Hierarchical attention and feature enhancement network for multi-scale small targets in pine wilt disease

PWD-lightweight and feature fusion network for multi-stage joint detection of pine wilt disease

Lightweight vision transformer model for pine wilt disease detection using aerial RGB image and adversarial data augmentation

Introduction Pine wilt disease (PWD) is recognized as a destructive forest disease worldwide, leading to massive mortality of many Pinus spp., including the Korean white pine Pinus koraiensis . Current work has focused on underlying development of this disease occurring aboveground, but few studies have assessed soil consequences from the destruction in pine forest by PWD. Methods In this study, we collected soil samples from one stand of PWD-resistant species Larix olgensis , and from four stands of PWD-susceptible P. koraiensis ( n = 8) following a natural chronosequence of PWD development (healthy, diseased, killed, and clear-cut P. koraiensis ). We aimed to investigate the shifts in soil microbial and nematode communities under the canopy of P. koraiensis over the PWD progression. Results The α-diversity e.g., species richness of bacterial community in soil of healthy P. koraiensis was ca. 17% lower than in soil of diseased pines. The species richness of fungal community in the soil of healthy P. koraiensis was also 24.5% lower than in soil of killed pines. The diseased and killed pines also exhibited different compositions in soil microbial community from the healthy pines, although these damaged trees did not differ themselves in the composition. In particular, the relative abundance of the methane-cycling Methylomirabilota became higher in bacterial community and the ectomycorrhizal Agaricomycetes was lower in fungal community in soil of the diseased or killed pines than healthy ones, suggesting an overall decrease in soil health caused by PWD. Although the α-diversity of soil nematode community did not vary over the development of PWD, its composition was significantly altered by the disease. Consequently, we observed a lower inter-kingdom network complexity in the soil community of the pines following the PWD, in which the bacterial networks decreased but fungal networks increased in complexity. The nematode community also showed a lower network complexity in soil of PWD-destructed pines, albeit that this only occurred when the pines were diseased rather than killed. Discussion By recording the structure dynamics of soil microbial and nematode communities in pines following the progression of PWD, this study helps to understand the impacts of PWD on soil biotic processes, thus providing an important reference for better assessing the ecological consequences of this devastating disease.

Pine wood nematode disease (PWD) is one of the most devastating forest diseases worldwide, often described as the “cancer” of pine trees due to its rapid and large-scale lethality. Early and accurate detection of infected trees is essential for interrupting the transmission cycle and mitigating the risk of further spread. However, current monitoring methods suffer from limited efficiency and insufficient precision. To address these challenges, this study introduces PWD-YOLO-D, an intelligent detection model for PWD based on unmanned aerial vehicle (UAV) remote sensing imagery and the YOLOv8 deep learning framework. The proposed model integrates an Efficient Multi-scale Cross-Attention (EMCA) mechanism to enhance feature representation across multiple scales and heterogeneous backgrounds; incorporates a Self-Ensemble Attention Module (SEAM) as the detection head to improve robustness in identifying occluded and overlapping diseased crowns; and adopts the Focaler-IoU loss function to refine localization accuracy and improve discrimination of complex samples. Experimental results indicate that the improved PWD-YOLO-D model outperforms the original YOLOv8 by 4.0% points in AP@0.5 and 7.3% points in AP@0.5:0.95, while reducing the Parameters by 0.48 MB. These enhancements provide strong technical support and data-driven evidence for the timely detection and precise management of infected pine trees.

BackgroundAcetylation is a widely occurring post-translational protein modification in animals, plants, and microorganisms. Histone acetyltransferase (HATs) are positive regulators of acetylation and are responsible for the growth, development, and virulence of pathogens. The pine wood nematode (PWN) Bursaphelenchus xylophilus has caused extensive pine tree mortality, leading to significant loss in Asia; however, there is no HATs have been characterized in PWN.ResultsIn the present study, 7 HATs in PWN were identified and grouped into 3 subfamilies with conserved protein structures and motif compositions. The RT-PCR analysis revealed that HAT expression levels varied across developmental, temperature, and infection stages in pines. After the interference of BxPCAF and BxElp3, two acetyltransferase genes belonging to the GANT subfamily, the reproductive number and the pathogenicity of PWN decreased.ConclusionsWe identified 7 HATs belonging to 3 subfamilies in PWN, and these proteins may play specific roles in undergrowth, environmental stress, and host invasion and are associated with the growth and pathogenicity of PWN.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12864-025-12175-8.

