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Ye X, Pan J, Liu G, Shao F. Exploring the Close-Range Detection of UAV-Based Images on Pine Wilt Disease by an Improved Deep Learning Method. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0129. [PMID: 38107768 PMCID: PMC10723834 DOI: 10.34133/plantphenomics.0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/23/2023] [Indexed: 12/19/2023]
Abstract
Pine wilt disease (PWD) is a significantly destructive forest disease. To control the spread of PWD, an urgent need exists for a real-time and efficient method to detect infected trees. However, existing object detection models have often faced challenges in balancing lightweight design and accuracy, particularly in complex mixed forests. To address this, an improvement was made to the YOLOv5s (You Only Look Once version 5s) algorithm, resulting in a real-time and efficient model named PWD-YOLO. First, a lightweight backbone was constructed, composed of multiple connected RepVGG Blocks, significantly enhancing the model's inference speed. Second, a C2fCA module was designed to incorporate rich gradient information flow and concentrate on key features, thereby preserving more detailed characteristics of PWD-infected trees. In addition, the GSConv network was utilized instead of conventional convolutions to reduce network complexity. Last, the Bidirectional Feature Pyramid Network strategy was used to enhance the propagation and sharing of multiscale features. The results demonstrate that on a self-built dataset, PWD-YOLO surpasses existing object detection models with respective measurements of model size (2.7 MB), computational complexity (3.5 GFLOPs), parameter volume (1.09 MB), and speed (98.0 frames/s). The Precision, Recall, and F1-score on the test set are 92.5%, 95.3%, and 93.9%, respectively, which confirms the effectiveness of the proposed method. It provides reliable technical support for daily monitoring and clearing of infected trees by forestry management departments.
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Affiliation(s)
- Xinquan Ye
- College of Forestry,
Nanjing Forestry University, Nanjing 210037, China
| | - Jie Pan
- College of Forestry,
Nanjing Forestry University, Nanjing 210037, China
- Co-Innovation Center for Sustainable Forestry in Southern China,
Nanjing Forestry University, Nanjing 210037, China
| | - Gaosheng Liu
- College of Forestry,
Nanjing Forestry University, Nanjing 210037, China
| | - Fan Shao
- College of Forestry,
Nanjing Forestry University, Nanjing 210037, China
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Luo Y, Huang H, Roques A. Early Monitoring of Forest Wood-Boring Pests with Remote Sensing. ANNUAL REVIEW OF ENTOMOLOGY 2023; 68:277-298. [PMID: 36198398 DOI: 10.1146/annurev-ento-120220-125410] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Wood-boring pests (WBPs) pose an enormous threat to global forest ecosystems because their early stage infestations show no visible symptoms and can result in rapid and widespread infestations at later stages, leading to large-scale tree death. Therefore, early-stage WBP detection is crucial for prompt management response. Early detection of WBPs requires advanced and effective methods like remote sensing. This review summarizes the applications of various remote sensing sensors, platforms, and detection methods for monitoring WBP infestations. The current capabilities, gaps in capabilities, and future potential for the accurate and rapid detection of WBPs are highlighted.
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Affiliation(s)
- Youqing Luo
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, P.R. China;
- Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University/French National Research Institute for Agriculture, Food and Environment (INRAE), Beijing, P.R. China/Paris, France
| | - Huaguo Huang
- Research Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, P.R. China;
| | - Alain Roques
- Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University/French National Research Institute for Agriculture, Food and Environment (INRAE), Beijing, P.R. China/Paris, France
- INRAE-Zoologie Forestière, Centre de recherche Val de Loire, Orléans, France;
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3
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Yu R, Huo L, Huang H, Yuan Y, Gao B, Liu Y, Yu L, Li H, Yang L, Ren L, Luo Y. Early detection of pine wilt disease tree candidates using time-series of spectral signatures. FRONTIERS IN PLANT SCIENCE 2022; 13:1000093. [PMID: 36311089 PMCID: PMC9606806 DOI: 10.3389/fpls.2022.1000093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75-0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring.
