Design and Implementation of an Intelligent Irrigation System Based on Multi-Sensor Fusion

DOI: https://doi.org/10.62517/jiem.202603213

Author(s)

Liming Jiang

Affiliation(s)

Electrical Engineering and Intelligent Control, College of Engineering, Shanghai Sanda University, Shanghai, China

Abstract

Aiming at the problems of one-sided sensor perception, rigid decision-making models, and insufficient scene adaptability in the current intelligent irrigation field, this paper designs and implements an irrigation solution integrating multi-dimensional perception and intelligent decision-making based on the actual needs of farmland production. The system constructs a three-dimensional perception network of "soil moisture-crop physiology-meteorological environment", integrates data from various high-precision sensors, processes multi-source heterogeneous data through a hierarchical data fusion algorithm, and builds an adaptive irrigation decision-making model combined with machine learning algorithms, realizing the upgrade from "passive water supplementation" to "active adaptation". The hardware adopts standardized interfaces and redundant design, and the software incorporates energy consumption optimization logic. Field tests show that compared with traditional irrigation modes, the system increases water resource utilization rate by 28.3% and average crop yield by 13.6%, providing a feasible practical scheme for modern agricultural irrigation.

Keywords

Multi-sensor Fusion; Intelligent Irrigation; Machine Learning; Adaptive Decision-Making; Precision Agriculture

References

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