基于环境温湿度的土壤水分温度预测研究

Research on soil moisture and temperature prediction based on environmental temperature and humidity

文中针对土壤水分温度预测的实际需求,从基于物联网技术的环境数据采集系统以及适配的深度学习预测模型选择两方面对相关问题进行研究,设计一种基于嵌入式系统及NB-IoT技术的物联网数据采集系统验证平台。基于采集到的数据,分析长短时记忆网络(LSTM)、门控循环单元(GRU)以及双向长短时记忆网络(Bi-LSTM)模型的预测效果。实验结果表明,所提出的数据采集系统可以有效获得深度学习所需数据,而基于该数据的深度学习预测模型可以实现仅依靠环境温湿度时间序列数据对土壤水分与温度的可靠预测。该研究可为相关领域的土壤水分温度预测系统设计提供参考。

In allusion to the actual demand of soil moisture and temperature prediction,related issues are researched in two aspects:environmental data acquisition system based on IoT(Internet of Things)technology and adaptive deep learning prediction model selection. A verification platform for IoT data acquisition system based on embedded system and NB-IoT(narrow-band Internet of Things)technology is designed. Based on the collected data,the prediction effects of LSTM(long and short term memory network),gated recurrent unit(GRU)and BI-LSTM(bidirectional long short memory network)models are analyzed. The experimental results show that the proposed data acquisition system can effectively obtain the required data needed by the deep learning,and the deep learning prediction model based on this data can realize the reliable prediction of soil moisture and temperature only depending on the environmental temperature-humidity time series data. This research can provide reference for the design of soil moisture and temperature prediction system in related fields.