Accurate and large-scale monitoring of pine wilt disease (PWD) remains a critical challenge in forest management due to limited coverage, high operational cost, and insufficient detection reliability in conventional survey methods. To address these limitations, this study develops a real-time intelligent monitoring system that integrates a fixed high-altitude sensing platform with a YOLOv5-based deep learning detector. The system provides continuous panoramic visual acquisition, while an edge-cloud collaborative architecture enables real-time image preprocessing and automatic identification of infected pine trees. A high-resolution dataset collected from the Qinba Mountain region of Chongqing, China, was constructed to train and evaluate multiple YOLOv5 variants. Experimental results demonstrate that the optimal model (YOLOv5x) achieves a mean average precision (mAP) of 68.8% and an overall detection accuracy of 93.78%. Compared with conventional unmanned aerial vehicle (UAV) based monitoring, the proposed system provides larger monitoring coverage (approximately 12 km²), lower operational cost per 700 km² monitoring area (reduced by approximately 76.00%), and improved identification reliability. These results demonstrate that stable high-altitude observation combined with deep learning-based detection offers a scalable and cost-efficient solution for large-area forest epidemic surveillance and intelligent ecological management.