Computer Vision-Based Multi-Growth Cycle Crop Recognition and Auxiliary Irrigation Decision-Making System

Under the wave of agricultural intelligence, a crop identification system based on YOLOv11/v10/v8/v5 framework came into being, and the backbone network selected in this study was YOLOv8n. The system can accurately identify cotton, rice, and wheat by training crop image data, and realize the identification of crops with multiple growth cycles according to their different growth stages, and propose auxiliary irrigation decisions, effectively improving agricultural production efficiency and reducing labor costs [1]–[2]. The system is developed based on Python combined with PyQT5 to create a detection and recognition platform with UI interface, which supports image, video and camera detection, can save detection results, has both display and practicability, and provides strong support for agricultural development.