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

Most bamboos die after flowering, and the molecular mechanisms responsible for flowering is poorly understood. The MIKCc-type MADS-box family gene is involved in the flowering process. To explore the mechanism of the MIKCc-type MADS-box gene and phytohormone regulation in the flowering of Dendrocalamus latiflorus Munro (D. latiflorus), characterized by extremely rapid growth and widely cultivated woody bamboo, we initially did a genome-wide analysis of the MIKCc-type MADS-box gene in D. latiflorus. In the meantime, transcriptome analysis was performed using the floral organs. A total of 170 MIKCc-Type MADS-Box genes were identified and divided into 15 categories. The cis-acting element analysis in promoters regions revealed that MIKC-type MADS-box family genes were associated with hormones, including auxin, abscisic acid (ABA), gibberellin (GA) and jasmonic acid (JA), which was found at 79, 476, 96, 486 sites and cover 61, 103, 73, 128 genes. Genome synteny analysis showed subgenome AA and BB were better than CC and obtained 49, 40, 39 synteny genes compared with Oryza sativa (O. sativa). In transcriptome analysis of floral organs, the enriched pathway from DEGs included circadian, vernalization and gibberellin pathways associated with the flowering process. We found that the jasmonic acid synthesis gene is highly expressed in the pistil, which may be the cause of Ma bamboo pollen abortion. The expression profile showed that most MIKC-type MADS-box genes exhibited high expression in flower organs. The consequences of this study will provide insight into the irregular flowering and low pollen counts of Ma bamboo.

/pdf/genome-wide-identification-of-mikcc-type-mads-box-family-2c62alvx.pdf

Genome-Wide Identification of MIKCc-Type MADS-Box Family Gene and Floral Organ Transcriptome Characterization in Ma Bamboo (Dendrocalamus latiflorus Munro)

Dendrocalamus latiflorus Munro (D. latiflorus) is a woody clumping bamboo with rapid shoot growth. Both genetic transformation and CRISPR-Cas9 gene editing techniques are available for D. latiflorus, enabling reverse genetic approaches. Thus, D. latiflorus has the potential to be a model bamboo species. However, the genome sequence of D. latiflorus has remained unreported due to its polyploidy and large genome size. Here, we sequenced the D. latiflorus genome and assembled it into three allele-aware subgenomes (AABBCC), representing the largest genome of a major bamboo species. We assembled 70 allelic chromosomes (2,737 Mb) for hexaploid D. latiflorus using both single-molecule sequencing from the Pacific Biosciences (PacBio) Sequel platform and chromosome conformation capture sequencing (Hi-C). Repetitive sequences comprised 52.65% of the D. latiflorus genome. We annotated 135,231 protein-coding genes in the genome based on transcriptomes from eight different tissues. Transcriptome sequencing using RNA-Seq and PacBio single-molecule real-time (SMRT) long-read isoform sequencing (Iso-Seq) revealed highly differential alternative splicing (AS) between non-abortive and abortive shoots, suggesting that AS regulates the abortion rate of bamboo shoots. This high-quality hexaploid genome and comprehensive strand-specific transcriptome datasets for this Poaceae family member will pave the way for bamboo research using D. latiflorus as a model species. This article is protected by copyright. All rights reserved.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jipb.13217

Allele-aware chromosome-scale assembly of the allopolyploid genome of hexaploid Ma Bamboo (Dendrocalamus latiflorus Munro).

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Papers

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

Genome-Wide Identification of MIKCc-Type MADS-Box Family Gene and Floral Organ Transcriptome Characterization in Ma Bamboo (Dendrocalamus latiflorus Munro)

Allele-aware chromosome-scale assembly of the allopolyploid genome of hexaploid Ma Bamboo (Dendrocalamus latiflorus Munro).