School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore.
Institute of High Performance Computing (IHPC), A*STAR, Singapore 138632, Singapore.
Sensors (Basel). 2022 Aug 3;22(15):5810. doi: 10.3390/s22155810.
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.
本文提出了一种使用微波短程宽带雷达传感器对土壤湿度进行有效估计的机器学习模型。土壤湿度通过使用工作在 3-10GHz 的短程现成雷达传感器以体积含水量进行测量。雷达捕获反射信号，对其进行后处理以确定土壤湿度，并将其映射到从反射信号中提取的输入特征，以训练机器学习模型。此外，还与接触式 Vernier 土壤传感器进行了比较和分析。本文对使用神经网络、支持向量机、线性回归和 K 近邻训练的不同机器学习模型进行了评估和介绍。使用均方根误差、决定系数和平均绝对误差来计算模型的效率。KNN、SVM 和线性回归的 RMSE 和 MAE 值分别为 11.51 和 9.27、15.20 和 12.74、3.94 和 3.54。结果表明，神经网络的 R2 值为 0.9894，效果最佳。这项研究工作旨在为农民等普通用户开发具有成本效益的解决方案，以提高准确性来监测土壤湿度状况。
