Inspection is a fundamental task for water plants, yet traditional methods are often labor-intensive, time-consuming, and costly. The rapid advancement of drone technology has significantly transformed environmental inspections, particularly in water plant assessments. Digital twins enhance modeling and simulation capabilities by integrating real-time data and feedback. This paper presents an intelligent water plant detection system based on YOLOv10 and drone technology. The system aims to monitor environmental conditions around water facilities and automatically identify anomalies in real time. The design utilizes dataset images of construction vehicles, maintenance hole covers, and pipe leaks collected from publicly accessible websites. The system integrates real-time drone inspection data into a digital twin platform for dynamic monitoring.