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Pine wilt disease (PWD) can cause destructive death in many species of pine trees within a short period. The recognition of infected pine trees in unmanned aerial vehicle (UAV) forest images is a key technology for automatic monitoring and early warning of pests. This paper collected UAV visible and multispectral images of Korean pines (Pinus koraiensis) and Chinese pines (P. tabulaeformis) infected by PWD and divided the PWD infection into early, middle, and late stages. With the open-source annotation tool, LabelImg, we labeled the category of infected pine trees at each stage. After coordinate-correction preprocessing of the ground truth, the Korean pine and Chinese pine datasets were established. As a means of detecting infected pine trees of PWD and determining different infection stages, a multi-band image-fusion infected pine tree detector (MFTD) based on deep learning was proposed. Firstly, the Halfway Fusion mode was adopted to fuse the network based on four YOLOv5 variants. Simultaneously, the Backbone network was initially designed as a dual branching network that includes visible and multispectral subnets. Moreover, the features of visible and multispectral images were extracted. To fully utilize the features of visible and multispectral images, a multi-band feature fusion transformer (MFFT) with a multi-head attention mechanism and a feed-forward network was constructed to enhance the information correlation between visible and multispectral feature maps. Finally, following the MFFT module, the two feature maps were fused and input into Neck and Head to predict the categories and positions of infected pine trees. The best-performing MFTD model achieved the highest detection accuracy with mean average precision values (mAP@50) of 88.5% and 86.8% on Korean pine and Chinese pine datasets, respectively, which improved by 8.6% and 10.8% compared to the original YOLOv5 models trained only with visible images. In addition, the average precision values (AP@50) are 87.2%, 93.5%, and 84.8% for early, middle, and late stages on the KP dataset and 81.2%, 92.9%, and 86.2% on the CP dataset. Furthermore, the largest improvement is observed in the early stage with 14.3% and 11.6%, respectively. The results show that MFTD can accurately detect the infected pine trees, especially those at the early stage, and improve the early warning ability of PWD.
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32Investigation and evaluation of algorithms for unmanned aerial vehicle multispectral image registration
Investigation and evaluation of algorithms for unmanned aerial vehicle multispectral image registration
65Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images
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.
Read more1Monitoring Pine Wilt Disease Using High-Resolution Satellite Remote Sensing at the Single-Tree Scale with Integrated Self-Attention
Pine wilt disease has caused severe damage to China’s forest ecosystems. Utilizing the rich information from very-high-resolution (VHR) satellite imagery for large-scale and accurate monitoring of pine wilt disease is a crucial approach to curbing its spread. However, current research on identifying infected trees using VHR satellite imagery and deep learning remains extremely limited. This study introduces several advanced self-attention algorithms into the task of satellite-based monitoring of pine wilt disease to enhance detection performance. We constructed a dataset of discolored pine trees affected by pine wilt disease using imagery from the Gaofen-2 and Gaofen-7 satellites. Within the unified semantic segmentation framework MMSegmentation, we implemented four single-head attention models—NLNet, CCNet, DANet, and GCNet—and two multi-head attention models—Swin Transformer and SegFormer—for the accurate semantic segmentation of infected trees. The model predictions were further analyzed through visualization. The results demonstrate that introducing appropriate self-attention algorithms significantly improves detection accuracy for pine wilt disease. Among the single-head attention models, DANet achieved the highest accuracy, reaching 73.35%. The multi-head attention models exhibited an excellent performance, with SegFormer-b2 achieving an accuracy of 76.39%, learning the features of discolored pine trees at the earliest stage and converging faster. The visualization of model inference results indicates that DANet, which integrates convolutional neural networks (CNNs) with self-attention mechanisms, achieved the highest overall accuracy at 94.43%. The use of self-attention algorithms enables models to extract more precise morphological features of discolored pine trees, enhancing user accuracy while potentially reducing production accuracy.
