{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T03:59:43Z","timestamp":1773719983511,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T00:00:00Z","timestamp":1589587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Attention to the natural environment is equivalent to observing the space in which we live. Plant roots, which are important organs of plants, require our close attention. The method of detecting root system without damaging plants has gradually become mainstream. At the same time, machine learning has been achieving good results in recent years; it has helped develop many tools to help us detect the underground environment of plants. Therefore, this article will introduce some existing content related to root detection technology and machine detection algorithms for root detection, proving that machine learning root detection technology has good recognition capabilities.<\/jats:p>","DOI":"10.3390\/s20102836","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T02:43:42Z","timestamp":1589769822000},"page":"2836","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9540-4627","authenticated-orcid":false,"given":"Hao","family":"Liang","sequence":"first","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"Beijing Laboratory of Urban and Rural Ecological Environment, Beijing 100083, China"},{"name":"Research Center for Intelligent Forestry, Beijing 10083, China"},{"name":"Key Lab of State Forestry Administration for Forestry Equipment and Automation, Beijing 10083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linyin","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"Research Center for Intelligent Forestry, Beijing 10083, China"},{"name":"Key Lab of State Forestry Administration for Forestry Equipment and Automation, Beijing 10083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"Beijing Laboratory of Urban and Rural Ecological Environment, Beijing 100083, China"},{"name":"Research Center for Intelligent Forestry, Beijing 10083, China"},{"name":"Key Lab of State Forestry Administration for Forestry Equipment and Automation, Beijing 10083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1049\/ecej:19960402","article-title":"Surface-penetrating radar","volume":"8","author":"Daniels","year":"1996","journal-title":"Electron. Commun. Eng. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s11104-013-1751-8","article-title":"Forward simulation of root\u2019s ground penetrating radar signal: Simulator development and validation","volume":"372","author":"Guo","year":"2013","journal-title":"Plant Soil"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/978-981-13-6713-7_7","article-title":"Use of Ground-Penetrating Radar (GPR) as an Effective Tool in Assessing Pavements\u2014A Review","volume":"29","author":"Ruchita","year":"2019","journal-title":"Geotech. Transp. Infrastruct."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1088\/1742-2132\/5\/4\/008","article-title":"3D visualization of integrated ground penetrating radar data and EM-61 data to determine buried objects and their characteristics","volume":"5","author":"Daniels","year":"2008","journal-title":"J. Geophys. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.15376\/biores.15.2.2237-2257","article-title":"Qualitative Research: The Impact of Root Orientation on Coarse Roots Detection Using Ground-Penetrating Radar (GPR)","volume":"15","author":"Wang","year":"2020","journal-title":"BioResources"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alani, A.M., Ciampoli, L.B., Tosti, F., Brancadoro, M.G., Pirrone, D., and Benedetto, A. (2017). Health Monitoring of a Matured Tree Using Ground Penetrating Radar\u2013Investigation of the Tree Root System and Soil Interaction. EGU General Assembly Conference Abstracts, IMEKO.","DOI":"10.1109\/ICGPR.2018.8441535"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ciampoli, L.B., Tosti, F., and Economou, N. (2019). Signal Processing of GPR Data for Road Surveys. Geosciences, 9.","DOI":"10.3390\/geosciences9020096"},{"key":"ref_8","unstructured":"Schoor, M., and Colvin, C. (2009, January 16\u201318). Tree root mapping with ground penetrating radar. Proceedings of the 11th SAGA Biennial Technical Meeting and Exhibition, Swaziland, South Africa."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Leucci, G., Giorgi, L.D., Ditaranto, I., Giuri, F., Ferrari, I., and Scardozzi, G. (2019). New Data on the Messapian Necropolis of Monte D\u2019Elia in Alezio (Apulia, Italy) from Topographical and Geophysical Surveys. Sensors, 19.","DOI":"10.3390\/s19163494"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Porcelli, F., Sambuelli, L., Comina, C., Span\u00f2, A., Lingua, A., Calantropio, A., Catanzariti, G., Chiabrando, F., Fischanger, F., and Maschio, P. (2020). Integrated Geophysics and Geomatics Surveys in the Valley of the Kings. Sensors, 20.","DOI":"10.3390\/s20061552"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gribbe, S., Blume-Werry, G., and Couwenberg, J. (2020). Digital, Three-Dimensional Visualization of Root Systems in Peat. Soil Syst., 4.","DOI":"10.3390\/soilsystems4010013"},{"key":"ref_12","first-page":"642","article-title":"Imaging tree root systems in situ","volume":"4084","author":"Wielopolski","year":"2000","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1093\/treephys\/21.17.1269","article-title":"Use of ground-penetrating radar to study tree roots in the southeastern United States","volume":"21","author":"Butnor","year":"2001","journal-title":"Tree Physiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s11104-013-1798-6","article-title":"Root orientation can affect detection accuracy of ground-penetrating radar","volume":"373","author":"Tanikawa","year":"2013","journal-title":"Plant Soil"},{"key":"ref_15","first-page":"123","article-title":"Methodology and Design of Field Experiments for Monitoring the Hygrothermal Performance of Wood Frame Enclosures","volume":"26","author":"Straube","year":"2002","journal-title":"J. Build. Phys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"114352","DOI":"10.1016\/j.geoderma.2020.114352","article-title":"Root-induced changes in soil thermal and dielectric properties should not be ignored","volume":"370","author":"Fu","year":"2020","journal-title":"Geoderma"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s00226-010-0316-8","article-title":"Dielectric mixing models for water content determination in woody biomass","volume":"45","author":"Paz","year":"2011","journal-title":"Wood Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107052","DOI":"10.1016\/j.measurement.2019.107052","article-title":"A method of electrical conductivity compensation in a low-cost soil moisture sensing measurement based on capacitance","volume":"150","author":"Deng","year":"2020","journal-title":"Measurement"},{"key":"ref_19","unstructured":"Sun, Y., Ma, J., Peng, J., Huang, S., Yang, K., Zhu, P., and Zhu, H. (August, January 28). Preliminary Applicability Analysis of Soil Dielectric Constant Model of the Different Soil Texture Condition. