Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review

Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpo...

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Bibliographic Details
Published inForests Vol. 13; no. 6; p. 911
Main Authors Duarte, André, Borralho, Nuno, Cabral, Pedro, Caetano, Mário
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2022
Subjects
Analytical methods
Bark
Bibliometrics
Climate change
Data collection
Data processing
Deep learning
Disease
Epidemics
forest
Forestry
insect pest and disease monitoring
Insects
Literature reviews
Machine learning
Meta-analysis
Pests
PRISMA protocol
Remote monitoring
Remote sensing
Reviews
Search engines
Sensors
Software
Spatial discrimination
Spatial resolution
Systematic review
Taxonomy
Trends
Unmanned aerial vehicles
Wilt
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Summary:Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red–green–blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented.
ISSN:1999-4907
1999-4907
DOI:10.3390/f13060911