Read more11A Pine Wilt Disease Detection Model Integrated with Mamba Model and Attention Mechanisms Using UAV Imagery
Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitoring, biochemical detection and satellite remote sensing are frequently inadequate for the timely detection and control of pine wilt disease. This paper presents a fusion model, which integrates the Mamba model and the attention mechanism, for deployment on unmanned aerial vehicles (UAVs) to detect infected pine trees. The experimental dataset presented in this paper comprises images of pine trees captured by UAVs in mixed forests. The images were gathered primarily during the spring of 2023, spanning the months of February to May. The images were subjected to a preprocessing phase, during which they were transformed into the research dataset. The fusion model comprised three principal components. The initial component is the Mamba backbone network with State Space Model (SSM) at its core, which is capable of extracting pine wilt features with a high degree of efficacy. The second component is the attention network, which enables our fusion model to center on PWD features with greater efficacy. The optimal configuration was determined through an evaluation of various attention mechanism modules, including four attention modules. The third component, Path Aggregation Feature Pyramid Network (PAFPN), facilitates the fusion and refinement of data at varying scales, thereby enhancing the model’s capacity to detect multi-scale objects. Furthermore, the convolutional layers within the model have been replaced with depth separable convolutional layers (DSconv), which has the additional benefit of reducing the number of model parameters and improving the model’s detection speed. The final fusion model was validated on a test set, achieving an accuracy of 90.0%, a recall of 81.8%, a map of 86.5%, a parameter counts of 5.9 Mega, and a detection speed of 40.16 FPS. In comparison to Yolov8, the accuracy is enhanced by 7.1%, the recall by 5.4%, and the map by 3.1%. These outcomes demonstrate that our fusion model is appropriate for implementation on edge devices, such as UAVs, and is capable of effective detection of PWD.
Read more56The developmental process of xylem embolisms in pine wilt disease monitored by multipoint imaging using compact magnetic resonance imaging.
In pine wilt disease (PWD), embolized tracheids arise after virulent pine wood nematodes (PWN), Bursaphelenchus xylophilus, invade the resin canal of pine tree; infected pine trees finally die from significant loss of xylem water conduction. We used a compact magnetic resonance imaging system with a U-shaped radio frequency (rf) probe coil to reveal the developmental process of the xylem dysfunction in PWD. Multiple cross-sectional slices along the stem axis were acquired to periodically monitor the total water distribution in each 1-year-old main stem of two 3-year-old Japanese black pines (Pinus thunbergii) after inoculation of PWN. During the development of PWD, a mass of embolized tracheids around the inoculation site rapidly enlarged in all directions. This phenomenon occurred before the significant decrease of water potential. Some patch-like embolisms were observed at all monitoring positions during the experimental period. Patchy embolisms in a cross-section did not expand, but the number of patches increased as time passed. When the significant decrease of water potential occurred, the xylem dysfunctional rate near the inoculation point exceeded 70%. Finally, almost the whole area of xylem was abruptly embolized in all cross-sections along the stem. This phenomenon occurred just after water conduction was mostly blocked in one of the cross-sections. Thus, it appears that the simultaneous expansion of embolized conduit clusters may be required to induce a large-scale embolism across the functional xylem. Consequently, xylem dysfunction in infected trees may be closely related to both the distribution and the number of PWN in the pine stem.
Read more3Bayesian Network and Factor Analysis for Modeling Pine Wilt Disease Prevalence
A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times. Seven factors that influence the distribution of PWD were extracted from the QuickBird images and were used as the independent variables. The results showed that the BN model predicted PWD with high accuracy. In a sensitivity analysis, elevation (EL), the normal differential vegetation index (NDVI), the distance to settlements (DS) and the distance to roads (DR) were strongly associated with PWD prevalence, and slope (SL) exhibited the weakest association with PWD prevalence. The study showed that BN is an effective tool for modeling PWD prevalence and quantifying the impact of various factors.
