Prediction models of soil moisture content and electrical conductivity in citrus orchard based on internet of things and LSTM

Objective  To build an internet of things (IoT) system for transmitting the environmental information of citrus orchards in real time, establish prediction models of soil moisture content and electrical conductivity in citrus orchard based on IoT system and long short-term memory (LSTM), and provide references for irrigation and fertilization management as well as effect prediction and evaluation.

Method  Soil temperature, moisture and electrical conductivity sensors were applied in five IoT nodes and a weather station was set in citrus orchard. The meteorological data and soil moisture data collected in the orchard were transmitted to a remote server via ZigBee, a short range wireless communication technique, and GPRS, a long distance wireless transmission technique. The prediction models of soil moisture content and electrical conductivity were established using weather data based on the LSTM model. The root mean square error (RMSE) and coefficient of determination (R²) were calculated to evaluate the performance of the model.

Result  The IoT system was capable to transmit environmental data of the citrus orchard to a remote server. LSTM and general regression neural network (GRNN) model were built to predict soil moisture content and electrical conductivity. The performance of models in five nodes were as following: The RMSE of soil moisture content and electrical conductivity ranged from 6.74 to 8.65 and 6.68 to 8.50 respectively based on LSTM model, and ranged from 7.01 to 14.70 and 7.60 to 13.70 respectively based on GRNN model. With the generated LSTM model and meteorological data for predicting, regression analysis was conducted between predicted and measured values of soil moisture content and electrical conductivity. The R² of soil moisture content and electrical conductivity ranged from 0.760 to 0.906 and 0.648 to 0.850 respectively based on LSTM model, and ranged from 0.126 to 0.369 and 0.132 to 0.268 respectively based on GRNN model. The results indicated that the LSTM model performed better than the GRNN model.

Conclusion  The IoT system for citrus orchard environmental information transmission is established. The LSTM model has high accuracy in predicting soil moisture content and electrical conductivity, and the model can be helpful for guiding irrigation and fertilization management.