Evaluation of a Ground Penetrating Radar to Map the Root Architecture of HLB-infected Citrus Trees

This paper investigates the influences of several limiting factors on the performance of ground penetrating radar (GPR) in accurately detecting huanglongbing (HLB)-infected citrus roots and determining their main structural characteristics. First, single-factor experiments were conducted to evaluate GPR performance. The factors that were evaluated were (i) root diameter; (ii) root moisture level; (iii) root depth; (iv) root spacing; (v) survey angle; and, (vi) soil moisture level. Second, two multi-factor field experiments were conducted to evaluate the performance of the GPR in complex orchard environments. The GPR generated a hyperbola in the radar profile upon root detection; the diameter of the root was successfully determined according to the width of the hyperbola when the roots were larger than 6 mm in diameter. The GPR also distinguished live from dead roots, a capability that is indispensable for studying the effects of soil-borne and other diseases on the citrus tree root system. The GPR can distinguish the roots only if their horizontal distance is greater than 10 cm and their vertical distance is greater than 5 cm if two or more roots are in proximity. GPR technology can be applied to determine the efficacy of advanced crop production strategies, especially under the pressures of disease and environmental stresses.

[1]  Lars Schmidt-Thieme,et al.  Buried pipe localization using an iterative geometric clustering on GPR data , 2014, Artificial Intelligence Review.

[2]  R. S. Freeland.  Imaging the Lateral Roots of the Orange Tree using Three-dimensional GPR , 2015 .

[3]  Gianfranco Morelli,et al.  In situ detection of tree root distribution and biomass by multi-electrode resistivity imaging. , 2008, Tree physiology.

[4]  Sven Birkenfeld.  Automatic detection of reflexion hyperbolas in gpr data with neural networks , 2010, 2010 World Automation Congress.

[5]  Xiaolin Zhu,et al.  Modeling tree root diameter and biomass by ground-penetrating radar , 2011 .

[6]  H. Ikeno,et al.  Reply to: “Comment on root orientation can affect detection accuracy of ground-penetrating radar” , 2014, Plant and Soil.

[7]  John R. Butnor,et al.  Experimental Evaluation of Several Key Factors Affecting Root Biomass Estimation by 1500 MHz Ground-Penetrating Radar , 2017, Remote. Sens..

[8]  K. Bowman,et al.  New citrus rootstocks released by USDA 2001-2010: field performance and nursery characteristics. , 2016 .

[9]  F. Tosti,et al.  Mapping the root system of matured trees using ground penetrating radar , 2018, 2018 17th International Conference on Ground Penetrating Radar (GPR).

[10]  Claude Doussan,et al.  Conventional detection methodology is limiting our ability to understand the roles and functions of fine roots. , 2005, The New phytologist.

[11]  H. Ikeno,et al.  Root orientation can affect detection accuracy of ground-penetrating radar , 2013, Plant and Soil.

[12]  Jin Chen,et al.  Detection of Root Orientation Using Ground-Penetrating Radar , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Yiannis Ampatzidis,et al.  UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence , 2019, Remote. Sens..

[14]  Yiannis Ampatzidis,et al.  UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning , 2019, Remote. Sens..

[15]  J. Malamy,et al.  Intrinsic and environmental response pathways that regulate root system architecture. , 2005, Plant, cell & environment.

[16]  Andrea Luvisi,et al.  Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence , 2019, Comput. Electron. Agric..

[17]  Lesley A Judd,et al.  Advancements in Root Growth Measurement Technologies and Observation Capabilities for Container-Grown Plants , 2015, Plants.

[18]  G. Albrigo,et al.  Transcriptional and Microscopic Analyses of Citrus Stem and Root Responses to Candidatus Liberibacter asiaticus Infection , 2013, PloS one.

[19]  Amr H. Abd-Elrahman,et al.  A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses , 2019, Comput. Electron. Agric..

[20]  Reza Ehsani,et al.  Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado , 2018, Comput. Electron. Agric..

[21]  Yiannis Ampatzidis,et al.  Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence , 2019, Comput. Electron. Agric..

[22]  M. A. Arain,et al.  Quantitative, nondestructive estimates of coarse root biomass in a temperate pine forest using 3‐D ground‐penetrating radar (GPR) , 2017 .

[23]  Stefan Mairhofer,et al.  Quantifying the impact of soil compaction on root system architecture in tomato (Solanum lycopersicum) by X-ray micro-computed tomography. , 2012, Annals of botany.

[24]  Jan Cermak,et al.  Instrumental methods for studies of structure and function of root systems of large trees. , 2003, Journal of experimental botany.

[25]  Bert Reubens,et al.  Assessing and analyzing 3D architecture of woody root systems, a review of methods and applications in tree and soil stability, resource acquisition and allocation , 2008, Plant and Soil.

[26]  Henry Lin,et al.  Application of ground penetrating radar for coarse root detection and quantification: a review , 2012, Plant and Soil.

[27]  Tim R. Gottwald,et al.  Citrus Huanglongbing: the pathogen and its impact. , 2007 .

[28]  Susan D. Day,et al.  Contemporary Concepts of Root System Architecture of Urban Trees , 2010, Arboriculture & Urban Forestry.

[29]  Yiannis Ampatzidis,et al.  Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence , 2019, Comput. Electron. Agric..

[30]  Andrea Luvisi,et al.  X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion , 2017, Front. Plant Sci..

[31]  F. Roka,et al.  Vector control and foliar nutrition to maintain economic sustainability of bearing citrus in Florida groves affected by huanglongbing. , 2014, Pest management science.

[32]  Thomas A. Obreza,et al.  Orange Tree Fibrous Root Length Distribution in Space and Time , 2007 .

[33]  Jin Chen,et al.  Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform , 2016, Remote. Sens..

[34]  C.Y. Jim,et al.  Greening Cities: Forms and Functions , 2017 .

[35]  Andrea Luvisi,et al.  Plant Pathology and Information Technology: Opportunity for Management of Disease Outbreak and Applications in Regulation Frameworks , 2016 .

[36]  Michael H. Thomas,et al.  Citrus Greening Disease (Huanglongbing) in Florida: Economic Impact, Management and the Potential for Biological Control , 2016, Agricultural Research.

[37]  Chen Jin.  Sensitive factors analysis in using GPR for detecting plant roots based on forward modeling , 2012 .

[38]  R. Ehsani,et al.  Development and evaluation of a mobile thermotherapy technology for in-field treatment of Huanglongbing (HLB) affected trees , 2019, Biosystems Engineering.

[39]  Xihong Cui,et al.  Comment on: “root orientation can affect detection accuracy of ground-penetrating radar” , 2014, Plant and Soil.

[40]  Farid Melgani,et al.  Automatic Analysis of GPR Images: A Pattern-Recognition Approach , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Yasuhiro Hirano,et al.  Calibrating the impact of root orientation on root quantification using ground-penetrating radar , 2015, Plant and Soil.

[42]  C. Barton,et al.  Detection of tree roots and determination of root diameters by ground penetrating radar under optimal conditions. , 2004, Tree physiology.