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

arXiv: https://arxiv.org/abs/2510.23003 · HTML: https://mlsanity.com/paper/2510.23003/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [Accurate Crop Spraying with RTK and Machine Learning on an Autonomous Field Robot](https://mlsanity.com/paper/2310.16812/) (2023)
- [SPARROW: Smart Precision Agriculture Robot for Ridding of Weeds](https://mlsanity.com/paper/2405.20896/) (2024)
- [Agricultural Robotic System: The Automation of Detection and Speech Control](https://mlsanity.com/paper/2307.09874/) (2023)

## Abstract (arXiv)

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.