Water resources have a great influence on human society, but saving water in irrigation still remains a challenge. This article proposes an intelligent irrigation system that integrates a data fusion model and a long-rang (LoRa) network for optimizing the watering schedule. A data fusion model is proposed, which first adopts the long short-term memory (LSTM) network to simulate and predict the proper watering demands by integrating multi-source heterogeneous data, that is, historical weather data, user irrigation logs, weather forecasts, and online monitoring sensor data. A self-powered wide-area network is implemented and deployed by using LoRa to facilitate multiple Internet of Things (IoT) application scenarios. It includes a gateway and two types of nodes: a valve node and a sensing node. The node is capable of energy autonomy through the scheme of waterflow-based power generation, thus realizing maintenance-free throughout the life cycle. A cloud platform is designed to provide network management, intelligent irrigation control, and the interface of the mobile application. The proposed system is evaluated through a case study of landscape watering. On average, the proposed system achieves a water-saving efficiency of 94.74% compared with the conventional manual setting solutions.

水资源对人类社会影响巨大，但灌溉节水仍是一个挑战。本文提出了一种智能灌溉系统，该系统集成了数据融合模型和远程 (LoRa) 网络，用于优化灌溉计划。提出一种数据融合模型，首先采用长短期记忆（LSTM）网络，通过整合多源异构数据，模拟预测合适的浇水需求。，即历史天气数据、用户灌溉日志、天气预报、在线监测传感器数据。使用LoRa实现和部署自供电广域网，方便多种物联网（IoT）应用场景。它包括一个网关和两类节点：阀门节点和传感节点。节点通过水流发电方案实现能源自主，实现全生命周期免维护。云平台旨在提供网络管理、智能灌溉控制和移动应用程序接口。所提议的系统通过景观浇水的案例研究进行评估。与传统的手动设置解决方案相比，该系统平均节水效率为 94.74%。