Read more4Extended Spectral to Visible Comparison Based on Spectral Band Selection Method for Robust Face Recognition
Multi-spectral imaging has recently acquired significant attention in biometrics based authentication due to it’s potential ability to capture spatio-spectral images across the electromagnetic spectrum. Especially, in the case of facial biometrics, multi-spectral imaging has shown significant promising results under unknown/varying illumination environment. However, the challenge arises when surveillance cameras provide the visible images while the enrollment are spectral band images. In order to address the backward/cross compatibility of probing visible images from regular surveillance cameras against the high quality spectral band images in enrollment, development of robust algorithms are required. In this paper, we present a new approach of selecting optimal band based on highest correlation coefficients of individual feature vectors from bands in comparison with feature vectors from visible images of respective individual classes for robust recognition performance. The proposed approach of band selection is validated on a newly collected face database of 168 subjects whose face images are collected in 9 different spectral bands and correspondingly their visible images from a regular camera operating in visible spectrum. The extensive set of experiments conducted on the new database with selected single band and multiple spectral bands in enrollment data versus the visible probe image has indicated the significance of the band selection. The new approach of spectral to visible matching with the proposed band selection method shows significant Rank- 1 recognition rate of 94.04% supporting the applicability of proposed method
Read more26Remote estimation of leaf nitrogen concentration in winter oilseed rape across growth stages and seasons by correcting for the canopy structural effect
Remote estimation of leaf nitrogen concentration in winter oilseed rape across growth stages and seasons by correcting for the canopy structural effect
Read morePWD-MFS: a lightweight multispectral fusion network for detection of pine wilt disease with UAV imagery
Pine Wilt Disease (PWD) is a severe forest quarantine pest, and its effective control heavily relies on early and accurate detection. Unmanned Aerial Vehicle (UAV) technology provides a platform for large-scale monitoring, but its onboard computing resources are severely limited, imposing stringent requirements on algorithm lightweightness and efficiency. Existing methods based on UAV RGB imagery face bottlenecks in early identification and practical deployment due to limited spectral information and the difficulty in balancing computational efficiency with detection performance. To address these issues, this paper proposes a lightweight multi-spectral fusion detection model (PWD-MFS) for UAV edge computing scenarios. First, to overcome the limitations of RGB information, a multi-spectral fusion dataset is constructed. Using an improved SIFT-RANSAC-based registration method, high-precision alignment between RGB and near-infrared (840 nm) bands is achieved, resulting in spatially consistent multi-channel inputs. Second, to meet the real-time requirements of edge deployment, the model architecture integrates the hierarchical semantic design of HGNetv2 and the efficiency of Ghost convolution, significantly reducing the parameter count while maintaining a strong capacity for extracting multi-scale PWD features. Finally, a detection architecture incorporating a Lightweight Shared Convolutional Detection (LSCD) head and a Multi-Path Coordinate Attention (MPCA) module is designed. Through parameter sharing and an adaptive feature enhancement mechanism, the detection accuracy for weakly visible targets is further improved. The model contains only 3.8 M parameters and 9.5 GFLOPs, achieving a precision of 0.88, recall of 0.818, F1-score of 0.9, and mAP of 0.848 in testing, outperforming mainstream models and striking a notable balance between accuracy and efficiency. Ablation studies show that introducing the near-infrared band improves key metrics including precision, recall, F1-score, mAP by 4.0%, 3.5%, 2.8%, and 3.8%, respectively, validating the effectiveness of multi-spectral fusion for early PWD detection. This study provides a feasible technical solution for resource-constrained UAV platforms to achieve early, accurate, and real-time monitoring of Pine Wilt Disease. The code is available at https://github.com/123456WPF/PWD-MFS/tree/master
Read more1An Improved YOLOv5-based Network on Pine Wilt Disease Detection Using UAV Image
Pine is one of the important forest tree species in China, with a wide distribution and significant economic value, accounting for over 70% of China's artificial forest area. However, due to the rapid spread of pine wilt disease, a large number of pine resources have been damaged, seriously affecting ecological civilization. The most effective method currently is to promptly detect and accurately locate pine trees infected with pine wilt disease (diseased trees). Manual survey and traditional detection method based on satellite remote sensing image have limitations in monitoring complex forest areas. With the advantages of real-time monitoring, low cost, and high spatial resolution, unmanned aerial vehicle (UAV) image has been widely used for agriculture and forestry. Currently, UAV images are mostly used for monitoring diseased trees. To further improve the accuracy of identifying diseased trees, this paper proposes a diseased tree detection method based on improved YOLOv5s network, which combines the YOLOv5s algorithm with Bi-directional Feature Pyramid Network (BiFPN) and Shuffle Attention (SA). In order to demonstrate the role and mutual influence of various modules in the improved YOLOv5s network, the detection results of the improved YOLOv5s algorithm and other algorithms on the same pine wilt disease dataset are compared. The experimental results show that our improved algorithm increased 2.1% in mAP and 6.1% in recall while the quantity of parameter and GFLOPs have only increased by 3.2% and 2.5%, respectively.
