期刊
SENSORS
卷 22, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/s22155810
关键词
KNN; linear regression; microwave radar; neural network; SVM; soil moisture; volumetric water content
资金
- RIE2020 Industry Alignment Fund-Industry Collaboration Projects (IAF-ICP) Funding Initiative
This paper proposes machine learning models for estimating soil moisture content using a microwave radar sensor. Different machine learning models, including neural network, are trained and compared with a traditional contact-based sensor. The research shows that the neural network model performs the best in terms of accuracy.
In this paper, machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor are proposed. The soil moisture is measured as the volumetric water content using a short-range off-the-shelf radar sensor operating at 3-10 GHz. The radar captures the reflected signals that are post processed to determine the soil moisture which is mapped to the input features extracted from the reflected signals for the training of the machine learning models. In addition, the results are compared and analyzed with a contact-based Vernier soil sensor. Different machine learning models trained using neural network, support vector machine, linear regression and k-nearest neighbor are evaluated and presented in this work. The efficiency of the model is computed using root mean square error, co-efficient of determination and mean absolute error. The RMSE and MAE values of KNN, SVM and Linear Regression are 11.51 and 9.27, 15.20 and 12.74, 3.94 and 3.54, respectively. It is observed that the neural network gives the best results with an R2 value of 0.9894. This research work has been carried out with an intention to develop cost-effective solutions for common users such as agriculturists to monitor the soil moisture conditions with improved accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据