Pine wilt disease (PWD), caused by the invasive nematode Bursaphelenchus xylophilus, poses a growing threat to East Asian coniferous forests, which is further exacerbated by climate change. While studies have successfully applied Maximum Entropy (MaxEnt) models to map the potential spread of PWD, they have primarily focused on broad spatial scales and climatic factors. This highlights the need for fine-scale, integrative modeling approaches that also account for environmental and anthropogenic factors. Therefore, we applied the MaxEnt model combined with change vector analysis to evaluate the spatial risk and potential future spread of PWD in Andong-si, Republic of Korea, under the SSP1-2.6 climate scenario. We integrated forest structure, soil conditions, topography, climate variables, and anthropogenic factors to generate high-resolution risk maps and identify the most influential environmental drivers. Notably, we demonstrated that historical infection proximity and isothermality strongly influence habitat suitability. We also, for the first time, projected an eastward shift of high-risk areas in Andong-si under future climate conditions. These findings provide timely insights for designing proactive surveillance networks, implementing risk-based monitoring, and developing climate-resilient management strategies. Our integrative modeling framework offers decision-support tools that can enhance early detection and targeted interventions against invasive forest pests under environmental change.

Dynamics of a pine wilt disease control model with nonlocal competition and memory diffusion.

Pine wilt disease, instigated by the Bursaphelenchus xylophilus (also called pine wood nematode [PWN]), poses a significant threat to coniferous forests across the globe, leading to widespread tree mortality and ecological disruption. While Japanese larch (Larix kaempferi) is a natural host of PWN, the molecular basis of its responses remains poorly understood. Here, we developed a callus-based parenchymal sentinel (CaPS) system mimicking xylem parenchyma-nematode interactions to bypass multi-tissue interference in traditional sapling studies. After 5 days of PWN inoculation, nematode proliferated 2.85-fold, while the callus exhibited water-soaked lesions and reduced cell viability, indicating a rapid defense activation. (i) Transcriptome analysis revealed 8515 differentially expressed genes related to chitinase signaling, calcium-regulated immunity and antimicrobial compound synthesis. (ii) Metabolomic analysis identified 389 defense-related metabolites (e.g., alkaloids). (iii) Integration of omics data uncovered 71 coordinated pathways categorized into eight functional groups, including reactive oxygen species burst and mitogen-activated protein kinase, and they formed a multi-layered defense network. Importantly, this CaPS system enabled 5-day phenotyping cycles of transgenic callus, significantly accelerating evaluation compared with traditional sapling methods. Our work reveals early-stage conifer immunity against PWN and establishes an accelerated evaluation program for future screening of transgenic callus and breeding resistant larch varieties.

Sugar-inducible promoters mitigate the fitness cost of engineered Serratia marcescens in the control of Monochamus alternatus.

Beauveria bassiana ERL836-mediated suppression of oxidative phosphorylation and immune response in fat body of Japanese pine sawyer beetle.

Enhanced pine wilt disease outbreak prediction: Integrating deep learning- detected infected trees with species distribution modeling

Enhancing pine wilt disease detection with synthetic data and external attention-based transformers

Pine wilt disease (PWD) poses a severe threat to forest ecosystems due to its high infectivity and destructive nature. Early identification of PWD-infected pines is critical to curbing disease spread and safeguarding forest resources. In order to timely detect and prevent the spread of PWD and meet the requirements of edge computing devices for real-time performance and computational efficiency, this paper proposes a lightweight model LW-PWDNet. The backbone network reconstructs HGNetV2 to achieve efficient feature extraction. It decomposes traditional convolutions into more lightweight feature generation and transformation operations, reducing computational cost while retaining discriminative power. The feature fusion layer reconstructs the path aggregation network based on RepBlock and multi-scale attention mechanism, capturing fine-grained details of small lesions, so as to better capture the detailed features of small targets. At the same time, this paper designs a lightweight D-Sample down-sampling module in the feature fusion layer to further improve the model's detection ability for multi-scale targets. Finally, this paper designs a lightweight prediction layer LightShiftHead for this model. By strengthening the local feature expression, the detection accuracy of PWD in small targets is further improved. A large number of experimental results show that LW-PWDNet maintains a high detection accuracy of mAP 89.7%, while achieving low computational complexity of 5.6 GFLOPs and only 1.9M parameters, as well as a high inference speed of 166 FPS when tested on an NVIDIA RTX 4070 GPU with a 13th Gen Intel(R) Core(TM) i7-13700KF processor, using PyTorch 2.0.1 and CUDA 12.6, based on Python 3.9. This model can provide an efficient and lightweight detection solution for PWD in resource-constrained scenarios such as unmanned aerial vehicle inspections.

