Soil Moisture Retrieval Using UWB Echoes via Fuzzy Logic and Machine Learning

Soil moisture (SM) retrieval using wireless signals has become a research focus with the development of sensor devices in Internet of Things. Studies applying ground-penetrating radar have improved the accuracy of SM retrieval; however, the field-scaled data are hardly satisfactory mainly due to the frequency response of the antenna, and it is not cost-effective for farmers to monitor the soil conditions. In this paper, we compare two fuzzy logic systems (FLSs): 1) type-1 FLS and 2) adaptive network-based fuzzy inference system (ANFIS) to extract fuzzy parameters of soil. Moreover, two machine learning algorithms: 1) random forest (RF) and 2) artificial neural network with principal component analysis are applied in the SM classifications. Nine types of UWB soil echoes of different texture and volume water content (VWC) are collected and investigated using our approaches. Final analysis shows that ANFIS with RF provides the best VWC correct recognition rate compared to other algorithms.