An Intelligent Water-Saving Irrigation System Based on Multi-Sensor Fusion and Visual Servoing Control

TL;DR

This paper presents an intelligent irrigation system that combines multi-sensor fusion, computer vision, and robotic control to optimize water use and adapt to complex terrains, achieving significant water savings and high efficiency.

Contribution

It introduces a novel multi-sensor fusion approach with real-time plant detection and terrain stabilization, enhancing precision and water efficiency in irrigation systems.

Findings

Achieved over 96% plant detection accuracy under varying lighting.

Successfully stabilized on slopes up to 10 degrees with 1.8s response time.

Reduced water consumption by 30-50% compared to traditional methods.

Abstract

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.