Bursaphelenchus xylophilus, the pine wood nematode (PWN), is responsible for causing pine wilt disease (PWD), leading to significant ecological and economic damage in pine forests. Endophytes are well-known producers of bioactive secondary metabolites, offering potential for environmentally friendly control agents. This study focused on identifying and evaluating nematicidal compounds from pine tree endophytes active against PWN. A total of 30 endophytes isolated from Korean pine trees were screened for their nematicidal activity and identified as novel nematicidal agents against PWN. Among them, Streptomyces sp. AN140557 showed the highest nematicidal activity. The active compound, murayaquinone, was isolated by nematicidal activity-guided fractionation, and it showed significant dose-dependent nematicidal activity and egg-hatching inhibition against PWN at 3.12, 6.25, 12.5, and 25 μM, respectively. Exposure to 25 μM murayaquinone led to 100% mortality of PWN. Additionally, murayaquinone strongly suppressed egg hatching. In addition, the assessment of the activity against the other four plant parasitic nematodes (Meloidogyne incognita, Ditylenchus destructor, Aphelenchoides subtenuis, and Heterodera trifolii) showed that it has a broad nematicidal spectrum. The greenhouse experiments suggested that the murayaquinone efficiently inhibited the development of PWD in 5-year-old Pinus thunbergii plants. Our results highlight the nematicidal potential of the murayaquinone derived from Streptomyces sp. AN140557. This is the first report of murayaquinone exhibiting biocontrol potential against PWN, suggesting its possibility as a PWD control agent. © 2025 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Pine wood nematodes (PWD) cause significant threat to ecosystems and forestry economies, and the long-term use of commercial fungicides has led to environmental pollution and ecological imbalance. Therefore, it is urgent to develop effective and eco-friendly plant-based derivatives to control PWD. A series of rosin-based nematicides were prepared, and their insecticidal activity was evaluated on pine wood nematodes. The result indicated that dehydroabietic acid-based amide derivatives-3q (DAAD-3q) exhibited the highest nematocidal activity, with the half-lethal concentration (LC50) of 95.9 μg/mL. Quantum chemical calculations revealed that the amide bond in DAAD-3q enhances binding to target sites. Physiological studies showed that DAAD-3q disrupted pine nematode microstructure, induced oxidative stress, and reduced glutathione S-transferase (GST) enzyme activities. Molecular docking revealed various interaction forces between DAAD-3q and GST, including hydrogen bonding and hydrophobic interactions. Metabolomic analysis indicated that DAAD-3q interferes with energy and lipid metabolism, while transcriptomic profiling showed alterations in genes related to development, metabolism, and antioxidant pathways. These molecular perturbations are associated with increased reactive oxygen species (ROS) production and decreased GST enzymatic activities, ultimately contributing to the death of pine wood nematodes. This research successfully prepared an efficient nematicide agent and explored its action mechanism, providing theoretical guidance for the green prevention and control of pine wood nematodes. © 2025 Society of Chemical Industry.

Pine Wilt Disease (PWD) is one of the most destructive forest infectious diseases affecting pine trees. Although infected pine trees exhibit subtle physiological changes in the early stages, it is difficult to detect these changes in a timely manner using spectral reflectance alone. Consequently, accurately identifying early-infected pine forests remains a major challenge for disease monitoring. This study explored the physiological and biochemical response mechanisms of PWD at different infection stages and confirmed the application potential of chlorophyll fluorescence combined with sensitive spectral bands for early diagnosis. The results indicate that in the early stage of PWD, minimal fluorescence (Fo) significantly decreases, while non-photochemical quenching (NPQ) increases. These parameters can serve as sensitive indicators for early disease diagnosis. Compared with using only sensitive bands, combining chlorophyll fluorescence parameters with the least absolute shrinkage and selection operator (LASSO) selected bands significantly improved the K-nearest neighbors (KNN) model's classification performance, resulting in a 10% increase in overall accuracy and a 29% increase in early-stage precision. The LASSO-selected bands combined with chlorophyll fluorescence parameters demonstrated optimal performance in the support vector machine (SVM) model, achieved an overall accuracy of up to 96%, with an early-stage precision at 91%. This study determined key chlorophyll fluorescence parameters and diagnostic spectral bands for early detection, and proposed a novel strategy of combining sensitive bands with chlorophyll fluorescence for early identification, effectively overcoming the technical bottleneck in early-stage detection of traditional single-modality data. © 2025 Society of Chemical Industry.

