Deep learning adaptive irrigation control method and system based on edge computing
By employing edge computing and deep learning-based irrigation control methods, a neural network model with a multi-dimensional feature extraction architecture was constructed. This solved the problem of a single dimension in irrigation decision-making, achieving precise adaptation to crop water requirements and improving the efficiency and environmental adaptability of the irrigation system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-11
- Publication Date
- 2026-07-17
AI Technical Summary
Existing irrigation technologies rely on a single dimension for decision-making, failing to meet the actual water needs of crops, leading to water waste and secondary ecological problems in the soil.
An edge computing-based deep learning adaptive irrigation control method is adopted. By constructing a deep neural network model with a multi-dimensional heterogeneous feature extraction architecture, and combining multi-source sensor data for real-time environmental parameter processing and feature mining, an irrigation demand index is generated, and intelligent irrigation decisions are made.
It significantly improves the accuracy of irrigation instructions, enabling them to match the dynamic water demand of crops throughout their entire growth cycle, reducing water waste, and enhancing the adaptability and precision of the irrigation system.
Smart Images
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