Prediction of soil moisture and temperature based on deep learning

Accurate prediction of soil moisture and temperature is helpful to regulate agricultural planting parameters and optimize crop planting quality. In this paper, relevant issues are studied from two aspects: environmental data acquisition system based on Internet of Things technology and adaptive deep learning prediction model selection. An NB-IoT IoT data collection system is proposed for deep learning of long-period and equal-interval time series data collection. Using the obtained environmental temperature and humidity and soil moisture and temperature data, a deep learning model based on long short-term memory network (LSTM) is implemented to train and predict soil moisture and temperature, and the prediction effect of each deep learning method is analyzed under different step length conditions. The experimental results show that the system can effectively obtain and manage the data required for deep learning, and the deep learning-based prediction model can achieve reliable prediction of soil moisture and temperature by only relying on the environmental temperature and humidity time series data.