To improve the detection accuracy and efficiency of avermectin pesticide residues in pine trees, this study develops an analysis method based on MSPE combined with high-performance liquid chromatography tandem mass spectrometry. A core–shell magnetic nanomaterial, Fe3O4-SiO2-NH2-Schiff-TAPB-DA, is synthesized by the solvothermal method. The Fourier Transform Infrared Spectroscopy (FTIR) analysis results confirmed its stable characteristic functional group structure (such as benzene ring, amide bond) and significant magnetic separation performance. Efficient adsorption of avermectin was achieved by utilizing the abundant benzene ring and amide bond sites on the material surface, and the solid-phase extraction pretreatment conditions were optimized. The results demonstrated that the target substance exhibited good linearity under optimal extraction conditions (R2 > 0.998). The matrix effects of standardized avermectin pesticides were all greater than 92%, and the Limit of Detection (LOD) for methomyl, avermectin, and tetracycline were 0.213, 0.185, and 0.209 mg L-1. The average recovery rate of some target substances was 73.2–85.6%. This study presents an efficient and environmentally friendly technical solution for the precise monitoring of avermectin residues in the prevention and control of pine wilt disease, providing a methodological reference for the multi-residue analysis of other forestry pesticides.

Xylophagous insects, like Monochamus saltuarius significantly affect tree-stem-associated microbial communities and pose major threats to forest ecosystems. As a vector for the pinewood nematode (Bursaphelenchus xylophilus) and a wood-boring pest, M. saltuarius infests various Pinus species. Yet, the interactions between M. saltuarius-associated fungi and the endophytic fungi of its host tree, Pinus koraiensis, remain uncharacterized. In this study, high-throughput sequencing was used to characterize fungal communities within infested and uninfested host trees. Endophytic fungi with lignocellulose-depolymerizing potential were identified, and their enzymatic activity was experimentally assessed. A significant reduction in fungal diversity was detected in infested samples, indicating that M. saltuarius infestation disrupts the native fungal community, favoring plant pathogens and diminishing the abundance of lignocellulosic depolymerizing fungi. To explore ecological shifts and diagnostic taxa, an integrated biomarker discovery framework combining Random Forest and LEfSe analysis was applied. This model revealed fungal biomarkers enriched in uninfested tissues and potentially involved in lignocellulose degradation based on functional annotation and in vitro assays. These taxa may serve as indicators of infestation status and provide insight into host-microbe-insect interactions. These findings contribute to understanding how xylophagous insect infestations restructure fungal communities and offer a model-based approach for detecting ecological signatures of pest impact in forest ecosystems.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s12866-025-04269-w.

Monochamus saltuarius is an important wood-boring pest of forests and a vector insect for the transmission of Bursaphelenchus xylophilus in China and other East Asian regions. To gain insight into the Mo. saltuarius olfactory system, we characterized the sizes and morphological characteristics of sensilla on antennae, maxillary palps, and labial palps of adults by scanning electron microscopy. Eight types of antennal sensilla were identified on the antennae: Böhm bristles (BBs), sensilla chaetica (SChs, with subtypes SChI and SChII), sensilla trichodea (STs, with subtypes STI, STII and STIII), sensilla auricillica (SAus), sensilla basiconica (SBs, with subtypes SBI and SBII), sensilla grooved peg (SGPs), dome shaped organs (DSOs), and cuticular pores (CPs); among these, BBs, STIs, STIIs, SChIs, and SChIIs may be mechanoreceptors, and STIIIs, SAus, SBIs, SBIIs, SGPs and CPs may be chemoreceptors. Seven sensillum types were identified on maxillary palps and labial palps: BBs, STs (with subtypes STII, and STIII), SChs, sensilla placodea (SPs), sensilla coeloconica (SCos), CPs, and sensilla twig basiconica (STBs, with subtypes STBI, STBII, STBIII, and STBIV), among which BBs, STIIs, and SChs may be mechanoreceptors, and STIIIs, SPs, CPs, STBIs, STBIIs, STBIIIs, and STIVs may be chemoreceptors. DSOs on the antennae and SCos on the palps may be hydroreceptors, and/or thermoreceptors. The types and densities of sensilla increased from the base to the tip of the antennae, and sensilla with chemical-sensing functions were concentrated mostly on the flagellum. Identification of these sensillum types provides a basis for analyzing the mechanisms of host recognition and environmental perception of Mo. saltuarius.