An Intelligent Water-Saving Irrigation System Based on Multi-Sensor Fusion and Visual Servoing Control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908614206160896 |
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| author | Huang, ZhengKai Wang, YiKun Hui, ChenYu XiaoCheng |
| author_facet | Huang, ZhengKai Wang, YiKun Hui, ChenYu XiaoCheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Intelligent Water-Saving Irrigation System Based on Multi-Sensor Fusion and Visual Servoing Control Huang, ZhengKai Wang, YiKun Hui, ChenYu XiaoCheng Robotics Computer Vision and Pattern Recognition Systems and Control 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. |
| title | An Intelligent Water-Saving Irrigation System Based on Multi-Sensor Fusion and Visual Servoing Control |
| topic | Robotics Computer Vision and Pattern Recognition Systems and Control |
| url | https://arxiv.org/abs/2510.23003 |