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Prediction and Monitoring Model for Farmland Environmental System Using Soil Sensor and Neural Network Algorithm by Tao Song; Yulong Si; Jie Gao; Wei Wang; Congwei Nie; Jiří Jaromír Klemeš is a Physics and Astronomy article available to read on EtoBox.

What is Prediction and Monitoring Model for Farmland Environmental System Using Soil Sensor and Neural Network Algorithm about?

In this study, data fusion algorithm is used to classify the soil species and calibrate the soil humidity sensor, and by using edge computing and a wireless sensor network, farmland environment monitoring system with a two-stage calibration function of frequency domain reflectometer (FDR) is established. Edge computing is used in system nodes, including the saturation value of the soil humidity sensor, the calculated soil hardness, the calculation process of the neural network, and the model of soil classification. A bagged tree is adopted to avoid over-fitting to reduce the prediction variance of the decision tree. A decision tree model is established on each training set, and the C4.5 algorithm is adopted to construct each decision tree. After primary calibration, the root mean squared error (RMSE) between the measured and standard values is reduced to less than 0.0849%. The mean squared error (MSE) and mean absolute error (MAE) are reduced to less than 0.7208 and 0.6929%. The bagged tree model and backpropagation neural network are used to classify the soil and train the dynamic soil dataset. The output of the trained neural network is closer to the actual soil humidity than tha

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Author

Tao Song; Yulong Si; Jie Gao; Wei Wang; Congwei Nie; Jiří Jaromír Klemeš

Publisher

Walter de Gruyter GmbH

Published

2023

Language

EN

Field

Physics and Astronomy (Physical Sciences)