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Over the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies.
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Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment
Natural disasters pose significant threats to human life and property, exacerbated by their sudden onset and increasing frequency. This paper conducts a comprehensive bibliometric review to explore robust methodologies for post-disaster building damage assessment and reconnaissance, focusing on the integration of advanced data collection technologies and computational techniques. The objectives of this study were to assess the current landscape of methodologies, highlight technological advancements, and identify significant trends and gaps in the literature. Using a structured approach for data collection, this review analyzed 370 journal articles from the Scopus database from 2014 to 2024, emphasizing recent developments in remote sensing, including satellite and UAV technologies, and the application of machine learning and deep learning for damage detection and analysis. Our findings reveal substantial advancements in data collection and analysis techniques, underscoring the critical role of machine learning and remote sensing in enhancing disaster damage assessments. The results are significant as they highlight areas requiring further research and development, particularly in data fusion techniques, real-time processing capabilities, model generalization, UAV technology enhancements, and training for the rescue team. These areas are crucial for improving disaster management practices and enhancing community resilience. The application of our research is particularly relevant in developing more effective emergency response strategies and in informing policy-making for disaster-prepared social infrastructure planning. Future research should focus on closing the identified gaps and leveraging cutting-edge technologies to advance the field of disaster management.
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- Award ID(s):
- 2346936
- PAR ID:
- 10528092
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Buildings
- Volume:
- 14
- Issue:
- 8
- ISSN:
- 2075-5309
- Page Range / eLocation ID:
- 2344
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/buildings14082344
