Soil moisture retrieval using fuzzy logic based on UWB signals

Zhu, Fengchao; Liu, Huaiyuan; Liang, Jing

doi:10.1109/wcsp.2015.7341191

Cited by 6 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, x n p×n , where n = 12800, p = 300. We perform data centering on the soil data for each vector according to formula (5), and obtain the matrix X after the centralization. Calculate the covariance matrix according to equation (6).…”

Section: B Signal Pre-processmentioning

confidence: 99%

“…Then they created templates utilizing non-singleton type-1 fuzzy logic system (NST1FLS) and test its robust Gaussian white noise. However, as mentioned in [5], the recognition the results are not very satisfactory, which means that there are more retrieval methods worth exploring.The work of [6], [7] and [8] are mainly focused on establishing a mapping relationship between UWB radar echo signals and different soils by utilizing mathematical model. Among them, [6] utilized leveraging fuzzy logic to retrieval soils with different moisture.…”

Section: Introductionmentioning

confidence: 98%

“…In 2016, Zhu and Liu [5] first attempted to achieve soil water content inversion by exploring the potential relationship between UWB radar soil echo signals and soil parameters. They chose UWB radar to collect soil echo signals, and applied type-2 fuzzy logic system (T2FLS) to extract features in UWB soil echoes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Soil pH Value Forecasting Using UWB Echoes Based on Ensemble Methods

Yang

Liang

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper proposed a new method to predict soil pH values based on ensemble methods via ultra-wideband (UWB) radar echoes, due to the fact that the ensemble method has a fast running speed, fewer parameters, and the amount of data required is not large. 16 categories of UWB soil echoes with different pH values are collected and investigated by 4 types of ensemble methods including bagging, randomforest, adaboost and gradientboost. We use principal component analysis(PCA) to reduce the dimensions of the raw data to reduce the overall amount of computation. First, we applied the PCA algorithm to extract features from the raw signals. Second, we applied these four prediction models to predict the pH values with different feature dimensions. Finally, we compare the prediction performance of these four prediction models with different SNRs. The simulation experiment results show that, when the feature dimension is reduced to 4 to 10, randomforest and bagging provide better performance than adaboost and gradientboost in terms of R 2 and MSE.

show abstract

Section: B Signal Pre-processmentioning

confidence: 99%