Invasive alien plants (IAPs) present a significant threat to ecosystems and agricultural production, necessitating rigorous monitoring and detection for effective management and control. To realize accurate and rapid detection of invasive alien plants in the wild, we proposed a rapid detection approach grounded in an advanced YOLOv9, referred to as YOLO-IAPs, which incorporated several key enhancements to YOLOv9, including replacing the down-sampling layers in the model’s backbone with a DynamicConv module, integrating a Triplet Attention mechanism into the model, and replacing the original CIoU with the MPDloU. These targeted enhancements collectively resulted in a substantial improvement in the model’s accuracy and robustness. Extensive training and testing on a self-constructed dataset demonstrated that the proposed model achieved an accuracy of 90.7%, with the corresponding recall, mAP50, and mAP50:95 measured at 84.3%, 91.2%, and 65.1%, and a detection speed of 72 FPS. Compared to the baseline, the proposed model showed increases of 0.2% in precision, 3.5% in recall, and 1.0% in mAP50. Additionally, YOLO-IAPs outperformed other state-of-the-art object detection models, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv10 series, Faster R-CNN, SSD, CenterNet, and RetinaNet, demonstrating superior detection capabilities. Ablation studies further confirmed that the proposed model was effective, contributing to the overall improvement in performance, which underscored its pre-eminence in the domain of invasive alien plant detection and offered a marked improvement in detection accuracy over traditional methodologies. The findings suggest that the proposed approach has the potential to advance the technological landscape of invasive plant monitoring.

Invasive plant species pose significant biodiversity and ecosystem threats. Real-time identification of invasive plants is a crucial prerequisite for early and timely prevention. While deep learning has shown promising results in plant recognition, the use of deep learning models often involve a large number of parameters and high data requirements for training. Unfortunately, the available data for various invasive plant species are often limited. To address this challenge, this study proposes a lightweight deep learning model called IPMCNet for the identification of multiple invasive plant species. IPMCNet attains high recognition accuracy even with limited data and exhibits strong generalizability. Simultaneously, by employing depth-wise separable convolutional kernels, splitting channels, and eliminating fully connected layer, the model’s parameter count is lower than that of some existing lightweight models. Additionally, the study explores the impact of different loss functions, and the insertion of various attention modules on the model’s accuracy. The experimental results reveal that, compared with eight other existing neural network models, IPMCNet achieves the highest classification accuracy of 94.52%. Furthermore, the findings suggest that focal loss is the most effective loss function. The performance of the six attention modules is suboptimal, and their insertion leads to a decrease in model accuracy.

Mikania micrantha, one of the world’s most destructive invasive species, is known for causing significant ecological and economic harm. While extensive research has focused on its growth characteristics, secondary metabolites, and control measures, its chemical interactions with the environment—particularly the role of flavonoids in shaping soil microbial communities—remain underexplored. In this study, we identified and quantified ten flavonoids from M. micrantha root exudates using UPLC-MS, including Hispidulin, Isorhamnetin, and Mikanin. To examine their impact, crude flavonoid extracts were applied to soil in potted experiments, which demonstrated that these compounds significantly increased soil fungal diversity and boosted the relative abundance of arbuscular mycorrhizal fungi (AMF). Furthermore, KEGG pathway analysis revealed that flavonoid addition elevated the copy numbers of genes involved in nitrogen cycling and metabolic functions, enhancing nutrient availability and microbial activity. Additionally, crude flavonoid extracts promoted the relative abundance of beneficial soil bacteria, such as Achromobacter, as well as AMF, both of which contribute to nutrient acquisition, plant growth, and soil health. These findings indicate that M. micrantha’s flavonoids can alter soil microbial community composition, thereby creating a favorable environment that reinforces its competitive edge over native plants.

We report on the detailed steps of a method to estimate the biomass of invasive plants based on UAV remote sensing and computer vision. To collect samples from the study area, we prepared a sample square assembly to randomize the sampling points. An unmanned aerial camera system was constructed using a drone and camera to acquire continuous RGB images of the study area through automated navigation. After completing the shooting, the aboveground biomass in the sample frame was collected, and all correspondences were labeled and packaged. The sample data was processed, and the aerial images were segmented into small images of 280 x 280 pixels to create an image dataset. A deep convolutional neural network was used to map the distribution of Mikania micrantha in the study area, and its vegetation index was obtained. The organisms collected were dried, and the dry weight was recorded as the ground truth biomass. The invasive plant biomass regression model was constructed using the K-nearest neighbor regression (KNNR) by extracting the vegetation index from the sample images as an independent variable and integrating it with the ground truth biomass as a dependent variable. The results showed that it was possible to predict the biomass of invasive plants accurately. An accurate spatial distribution map of invasive plant biomass was generated by image traversal, allowing precise identification of high-risk areas affected by invasive plants. In summary, this study demonstrates the potential of combining unmanned aerial vehicle remote sensing with machine learning techniques to estimate invasive plant biomass. It contributes significantly to the research of new technologies and methods for real-time monitoring of invasive plants and provides technical support for intelligent monitoring and hazard assessment at the regional scale.