Read more9A UAV-Based Energy-Efficient and Real-Time Object Detection System with Multi-Source Image Fusion
Deep-learning-based object detection is widely used in unmanned aerial vehicle (UAV) systems. However, existing methods can only perform the object detection algorithm on visible images. In many scenarios, the infrared image performs better than the visible image because it can represent more invisible features. This paper proposes an object detection algorithm using image fusion to fully use the advantages of both visible light images and infrared images. Moreover, we optimize and re-design the standard object detection algorithm, YOLO V2, to improve its performance on embedded platforms. By evaluating the performance of the proposed method, we found that the recognition accuracy rate is 97.43%, while the recognition accuracy rate of the visible image is 91.09% and the recognition accuracy rate of the infrared image is 91.39%. The experimental results indicate that the proposed method can increase the recognition accuracy rate of the visible image by 6.34% and increase the recognition accuracy rate of the infrared image by 6.04%.
Read more36A robust registration method for UAV thermal infrared and visible images taken by dual-cameras
A robust registration method for UAV thermal infrared and visible images taken by dual-cameras
20Volatile compounds in pine stands suffering from pine wilt disease: qualitative and quantitative evaluation
Pine wilt disease causes ecological and economic damage in Japanese pine forests in spite of intensive effort to protect them from the pine wood nematode, Bursaphelenchus xylophilus. Pine trees infected with B. xylophilus emit a characteristic bouquet of volatile compounds bioactive to the vector beetle of the nematode, Monochamus alternatus, and potentially affecting symptom development inside the trees. To investigate the qualitative and quantitative properties of volatile compounds in the field, we profiled the volatile emissions in two Japanese black pine stands, one naturally suffering from pine wilt disease and the other artificially inoculated with B. xylophilus. In both pine stands, the emission of some terpenoids from the infected trees such as (−)-α-pinene, began to increase in summer, overlapping the oviposition season of the vector beetle, but peaked in the summer and autumn. These data suggest that the beetles may not necessarily depend on the tremendous quantity of volatiles alone when they search for suitable trees on which to oviposit.
Read more23Pine wilt disease detection algorithm based on improved YOLOv5.
Pine wilt disease (PWD) poses a significant threat to forests due to its high infectivity and lethality. The absence of an effective treatment underscores the importance of timely detection and isolation of infected trees for effective prevention and control. While deep learning techniques combined unmanned aerial vehicle (UAV) remote sensing images offer promise for accurate identification of diseased pine trees in their natural environments, they often demand extensive prior professional knowledge and struggle with efficiency. This paper proposes a detection model YOLOv5L-s-SimAM-ASFF, which achieves remarkable precision, maintains a lightweight structure, and facilitates real-time detection of diseased pine trees in UAV RGB images under natural conditions. This is achieved through the integration of the ShuffleNetV2 network, a simple parameter-free attention module known as SimAM, and adaptively spatial feature fusion (ASFF). The model boasts a mean average precision (mAP) of 95.64% and a recall rate of 91.28% in detecting pine wilt diseased trees, while operating at an impressive 95.70 frames per second (FPS). Furthermore, it significantly reduces model size and parameter count compared to the original YOLOv5-Lite. These findings indicate that the proposed model YOLOv5L-s-SimAM-ASFF is most suitable for real-time, high-accuracy, and lightweight detection of PWD-infected trees. This capability is crucial for precise localization and quantification of infected trees, thereby providing valuable guidance for effective management and eradication efforts.
Read more10DFA-Net: Multi-Scale Dense Feature-Aware Network via Integrated Attention for Unmanned Aerial Vehicle Infrared and Visible Image Fusion
Fusing infrared and visible images taken by an unmanned aerial vehicle (UAV) is a challenging task, since infrared images distinguish the target from the background by the difference in infrared radiation, while the low resolution also produces a less pronounced effect. Conversely, the visible light spectrum has a high spatial resolution and rich texture; however, it is easily affected by harsh weather conditions like low light. Therefore, the fusion of infrared and visible light has the potential to provide complementary advantages. In this paper, we propose a multi-scale dense feature-aware network via integrated attention for infrared and visible image fusion, namely DFA-Net. Firstly, we construct a dual-channel encoder to extract the deep features of infrared and visible images. Secondly, we adopt a nested decoder to adequately integrate the features of various scales of the encoder so as to realize the multi-scale feature representation of visible image detail texture and infrared image salient target. Then, we present a feature-aware network via integrated attention to further fuse the feature information of different scales, which can focus on specific advantage features of infrared and visible images. Finally, we use unsupervised gradient estimation and intensity loss to learn significant fusion features of infrared and visible images. In addition, our proposed DFA-Net approach addresses the challenges of fusing infrared and visible images captured by a UAV. The results show that DFA-Net achieved excellent image fusion performance in nine quantitative evaluation indexes under a low-light environment.
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