Prediction models of soil moisture content and electrical conductivity in citrus orchard based on internet of things and LSTM
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摘要: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。
Abstract:ObjectiveTo 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.
MethodSoil 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.
ResultThe 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.
ConclusionThe 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.
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图 8 LSTM模型在不同节点的土壤含水量实测值与预测值的比较
a1~e1中，蓝线代表实测值，红线代表预测值
Figure 8. Comparison of measured and predicted soil moisture of LSTM model in different nodes
In a1−e1, blue line represents measured value, and red line represents predicted value
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图 9 LSTM模型在不同节点的土壤电导率实测值与预测值的比较
a1~e1中，蓝线代表实测值，红线代表预测值
Figure 9. Comparison of measured and predicted soil electrical conductivity of LSTM model in different nodes
In a1−e1, blue line represents measured value, and red line represents predicted value
表 1 土壤含水量和电导率与环境参数的相关系数
Table 1 Correlation coefficients of soil moisture, electrical conductivity and environmental parameters
土壤因子
Soil factor空气温度 Air temperature 降雨量
Precipitation空气湿度
Air
humidity土壤温度
Soil
temperature蒸散量
Evapotranspiration最高值
Max.
最低值
Min.
平均值
Mean
含水量 Moisture content 0.490 0.598 0.644 0.123 0.430 0.652 0.158 电导率 Electrical conductivity 0.467 0.311 0.231 0.300 0.168 0.251 0.148 
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表 2 节点数据信息
Table 2 Datasets of nodes
节点
Node数据总数
Total number训练集
Training dataset测试集
Testing dataset1 216 151 65 2 241 168 73 3 241 168 73 4 196 137 59 5 203 142 61 气象站
Weather station241 168 73
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表 3 模型性能表现
Table 3 Performance of models
节点
Node模型
Model土壤含水量
Soil moisture
content土壤电导率
Soil electrical
conductivityRMSE R2 RMSE R2 1 LSTM 8.65 0.811 6.88 0.850 GRNN 13.30 0.126 12.30 0.106 2 LSTM 7.30 0.906 6.68 0.727 GRNN 14.70 0.323 13.70 0.185 3 LSTM 6.74 0.814 8.22 0.648 GRNN 7.01 0.369 8.26 0.268 4 LSTM 7.93 0.760 8.50 0.767 GRNN 10.27 0.256 7.85 0.167 5 LSTM 8.40 0.768 7.78 0.769 GRNN 8.80 0.206 7.60 0.132
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