find Full Text PDFThe Impact of Adverse Childhood Experiences on the Development of Adolescent Risk-Taking: The Mediating Effect of Self-Control and Moderating Effect of Genetic Variations.
J Youth Adolesc
January 2025
Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, China.
Risk-taking is a concerning yet prevalent issue during adolescence and can be life-threatening. Examining its etiological sources and evolving pathways helps inform strategies to mitigate adolescents' risk-taking behavior. Studies have found that unfavorable environmental factors, such as adverse childhood experiences (ACEs), are associated with momentary levels of risk-taking in adolescents, but little is known about whether ACEs shape the developmental trajectory of risk-taking.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!