基于物联网和LSTM的柑橘园土壤含水量和电导率预测模型

目的 构建柑橘果园环境信息物联网实时采集系统，建立基于物联网和长短期记忆(LSTM)的柑橘园土壤含量和电导率预测模型，为果园灌溉施肥管理、效果预测评估提供参考依据。方法 利用土壤温度、含水量、电导率三合一传感器，在柑橘果园中设置5个节点和1个气象站，通过ZigBee短距离无线通信和GPRS远距离无线传输，将果园气象数据和土壤墒情数据传输至远程服务器。利用LSTM模型建立气象数据与土壤含水量和电导率的预测模型，计算均方根误差(RMSE)和决定系数(R²)以进行性能评估。结果 物联网系统能够实现远程传输柑橘果园环境数据，建立了基于LSTM和广义回归神经网络(GRNN)的土壤含水量和电导率预测模型，模型在5个节点的数据集的训练结果分别为：LSTM模型训练的土壤含水量和电导率的RMSE范围分别为6.74~8.65和6.68~8.50，GRNN模型训练的土壤含水量和电导率的RMSE范围分别为7.01~14.70和7.60~13.70。利用生成的LSTM模型和气象数据进行拟合，将土壤含水量和电导率的预测值与实测值进行回归分析，LSTM模型拟合的土壤含水量和电导率的R²范围分别为0.760~0.906和0.648~0.850，GRNN模型拟合的土壤含水量和电导率的R²范围分别为0.126~0.369和0.132~0.268，说明LSTM模型的性能表现较好。结论 建立了柑橘果园环境的物联网信息传输系统，构建的基于LSTM的果园土壤含水量和电导率预测模型具有较高的精度，可用于指导柑橘果园的灌溉施肥管理。

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.