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

Abstract

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.

Entomopathogens in the integrated management of forest insects: from science to practice

“…143 For example, research on the use of molecular methods, consisting of analyzing DNA extracted from leaves to detect the presence of target species, 144 and on the use of unmanned aerial vehicles (UAVs) together with geographic information systems (GIS) provide information on stands damaged by insects and the direction and speed of outbreak spread. 145 Locations where insects increase after a period of latency and knowledge of the mechanisms by which they disperse allow early identification of areas where outbreaks may occur. Using natural enemies in such locations could be a prophylactic measure to reduce the spread of pests during periods of increased insect emergence, which in turn may prevent outbreaks from developing.…”

“…Improving methods for detecting insect pests in the forest (by counting insect populations in the soil, litter, and trees, and by using traps baited with pheromones or kairomones) is extremely important 143 . For example, research on the use of molecular methods, consisting of analyzing DNA extracted from leaves to detect the presence of target species, 144 and on the use of unmanned aerial vehicles (UAVs) together with geographic information systems (GIS) provide information on stands damaged by insects and the direction and speed of outbreak spread 145 …”

Extraction of pine wilt disease based on a two-stage unmanned aerial vehicle deep learning method

“…By comparison, unmanned aerial vehicles (UAVs), as a rapid, real-time, reliable, and easily deployable monitoring technology, overcome the limitations of ground surveys and satellite remote sensing. 12 They offer the flexibility to set spatial resolution and data acquisition types. Moreover, UAV platforms enable the rapid acquisition of various remote sensing data for the monitoring area, including visible, multi-spectral or hyper-spectral data.…”

“…By comparison, unmanned aerial vehicles (UAVs), as a rapid, real-time, reliable, and easily deployable monitoring technology, overcome the limitations of ground surveys and satellite remote sensing 12 . They offer the flexibility to set spatial resolution and data acquisition types.…”

Texture descriptors and machine learning algorithms for mistletoe detection in urban forests using multispectral imagery

“…In recent years, Remote Sensing (RS) has played an important role for forest pest monitoring given the acquisition possibilities of high-resolution images through unmanned aerial vehicles (UAVs). 1 In particular, the autonomous identification of mistletoe species has become of particular interest because of their rapid spreading mechanisms; mistletoe is a hemiparasitic plant that grows on host trees of coniferous and broad-leaved forests, woodlands, and urban parks by inserting a modified root in the first layers of the host's branches. Thus, once the plant is established as an extension of the tree, it can access to salts, minerals, and water, reducing the quality of life of the host.…”