Due to the increased frequency and intensity of forest damage caused by diseases and pests, effective methods are needed to accurately monitor the damage degree. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technique for forest health surveying and monitoring. In this study, a framework is proposed for identifying the severity of damage caused by Tomicus spp. (the pine shoot beetle, PSB) to Yunnan pine (Pinus yunnanensis Franch) using UAV-based hyperspectral images. Four sample plots were set up in Shilin, Yunnan Province, China. A total of 80 trees were investigated, and their hyperspectral data were recorded. The spectral data were subjected to a one-way ANOVA. Two sensitive bands and one sensitive parameter were selected using Pearson correlation analysis and stepwise discriminant analysis to establish a diagnostic model of the damage degree. A discriminant rule was established to identify the degree of damage based on the median value between different degrees of damage. The diagnostic model with R₆₉₀ and R₇₉₈ as variables had the highest accuracy (R² = 0.854, RMSE = 0.427), and the test accuracy of the discriminant rule was 87.50%. The results are important for forest damage caused by the PSB.

It remains challenging to control Tomicus spp., a pest with fast spreading capability, leading to the death of large numbers of Pinus yunnanensis (Franch.) and posing a severe threat to ecological security in southwest China. Therefore, it is crucial to effectively and accurately monitor the damage degree for Pinus yunnanensis attacked by Tomicus spp. at large geographical scales. Airborne hyperspectral remote sensing is an effective, accurate means to detect forest pests and diseases. In this study, we propose an innovative and precise classification framework to monitor the damage degree of Pinus yunnanensis infected by Tomicus spp. using hyperspectral UAV (unmanned aerial vehicle) imagery with machine learning algorithms. First, we revealed the hyperspectral characteristics of Pinus yunnanensis from a UAV-based hyperspectral platform. We obtained 22 vegetation indices (VIs), 4 principal components, and 16 continuous wavelet transform (CWT) features as the damage degree sensitive features. We classified the damage degree of Pinus yunnanensis canopies infected by Tomicus spp. via three methods, i.e., discriminant analysis (DA), support vector machine (SVM), and backpropagation (BP) neural network. The results showed that the damage degree detected from the BP neural network, combined with 16 CWT features, achieved the best performance (training accuracy: 94.05%; validation accuracy: 94.44%).

Pine wilt disease endangers the ecological stability of China’s coniferous woodlands. In a specific region, the number of dead pine trees has exhibited a consistent year-on-year increase, highlighting the urgent need for efficient and sustainable monitoring strategies. However, UAV-based remote sensing methods currently face challenges in complex environments, including insufficient feature-capture capabilities, interference from visually similar objects, and limited localization accuracy. This study developed a remote sensing workflow leveraging high-resolution UAV imagery to oversee pine trees affected with pine wilt disease. An enhanced YOLOv5 detection model was employed to identify symptomatic trees. To strengthen feature extraction capabilities—particularly for color and texture traits indicative of infection—different types of attention mechanisms, for instance SE, CBAM, ECA, and CA, were integrated as part of the model. Furthermore, a BiFPN structure was incorporated to enhance the fusion of features across multiple scales, and the EIoU loss function was adopted to boost the accuracy of bounding box prediction, ultimately enhancing detection precision. Experimental results show that the enhanced SEBiE-YOLOv5 framework achieved a precision of 89.4%, with an AP of 86.1% and an F1-score of 83.1%. UAV-based monitoring conducted during the spring and autumn of 2023 identified 616 dead trees, with field verification accuracy ranging from 88.91% to 92.42% and localization errors within 1–10 m. These findings validate the method’s high accuracy and spatial precision in complex mountainous forest environments. By integrating attention mechanisms, BiFPN, and the EIoU loss function, the proposed SEBiE-YOLOv5 model substantially enhances the recognition accuracy of key features in infected trees as well as their localization performance, and offers a practical and computationally efficient approach for the long-term surveillance of pine wilt disease in challenging terrain.

