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%).

Cephalcia chuxiongica has caused significant damage to pine forests, becoming a major biological disaster that hinders the sustainable development of forestry in China. To investigate the efficacy of biological control measures, entomopathogenic fungi were isolated and purified from the larvae of Ce. chuxiongica that had succumbed to diseases. The pathogenic capacity of strains was assessed using bioassay methods, and their infection process was observed using scanning electron microscopy. ITS, LSU, and TEF analysis disclosed Clonostachys rogersoniana as the highly virulent strain responsible for the death of Ce. chuxiongica. The optimal medium for its mycelial growth and sporulation was found to be PPDA. In addition, the bioassay revealed that the median lethal time (LT₅₀) for Ce. chuxiongica was 24.34 h and median lethal concentration (LC₅₀) was 2.35 × 10⁵ conidia/mL, indicating that C. rogersoniana possesses potent virulence and demonstrates rapid pathogenicity. Furthermore, scanning electron microscopy demonstrated that C. rogersoniana initially entered the body of Ce. chuxiongica through the spiracle and progressively made its way into the body wall, resulting in the insect’s death. The mode of infection for C. rogersoniana is exceedingly rare. As a consequence, the results of this study can serve as a reference for the management of chewing insects, such as Ce. chuxiongica.

Impatiens hawkeri, a plant of great horticultural significance with high ornamental and economic value, is frequently afflicted by leaf spot disease caused by Stagonosporopsis cucurbitacearum, and effective biological control methods for this disease are yet to be fully explored. To address this issue, the plate confrontation method was used to isolate five antagonistic fungal strains from Quercus spinosa, which showed strong inhibitory effects against S. cucurbitacearum with inhibition rates of 53.12% for CG6, 43.42% for CG7, 68.13% for CY12, 54.30% for CJ18, and 67.23% for CJ19, respectively. Among them, strain CY12 demonstrated the highest antifungal activity, significantly suppressing the mycelial growth of S. cucurbitacearum. Through morphological observation and sequence analyses of ITS, TEF, and RPB2, CY12 was identified as Fusarium solani. Further experiments revealed that CY12 could produce cell wall hydrolases and exhibited multiple growth-promoting properties, such as phosphorus and potassium solubilization, nitrogen fixation, and indoleacetic acid (IAA) production, and it also produced various cell wall-degrading enzymes like amylase, cellulase, and β-glucanase. Additionally, CY12 showed strong antagonistic effects against six common ornamental plant pathogens, with an inhibition rate of 71.41% against the pathogen causing Rhododendron leaf spot disease. Pot experiments indicated that CY12 effectively reduced the occurrence of leaf spot disease on I. hawkeri plants, increased the seed germination rate from 93% in the control group to 98.33%, and promoted seedling growth. These results suggest that the endophytic fungus CY12 has strong antifungal and growth-promoting properties, providing potential strain resources for the biological control of leaf spot disease in I. hawkeri and the development of fungal fertilizers, and further exploration of its field application effects and action mechanisms is necessary.

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.

Rhododendron hybridum Ker Gawl, a widely cultivated horticultural species in China, is highly valued for its ornamental and medicinal properties. However, with the expansion of its cultivation, leaf spot disease has become more prevalent, significantly affecting the ornamental value of R. hybridum Ker Gawl. In this study, R. hybridum Ker Gawl from the Kunming area was selected as the experimental material. The tissue isolation method was employed in this study to isolate pathogenic strains. The biological characteristics of the pathogens were determined using the mycelial growth rate method. The pathogens’ influence on the host plant’s ultrastructure was investigated using transmission electron microscopy (TEM). Colletotrichum nymphaeae was identified as the pathogen implicated in the development of leaf spot disease in R. hybridum Ker Gawl across three regions in Kunming City through the integration of morphological traits and phylogenetic analyses of multiple genes (ITS, ACT, GAPDH, HIS3, CHS1, and TUB2). Its mycelial growth is most effective at a temperature of 25 °C. pH and light have relatively minor effects on the growth of mycelium. The preferred carbon and nitrogen sources were identified as mannitol and yeast extract, respectively. Additionally, TEM observations revealed significant damage to the cell structure of R. hybridum Ker Gawl leaves infected by the pathogen. The cell walls were dissolved, the number of chloroplasts decreased markedly, starch granules within chloroplasts were largely absent, and the number of osmiophilic granules increased. This is the first report of leaf spot disease in R. hybridum Ker Gawl caused by C. nymphaeae. The results of this study provide valuable insights for future research on the prevention and control of this disease.

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.