An Intelligent Water-Saving Irrigation System Based on Multi-Sensor Fusion and Visual Servoing Control
This addresses water waste in agriculture, particularly for greenhouse and hilly terrain applications, but is incremental as it combines existing technologies like computer vision and robotic control.
The paper tackled inefficient water use and poor terrain adaptability in precision agriculture by developing an intelligent irrigation system that reduced water consumption by 30-50% compared to conventional flood irrigation, achieving over 92% water use efficiency in tests.
This paper introduces an intelligent water-saving irrigation system designed to address critical challenges in precision agriculture, such as inefficient water use and poor terrain adaptability. The system integrates advanced computer vision, robotic control, and real-time stabilization technologies via a multi-sensor fusion approach. A lightweight YOLO model, deployed on an embedded vision processor (K210), enables real-time plant container detection with over 96% accuracy under varying lighting conditions. A simplified hand-eye calibration algorithm-designed for 'handheld camera' robot arm configurations-ensures that the end effector can be precisely positioned, with a success rate exceeding 90%. The active leveling system, driven by the STM32F103ZET6 main control chip and JY901S inertial measurement data, can stabilize the irrigation platform on slopes up to 10 degrees, with a response time of 1.8 seconds. Experimental results across three simulated agricultural environments (standard greenhouse, hilly terrain, complex lighting) demonstrate a 30-50% reduction in water consumption compared to conventional flood irrigation, with water use efficiency exceeding 92% in all test cases.