In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

/pdf/uav-based-forest-health-monitoring-a-systematic-review-2yanid5m.pdf

UAV-Based Forest Health Monitoring: A Systematic Review

Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery

In recent years, UAV remote sensing has gradually attracted the attention of scientific researchers and industry, due to its broad application prospects. It has been widely used in agriculture, forestry, mining, and other industries. UAVs can be flexibly equipped with various sensors, such as optical, infrared, and LIDAR, and become an essential remote sensing observation platform. Based on UAV remote sensing, researchers can obtain many high-resolution images, with each pixel being a centimeter or millimeter. The purpose of this paper is to investigate the current applications of UAV remote sensing, as well as the aircraft platforms, data types, and elements used in each application category; the data processing methods, etc.; and to study the advantages of the current application of UAV remote sensing technology, the limitations, and promising directions that still lack applications. By reviewing the papers published in this field in recent years, we found that the current application research of UAV remote sensing research can be classified into four categories according to the application field: (1) Precision agriculture, including crop disease observation, crop yield estimation, and crop environmental observation; (2) Forestry remote sensing, including forest disease identification, forest disaster observation, etc.; (3) Remote sensing of power systems; (4) Artificial facilities and the natural environment. We found that in the papers published in recent years, image data (RGB, multi-spectral, hyper-spectral) processing mainly used neural network methods; in crop disease monitoring, multi-spectral data are the most studied type of data; for LIDAR data, current applications still lack an end-to-end neural network processing method; this review examines UAV platforms, sensors, and data processing methods, and according to the development process of certain application fields and current implementation limitations, some predictions are made about possible future development directions.

https://www.mdpi.com/2504-446X/7/6/398/pdf?version=1686822643

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level

In recent decades, scientific and technological developments have continued to increase in speed, with researchers focusing not only on the innovation of single technologies but also on the cross-fertilization of multidisciplinary technologies. Unmanned aerial vehicle (UAV) technology has seen great progress in many aspects, such as geometric structure, flight characteristics, and navigation control. The You Only Look Once (YOLO) algorithm was developed and has been refined over the years to provide satisfactory performance for the real-time detection and classification of multiple targets. In the context of technology cross-fusion becoming a new focus, researchers have proposed YOLO-based UAV technology (YBUT) by integrating the above two technologies. This proposed integration succeeds in strengthening the application of emerging technologies and expanding the idea of the development of YOLO algorithms and drone technology. Therefore, this paper presents the development history of YBUT with reviews of the practical applications of YBUT in engineering, transportation, agriculture, automation, and other fields. The aim is to help new users to quickly understand YBUT and to help researchers, consumers, and stakeholders to quickly understand the research progress of the technology. The future of YBUT is also discussed to help explore the application of this technology in new areas.

/pdf/yolo-based-uav-technology-a-review-of-the-research-and-its-2mctscgl.pdf

YOLO-Based UAV Technology: A Review of the Research and Its Applications

Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning

Background The immunity of moso bamboo (Phyllostachys heterocycle) to insect defoliator outbreaks differs between on-years to off-years; however, the underlying genetic mechanisms remain unknown. In this study, the genetic relationships of functional genes conferring pest resistance were investigated. Results PhJAZ1 (Phyllostachys heterocycla JASMONATE ZIM-domain protein 1) exhibited the highest enrichment and was expressed at higher levels in the leaves of on-year bamboo plants compared with off-year, whereas the expression of PhSPL17 (Phyllostachys heterocycla SQUAMOSA Promoter binding protein-Like 17) showed the reverse pattern. The expression pattern of PhJAZ1 differed in on- and off-year bamboo (i.e., decreasing in the off-year with no obvious change in the on-year) after feeding by Pantana phyllostachysae (lepidopteran caterpillar of moso bamboo). Due to the lack of a genetic transformation system, the model plant Arabidopsis was used for the investigation of the genetic relationships between PhJAZ1 and PhSPL17. Overexpression of the PhJAZ1 protein in Arabidopsis showed a negative impact on the survival ratio and weight of third-instar Helicoverpa armigera (Arabidopsis leaf-feeding lepidopteran caterpillar). Transcriptional suppression of PhJAZ1 by PhSPL17 was observed, which was able to reveal the reverse expression pattern of PhJAZ1 and PhSPL17. Conclusion Together, these results suggest that on- and off-years (leaf age) regulate the expression of PhSPL17, which negatively regulates the expression of PhJAZ1 to generate differential response to Jasmonate. This study is the first to detail the genetic connection between leaf age and Jasmonate response in moso bamboo and provides a foundation for further pest control via the genetic breeding of moso bamboo. © 2019 Society of Chemical Industry.

The interactions of PhSPL17 and PhJAZ1 mediate the on- and off-year moso bamboo (Phyllostachys heterocycla) resistance to the Pantana phyllostachysae larval feeding.

/pdf/endophyte-colonization-enhances-tolerance-of-casuarina-3rfmg74pm0.pdf

Endophyte Colonization Enhances Tolerance of Casuarina Equisetifolia to NaCl Stress by Modifying Osmoregulation, Antioxidant Enzymes, and Phytohormones

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Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning

The interactions of PhSPL17 and PhJAZ1 mediate the on- and off-year moso bamboo (Phyllostachys heterocycla) resistance to the Pantana phyllostachysae larval feeding.

Endophyte Colonization Enhances Tolerance of Casuarina Equisetifolia to NaCl Stress by Modifying Osmoregulation, Antioxidant Enzymes, and Phytohormones