The outbreak of Pine Shoot Beetle (PSB, Tomicus spp.) posed a significant threat to the health of Yunnan pine forests, necessitating the development of an efficient and accurate remote sensing monitoring method. The integration of unmanned aerial vehicle (UAV) imagery and deep learning algorithms shows great potential for monitoring forest-damaged trees. Previous studies have utilized various deep learning semantic segmentation models for identifying damaged trees in forested areas; however, these approaches were constrained by limited accuracy and misclassification issues, particularly in complex forest backgrounds. This study evaluated the performance of five semantic segmentation models in identifying PSB-damaged trees (UNet, UNet++, PAN, DeepLabV3+ and FPN). Experimental results showed that the FPN model outperformed the others in terms of segmentation precision (0.8341), F1 score (0.8352), IoU (0.7239), mIoU (0.7185) and validation accuracy (0.9687). Under the pure Yunnan pine background, the FPN model demonstrated the best segmentation performance, followed by mixed grassland-Yunnan pine backgrounds. Its performance was the poorest in mixed bare soil-Yunnan pine background. Notably, even under this challenging background, FPN still effectively identified diseased trees, with only a 1.7% reduction in precision compared to the pure Yunnan pine background (0.9892). The proposed method in this study contributed to the rapid and accurate monitoring of PSB-damaged trees, providing valuable technical support for the prevention and management of PSB.

To investigate the effects of the endophytic fungus Setophoma terrestris (isolated from Panax notoginseng roots) on the growth and rhizosphere microbiota of understory-cultivated P. notoginseng, we prepared liquid and solid fermentates of the fungus and applied them separately via irrigation. Rhizosphere soil of P. notoginseng was subjected to non-targeted metabolomics and microbiome sequencing for detection and analysis. Relative to the control, P. notoginseng treated with liquid and solid fermentates exhibited increases in plant height (3.5% and 0.7%), chlorophyll content (23.4% and 20.4%), and total saponin content (14.6% and 17.0%), respectively. Non-targeted metabolomics identified 3855 metabolites across 23 classes, with amino acids and their derivatives (21.54%) and benzene derivatives (14.21%) as the primary components. The significantly altered metabolic pathways shared by the two treatment groups included ABC transporters, purine metabolism, and the biosynthesis of various other secondary metabolites. Exogenous addition of S. terrestris significantly affected the composition of the rhizosphere soil microbial community of P. notoginseng and increased the relative abundance of genera such as Bradyrhizobium. In conclusion, the endophytic fungus S. terrestris enhances P. notoginseng growth and modulates both rhizosphere soil metabolites and microbial abundance. This study can provide certain data support for research on endophytic fungi of P. notoginseng.

To explore superior biocontrol resources for Rhododendron brown spot disease, five antagonistic fungal strains exhibiting significant inhibitory activity against the pathogen responsible for RBS were isolated from healthy Rhododendron hybridum Ker Gawl leaves. Among them, strain DJW5-2-1 demonstrated the highest inhibition rate, reaching 63.88% against the pathogenic fungus. Based on morphological characteristics and multigene phylogenetic analysis (ITS, β-tubulin, and tef1-α), DJW5-2-1 was identified as Diaporthe phoenicicola (Traverso & Spessa) Udayanga, Crous & K.D. Hyde. Dual culture assays further confirmed its broad-spectrum antifungal activity, with inhibition rates ranging from 39.15% to 72.54% against various phytopathogenic fungi. Biochemical analyses revealed that DJW5-2-1 secretes multiple extracellular enzymes and exhibits plant growth-promoting traits. In both in vitro and potted plant efficacy assays, the biocontrol efficacy of strain DJW5-2-1 against RBS was 49.67% and 50.61%, respectively, indicating that strain DJW5-2-1 exhibits a certain level of control efficacy against RBS. Through pot experiments, we found that strain DJW5-2-1 could promote the growth of rhododendron seedlings and significantly increase growth indicators. Among these indicators, the growth-promoting rates of plant height and stem diameter were 15.27% and 41.27%, respectively. Moreover, DJW5-2-1 contributed to improved host resistance by elevating the activities of key defense-related enzymes, including superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and polyphenol oxidase (PPO). Taken together, these findings suggest that strain DJW5-2-1 represents a promising microbial agent for the integrated control of RBS and the development of fungal-based biofertilizers. Further investigation is warranted to assess its performance under field conditions and elucidate its underlying mechanisms of action.