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Affiliation(s)
- Run Yu
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, China
| | - Langning Huo
- Department of Forest Resource Management, Swedish University of Agriculture Sciences, Umeå, Sweden
| | - Huaguo Huang
- Research Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, China
| | - Yuan Yuan
- French National Research Institute for Agriculture, Food and Environment (INRAE)—Zoologie Forestiere Centre de recherche d’Orléans, Orléans, France
| | - Bingtao Gao
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, China
| | - Yujie Liu
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, China
| | - Linfeng Yu
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, China
| | - Haonan Li
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, China
| | - Liyuan Yang
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, China
| | - Lili Ren
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, China
- Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University—French National Research Institute for Agriculture, Food and Environment (INRAE), Beijing, China
| | - Youqing Luo
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, China
- Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University—French National Research Institute for Agriculture, Food and Environment (INRAE), Beijing, China
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Lu J, Qiu H, Zhang Q, Lan Y, Wang P, Wu Y, Mo J, Chen W, Niu H, Wu Z. Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method. FRONTIERS IN PLANT SCIENCE 2022; 13:1009630. [PMID: 36247579 PMCID: PMC9562855 DOI: 10.3389/fpls.2022.1009630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/29/2022] [Indexed: 06/16/2023]
Abstract
During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R 2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R 2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.
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Affiliation(s)
- Jianqiang Lu
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Hongbin Qiu
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Qing Zhang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Yubin Lan
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Panpan Wang
- The 14th Division of Xinjiang Production and Construction Corps, Institute of Agricultural Sciences, Kunyu, China
| | - Yue Wu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Jiawei Mo
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Wadi Chen
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - HongYu Niu
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Zhiyun Wu
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
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Abstract
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.
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Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review. FORESTS 2022. [DOI: 10.3390/f13060911] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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.
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Xia F, Quan L, Lou Z, Sun D, Li H, Lv X. Identification and Comprehensive Evaluation of Resistant Weeds Using Unmanned Aerial Vehicle-Based Multispectral Imagery. FRONTIERS IN PLANT SCIENCE 2022; 13:938604. [PMID: 35937335 PMCID: PMC9346607 DOI: 10.3389/fpls.2022.938604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 06/10/2022] [Indexed: 05/08/2023]
Abstract
Atrazine is one of the most widely used herbicides in weed management. However, the widespread use of atrazine has concurrently accelerated the evolution of weed resistance mechanisms. Resistant weeds were identified early to contribute to crop protection in precision agriculture before visible symptoms of atrazine application to weeds in actual field environments. New developments in unmanned aerial vehicle (UAV) platforms and sensor technologies promote cost-effective data collection by collecting multi-modal data at very high spatial and spectral resolution. In this study, we obtained multispectral and RGB images using UAVs, increased available information with the help of image fusion technology, and developed a weed spectral resistance index, WSRI = (RE-R)/(RE-B), based on the difference between susceptible and resistant weed biotypes. A deep convolutional neural network (DCNN) was applied to evaluate the potential for identifying resistant weeds in the field. Comparing the WSRI introduced in this study with previously published vegetation indices (VIs) shows that the WSRI is better at classifying susceptible and resistant weed biotypes. Fusing multispectral and RGB images improved the resistance identification accuracy, and the DCNN achieved high field accuracies of 81.1% for barnyardgrass and 92.4% for velvetleaf. Time series and weed density influenced the study of weed resistance, with 4 days after application (4DAA) identified as a watershed timeframe in the study of weed resistance, while different weed densities resulted in changes in classification accuracy. Multispectral and deep learning proved to be effective phenotypic techniques that can thoroughly analyze weed resistance dynamic response and provide valuable methods for high-throughput phenotyping and accurate field management of resistant weeds.
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Affiliation(s)
- Fulin Xia
- College of Engineering, Northeast Agricultural University, Harbin, China
| | - Longzhe Quan
- College of Engineering, Anhui Agricultural University, Anhui, China
- College of Engineering, Northeast Agricultural University, Harbin, China
- *Correspondence: Longzhe Quan,
| | - Zhaoxia Lou
- College of Engineering, Northeast Agricultural University, Harbin, China
| | - Deng Sun
- College of Engineering, Northeast Agricultural University, Harbin, China
| | - Hailong Li
- College of Engineering, Anhui Agricultural University, Anhui, China
| | - Xiaolan Lv
- Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences (JAAS), Jiangsu, China
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Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images. REMOTE SENSING 2021. [DOI: 10.3390/rs13204065] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.
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