{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T20:22:42Z","timestamp":1774038162415,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"RIE2020 Industry Alignment Fund\u2014Industry Collaboration Projects (IAF-ICP) Funding Initiative"},{"name":"Singapore Telecommunications Limited (Singtel)"},{"name":"Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u201310 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.<\/jats:p>","DOI":"10.3390\/s22155810","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"5810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor"],"prefix":"10.3390","volume":"22","author":[{"given":"Akileshwaran","family":"Uthayakumar","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore"}]},{"given":"Manoj Prabhakar","family":"Mohan","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore"}]},{"given":"Eng Huat","family":"Khoo","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing (IHPC), A*STAR, Singapore 138632, Singapore"}]},{"given":"Joe","family":"Jimeno","sequence":"additional","affiliation":[{"name":"NCS Pte Ltd., 5 Ang Mo Kio Street 62, NCS Hub, Singapore 569141, Singapore"}]},{"given":"Mohammed Yakoob","family":"Siyal","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore"}]},{"given":"Muhammad Faeyz","family":"Karim","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.advwatres.2017.09.006","article-title":"Four decades of microwave satellite soil moisture observations: Part 1. 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