An Intelligent Water-Saving Irrigation System Based on Multi-Sensor Fusion and Visual Servoing Control
ZhengKai Huang, YiKun Wang, ChenYu Hui, XiaoCheng

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…
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