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.biosystemseng.2006.01.002","article-title":"Effect of soil water on apparent soil electrical conductivity and texture relationships in a dryland field","volume":"94","author":"Mccutcheon","year":"2006","journal-title":"Biosyst. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2618","DOI":"10.1007\/s12205-019-2012-z","article-title":"A Machine Learning Based Approach for Automatic Rebar Detection and Quantification of Deterioration in Concrete Bridge Deck Ground Penetrating Radar B-scan Images","volume":"23","author":"Asadi","year":"2019","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/LGRS.2013.2248119","article-title":"A Data Pair-Labeled Generalized Hough Transform for Radar Location of Buried Objects","volume":"11","author":"Windsor","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TGRS.2016.2592679","article-title":"Real-Time Hyperbola Recognition and Fitting in GPR Data","volume":"55","author":"Dou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0893-6080(88)90020-2","article-title":"An introduction to neural computing","volume":"1","author":"Kohonen","year":"1988","journal-title":"Neural Netw."},{"key":"ref_25","unstructured":"Jin, W., and Li, Z.J. (2000, January 21\u201325). The improvements of BP neural network learning algorithm. Proceedings of the 16th World Computer Congress 2000, Beijing, China."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s10462-011-9208-z","article-title":"An optimizing BP neural network algorithm based on genetic algorithm","volume":"36","author":"Ding","year":"2011","journal-title":"Artif. Intell. Rev."},{"key":"ref_27","first-page":"48","article-title":"An Application of Genetic Algorithm in GPR Data Analysis for Buried Tomb Relics","volume":"2003","author":"Yao","year":"2003","journal-title":"J. East China Norm. Univ."},{"key":"ref_28","unstructured":"Wang, Y.P., Li, S., and Wei, Q. (2007). Biology Inspired Robot Behavior Selection Mechanism: Using Genetic Algorithm, Springer."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1109\/TGRS.2009.2012701","article-title":"Automatic Analysis of GPR Images: A Pattern-Recognition Approach","volume":"47","author":"Pasolli","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, H., and Cohn, A.G. (2010, January 18\u201323). Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data. Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain.","DOI":"10.1109\/IJCNN.2010.5596298"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.cageo.2013.04.012","article-title":"Using Pattern Recognition to Automatically Localize Reflection Hyperbolas in Data from Ground Penetrating Radar","volume":"58","author":"Maas","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pham, M., and Lef\u00e8vre, S. (2018). Buried object detection from B-scan ground penetrating radar data using Faster-RCNN. arXiv.","DOI":"10.1109\/IGARSS.2018.8517683"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kafedziski, V., Pecov, S., and Tanevski, D. (2018, January 20\u201321). Detection and Classification of Land Mines from Ground Penetrating Radar Data Using Faster R-CNN. Proceedings of the 26th Telecommunications Forum (TELFOR), Belgrade, Serbia.","DOI":"10.1109\/TELFOR.2018.8612117"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11104-012-1455-5","article-title":"Application of ground penetrating radar for coarse root detection and quantification: A review","volume":"362","author":"Guo","year":"2013","journal-title":"Plant Soil"},{"key":"ref_35","unstructured":"Birkenfeld, S. (2010, January 19\u201323). Automatic detection of reflexion hyperbolas in GPR data with neural networks. Proceedings of the 2010 World Automation Congress, Kobe, Japan."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Besaw, L.E., and Stimac, P.J. (2015). Deep convolutional neural networks for classifying GPR B-Scans. Proceedings of SPIE the International Society for Optical Engineering, SPIE.","DOI":"10.1117\/12.2176250"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4417","DOI":"10.1109\/TGRS.2019.2891206","article-title":"A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar with Application to Full-Waveform Inversion","volume":"57","author":"Giannakis","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","unstructured":"Giannakis, I., Giannopoulos, A., and Warren, C. (2020). A Machine Learning Scheme for Estimating the Diameter of Reinforcing Bars Using Ground Penetrating Radar. IEEE Geosci. Remote Sens. Lett., 1\u20135."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ando, J. (2019). Attempt to apply the machine learning for GPR data. The 13th SEGJ International Symposium Tokyo, Japan, 12\u201314 November 2018, Society of Exploration Geophysicists.","DOI":"10.1190\/SEGJ2018-137.1"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2836\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:29:29Z","timestamp":1760174969000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2836"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,16]]},"references-count":39,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102836"],"URL":"https:\/\/doi.org\/10.3390\/s20102836","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,16]]}}}