Pine wilt disease detection algorithm based on improved YOLOv5.

Pine wilt disease (PWD) is a forest disease characterized by rapid spread and extremely high lethality, posing a serious threat to the ecological security of China’s forests and causing significant economic losses in forestry. Given the extensive forestry area, limited personnel for inspection and monitoring, and high costs, utilizing UAV-based remote sensing monitoring for diseased trees represents an effective approach for controlling the spread of PWD. However, due to the small target size and uneven scale of pine wilt disease, as well as the limitations of real-time detection by drones, traditional disease tree detection algorithms based on RGB remote sensing images do not achieve an optimal balance among accuracy, detection speed, and model complexity due to real-time detection limitations. Consequently, this paper proposes Light-ViTeYOLO, a lightweight pine wilt disease detection method based on Vision Transformer-enhanced YOLO (You Only Look Once). A novel lightweight multi-scale attention module is introduced to construct an EfficientViT feature extraction network for global receptive field and multi-scale learning. A novel neck network, CACSNet(Content-Aware Cross-Scale bidirectional fusion neck network), is designed to enhance the detection of diseased trees at single granularity, and the loss function is optimized to improve localization accuracy. The algorithm effectively reduces the number of parameters and giga floating-point operations per second (GFLOPs) of the detection model while enhancing overall detection performance. Experimental results demonstrate that compared with other baseline algorithms, Light-ViTeYOLO proposed in this paper has the least parameter and computational complexity among related algorithms, with 3.89 MFLOPs and 7.4 GFLOPs, respectively. The FPS rate is 57.9 (frames/s), which is better than the original YOLOv5. Meanwhile, its mAP@0.5:0.95 is the best among the baseline algorithms, and the recall and mAP@0.5 slightly decrease. Our Light-ViTeYOLO is the first lightweight method specifically designed for detecting pine wilt disease. It not only meets the requirements for real-time detection of pine wilt disease outbreaks but also provides strong technical support for automated forestry work.

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.

Pine Wilt Disease (PWD) is a devastating global forest disease that spreads rapidly and causes severe ecological and economic losses. Drone remote sensing imaging technology is an effective way to detect PWD and control its spread. However, the existing algorithms for detecting PWD using drone images have low recognition accuracy, difficult image calibration, and slow detection speed. We propose a fast detection algorithm for PWD based on an improved YOLOv8 model. The model first adds a small object detection layer to the Neck module in the YOLOv8 base framework to improve the detection performance of small diseased pine trees and then inserts three attention mechanism modules on the backbone network to extend the sensory field of the network to enhance the extraction of image features of deep diseased pine trees. To evaluate the proposed algorithm framework, we collected and created a dataset in Weihai City, China, containing PWD middle-stage and late-stage infected tree samples. The experimental results show that the improved YOLOv8s-GAM model achieves 81%, 67.2%, and 76.4% optimal detection performance on mAP50, mAP50-95, and Mean evaluation metrics, which is 4.5%, 4.5%, and 2.7% higher than the original YOLOv8s model. Our proposed improved YOLOv8 model basically meets the needs of large-scale PWD epidemic detection and can provide strong technical support for forest protection personnel.

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.

Pine wilt disease (PWD) is a worldwide affliction that poses a significant menace to forest ecosystems. The swift and precise identification of pine trees under infection holds paramount significance in the proficient administration of this ailment. The progression of remote sensing and deep learning methodologies has propelled the utilization of target detection and recognition techniques reliant on remote sensing imagery, emerging as the prevailing strategy for pinpointing affected trees. Although the existing object detection algorithms have achieved remarkable success, virtually all methods solely rely on a Digital Orthophoto Map (DOM), which is not suitable for diseased trees detection, leading to a large false detection rate in the detection of easily confused targets, such as bare land, houses, brown herbs and so on. In order to improve the ability of detecting diseased trees and preventing the spread of the epidemic, we construct a large-scale PWD detection dataset with both DOM and Digital Surface Model (DSM) images and propose a novel detection framework, DDNet, which makes full use of the spectral features and geomorphological spatial features of remote sensing targets. The experimental results show that the proposed joint network achieves an AP50 2.4% higher than the traditional deep learning network.

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

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.

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.

In order to recognize pine wood affected by pine wilt disease (PWD) efficiently, a dataset of infected pine wood was constructed and a recognition algorithm YOLOv5-PWD based on YOLOv5 was designed. In the mountainous area of Fushun City, Liaoning Province, an unmanned aerial vehicle (UAV) was used to collect images of infected pine wood at three different heights of 200/220m, 100m and 60/80m, and the images were expanded to establish an infected pine wood dataset. Based on the YOLOv5 framework, combined with Moasic data enhancement, Focus unit and spatial pyramid pooling (SPP) unit, a recognition algorithm for pine wood affected by pine wilt disease was designed. The experimental results show that the mean average precision of the dataset at the 100m collection height is the highest. Compared with the Faster R-CNN algorithm, the mean average precision of YOLOv5-PWD is increased by 16.8%, and the recognition speed is increased by 64.5%.

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.

