Evaluation of Feedforward Artificial Neural Networks (ANN) to Adjust Soil Moisture Estimates Derived From Time Domain Reflectometry (TDR) Measurements Using Soil Temperature and Gravimetric Data

Soil temperature is one of the soil characteristics that greatly influences the accuracy of Time Domain Reflectometry (TDR) measurements for estimating soil moisture content. The authors examine the performance of two feedforward Artificial Neural Networks (ANN) configurations, commonly used for data regression analysis, to adjust TDR soil moisture estimates using soil temperature and gravimetric data. The data used for this study was obtained during a period of six weeks (October-November 2017) in three adjacent test sites in the Purepecha Plateau (Michoacaacuten, Meacutexico) managed under different tillage practices: at rest, reduced tillage and intensive tillage respectively. 10 TDR measurements per week were obtained from each test site. 60 Soil samples from each measurement site were also collected simultaneously, to determine the soil moisture content by the gravimetric method, and the soil temperature at 20 cm depth. 24 different configurations of ANNs were tested. The best result was obtained using a feedforward ANN with 11 tanh-sigmoid neurons in the input (hidden) layer. In addition, the authors also analyze the effect of different tillage practices on the soil moisture data. The results corroborate that tillage practices influence the soil moisture measurements and thus the best ANN results are obtained when the data used for training the ANNs is derived from sites managed under the same tillage practice

Validation of Wireless Volumetric Soil Water Content Sensor Based on Soil Temperature and Impedance Measurements

The rational use of water resources requires accurate assessment of soil moisture content. During the last three decades, electromagnetic measurement techniques have evolved into versatile, cost- effective solutions for conducting in situ soil moisture measurements. However, it is still necessary to further continue developing technological solutions that can yield soil moisture measurements close to the real content, stressing ease of use and can be adjusted to operate under different site conditions. Here the authors describe a volumetric soil moisture measurement instrument based on soil impedance measurements. The soil temperature is used as an additional parameter to implement a measurement compensation method. The measurement compensation process uses a feedforward artificial neural network. 10 measurements were obtained in situ in three test fields (maize, wheat, pastureland), over a period of 10 weeks (october-december 2017). The results were compared to measurements obtained using a commercial soil moisture instrument (6050X1 Trase System) and the gravimetic method. The results indicate that the prototype developed for this application can yield information close to gravimetric data for the three test sites (Maize SSE [sum of squared error]: 5.97, Wheat SSE: 19.81, Pastureland SSE: 12.71) in agreement with TDR data.nbs