Pine Wilt Disease (PWD) is a severe and highly contagious forest disease that poses a significant challenge to sustainable development. This study utilizes Unmanned Aerial Vehicle (UAV) remote sensing and RGB images, combining deep learning techniques to address the challenge of detecting infected trees. The proposed model, YOLO-PWD, integrates the Squeeze-and-Excitation network (SE) and Convolutional Block Attention Module (CBAM) to improve the feature extraction capabilities. Additionally, the integration of a Bidirectional Feature Pyramid Network (BiFPN) enhances the capture of global scene information and adapts to complex environmental variations. We further enhance detection accuracy by incorporating a dynamic convolution kernel, ensuring the system's suitability for deployment on edge devices such as UAVs. Compared to the original YOLOv5s model, YOLO-PWD demonstrates a significant improvement in recall rate, with an increase of 14.2%, and achieves an impressive Average Precision (AP) of 95.2% for detecting discolored pine trees. The precision, recall, and AP for dead pine trees are also enhanced, with precision increasing by 9.7% and AP by 6.7%. Despite a mere 0.9MB increase in model size, the F1-score for discolored pine trees improves by 4.1%, and the F1-score for dead pine trees increases by 3.3%. The experimental results suggest that the YOLO-PWD model can better meet the requirements of PWD detection by UAV remote sensing in large epidemic areas. This advancement carries significant implications for the protection of pine forest resources and contributes to environmentally sustainable development.

In the context of the booming Internet of Vehicles, predicting vehicle trajectories is crucial for intelligent transportation systems. Existing methods, reliant on sensor data and behavior models, struggle with intricate relationships between vehicles and dynamic road networks. To overcome these challenges, we propose the Vehicle Interaction-based Dynamic Graph Neural Network (VI-DGNN) model. This model constructs a vehicle interaction graph to capture temporal and spatial dependencies among vehicles. A spatiotemporal attention network is employed to discern patterns in vehicle movements, addressing high-speed changes. Our model introduces a vehicle interaction mechanism for dynamic movement, leveraging proximity timestamp graph structures. By incorporating vehicle behavioral features and road network topology, our model minimizes distribution prediction variance, enhancing stability. Experimental results on real datasets demonstrate superior long-term prediction performance compared to state-of-the-art baselines.