Pine Wilt Disease (PWD) is a highly infectious and lethal disease that severely threatens global pine forest ecosystems and forestry economies. Early and accurate detection of infected trees is crucial to prevent large-scale outbreaks and support timely forest management. However, existing remote sensing-based detection models often struggle with performance degradation in complex environments, as well as a trade-off between detection accuracy and real-time efficiency. To address these challenges, we propose an improved object detection model, YOLOv8-MFD, designed for accurate and efficient detection of PWD-infected trees from UAV imagery. The model incorporates a MobileViT-based backbone that fuses convolutional neural networks with Transformer-based global modeling to enhance feature representation under complex forest backgrounds. To further improve robustness and precision, we integrate a Focal Modulation mechanism to suppress environmental interference and adopt a Dynamic Head to strengthen multi-scale object perception and adaptive feature fusion. Experimental results on a UAV-based forest dataset demonstrate that YOLOv8-MFD achieves a precision of 92.5%, a recall of 84.7%, an F1-score of 88.4%, and a mAP@0.5 of 88.2%. Compared to baseline models such as YOLOv8 and YOLOv10, our method achieves higher accuracy while maintaining acceptable computational cost (11.8 GFLOPs) and a compact model size (10.2 MB). Its inference speed is moderate and still suitable for real-time deployment. Overall, the proposed method offers a reliable solution for early-stage PWD monitoring across large forested areas, enabling more timely disease intervention and resource protection. Furthermore, its generalizable architecture holds promise for broader applications in forest health monitoring and agricultural disease detection.

Application of bio-based nanopesticides for pine surface retention and penetration, enabling effective control of pine wood nematodes

This study investigates the spatiotemporal dynamics of pine wilt disease (PWD) to inform data-driven management. We implemented a time-series monitoring framework in a township in Zhejiang Province, China, acquiring seven sequences of unmanned aerial vehicle (UAV) orthomosaics between 2022 and 2024. An enhanced YOLOX-based change detection model was developed and trained on 300 000 samples. This model exploits phenological variations to automatically identify PWD-discolored pines, effectively filtering confounding objects. The model demonstrated robust performance, achieving an Average Precision (AP) of 0.89, with Precision and Recall exceeding 85%. Analysis revealed a consistent westward expansion and a progressive increase in disease hotspots. Crucially, winter surveys detected substantial delayed-symptom pines missed in autumn, roughly equivalent to the autumn baseline. Consequently, the annual cumulative mortality caused by PWD was nearly double (2×) the autumn count. Over 90% of trees newly identified in autumn were located within 300 m of infections detected the previous spring, indicating strong spatial clustering. Furthermore, 80% of infected trees occurred at elevations < 400 m and slopes < 29°, aligning with prevailing easterly winds. This research establishes a validated framework bridging remote sensing and on-the-ground sanitation. By quantifying the symptom lag effect (which doubles mortality estimates relative to traditional autumn surveys) and elucidating environmentally driven spread mechanisms, we provide a scientific basis for correcting census biases and optimizing resource allocation for precise PWD management. © 2026 Society of Chemical Industry.

Remote sensing images of unusual dead pines were acquired at the heights of 430m and 700m by UAV (unmanned aerial vehicle) equipped with the visible light camera.The geographic location information collected by GPS was compared with the data processing information of ENVI software and the data processing information of interpretation.Six dead pine trees were identified by artificial screening and seven dead pine trees were identified by the NDVI value.Data extraction efficiency was 85.7%.The horizontal error was between 0.86 m and 4.20 m for geographical location information of the six dead pine trees obtained by remote sensing.The results showed that unmanned aerial vehicle remote sensing had basically realized the accurate location of pine trees dying from the pine wood nematode disease,which was of great significance for the early monitoring and later treatment of the pine wood nematode disease.

In recent years, unmanned aerial vehicles (UAV) have been increasingly used in power line inspections. Birds often nest on transmission line towers, which threatens safe power line operation. The existing research on bird’s nest inspection using UAVs mainly stays at the level of image postprocessing detection, which has poor real-time performance and cannot obtain timely bird’s nest detection results. Considering the above shortcomings, we designed a power inspection UAV system based on deep learning technology for autonomous flight, positioning and photography, real-time bird nest detection, and result export. In this research, 2000 bird’s nest images in the actual power inspection environment were shot and collected to create the dataset. The parameter optimization and test comparison for bird’s nest detection are based on the three target detection models of YOLOv3, YOLOv5-s, and YOLOX-s. A YOLOv5-s bird’s nest detection model optimized for bird’s nest real-time detection is proposed, and it is deployed to the onboard computer for real-time detection and verification during flight. The DJI M300 RTK UAV was used to conduct a test flight in a natural power inspection environment. The test results show that the mAP of the UAV system designed in this paper for bird’s nest detection is 92.1%, and the real-time detection frame rate is 33.9 FPS. Compared with the previous research results, this paper proposes a new practice of using drones for bird’s nest detection, dramatically improving the real-time accuracy of bird’s nest detection. The UAV system can efficiently complete the task of bird’s nest detection in the process of electric power inspection, which can significantly reduce manpower consumption in the power inspection process.