Objective 1: Optimize an advanced, non-invasive, and automated root mapping system utilizing a Ground Penetrating Radar (GPR). Task 1: Evaluate and optimize the performance of the GPR. Conclusions: The goal os this task was to evaluate and optimize the performance of a ground penetrating radar (GPR) to accurately detect citrus tree roots and generate 3D morphology root maps of citrus trees that are grown in a complex field environment in southwest Florida. Several single-factor and multi-factor experiments, including root diameter, root moisture content, root depth, root spacing, survey angle, and soil moisture content, were conducted to achieve this goal. The specific conclusions and suggestions are as follows (Zhang et al., 2019): In a controlled environment, the GPR is suitable for monitoring the roots distributed in shallow soil layers with a diameter that is larger than 6 mm. The diameter of the root influences the width of the hyperbola and the intensity (strength) of the signal. As the root diameter increases, the hyperbola widens, and consequently the reflected signal is strong. The relationship between diameter and hyperbolic widths was linear under the conditions of this study for roots with a diameter of 0.5 to 5 cm. The live and dead roots were clearly distinguished in the radar profiles. The ability of the GPR system to distinguish between the live and dead roots is valuable for studying the effects of diseases, such as HLB or soil-borne pests and pathogens, on tree root growth. The direction of the survey (scan) lines strongly affect detection accuracy; keeping the survey lines perpendicular to the roots can significantly increase the GPR detection accuracy. It was difficult to identify the hyperbolas when the angle between the survey line and the direction of the root was less than 45°. Combining concentric circles with orthogonal grids would greatly improve the detection accuracy of the GPR, because roots grow in various directions. Two roots that were located in proximity cannot be clearly detected by 1600 MHz GPR when their horizontal distance is less than 10 cm and their vertical distance is less than 5 cm. Soil water content determines the dielectric constant, which affects GPR signal generation and root detection accuracy. Sandy soil (typical of southwest Florida citrus groves) has a rapid and high-water infiltration rate, which may affect GPR performance. Artificial intelligence and machine learning have been utilized to correctly identify and classify objects, such as crops, crop pests, and diseases. A similar approach could be adopted to automate the root detection procedure by analyzing and identify “root” hyperbolas that are produced by GPR, by utilizing artificial intelligence and machine learning. Task 2: Develop an automated (remote-controlled) GPR. Conclusions: A remote-controled GPR was developed and several experiments were conducted to evaluate its performance in the field. These experiments show that the remote-controlled process can reduce the required application time by 3 times when compared to a manual process. It can also reduce the human effort required and increase the precision of the data collection process. There are also some problems faced in the field with both the remote-controlled and the manual process. For both of them, initial cleanup is required to clear the debris and fruit drops under the tree. This process can take up to an average of 60 seconds per tree. The other problem faced is the presence of irrigation lines close to the trees. In few situations, they can be moved to the side and the scanning process can be done; but in some cases, the operator has to manually lift up the irrigation lines to allow the scanner to move under it. Further additional development has to focus on solving this issue. Objective 2: Compare root mapping data with data collected from commercial field trials involving different rootstock varieties and propagation methods. Conclusions: To achieve the second objective, we had to first develop an accurate model and calibration method for root depth estimation at different depths with the consideration of radar signal propagation velocity change in different depths. Several controlled experiments were conducted and advanced data post-processing techniques were developed. The following points summarize the conclusions of this study: 1) The developed calibration method combines a dielectric constant calibration in field with post-processing corrections in the lab. It uses two dielectric constants (for shallow and deep levels) to correct the detected root depths by the GPR, utilizing the soil moisture content. 2) It is a simple and practical method suitable for large-scale field data collection. It does not require measuring the thickness and propagation velocity of each soil layer, and does not need the application of recursive formulas in all soil layers (suggested by several researchers). Only three points are needed to correct the root depth measurement by the GPR in deeper soil. Because of Covid-19, we were not able to conduct more experiments in commercial orchards. Objective 3: Develop outreach to transfer technology to growers and other industry clientele. An outreach program was developed to present this technology and the results of this study to stakeholder. The outreach activities included: Extension Talks: 1. Emerging Technologies for Specialty Crops. SWFREC Foundation Board Meeting. Southwest Florida Research and Education Center (SWFREC), Immokalee, January 21, 2020. 2. Precision agriculture technologies and UAV applications in citrus. Risk Management Citrus Day, Southwest Florida Research and Education Center (SWFREC), Immokalee, May 16, 2019. 3. Precision Agriculture Technologies. In Service Training, 2019 Extension Symposium, Gainesville, Florida, May 7-8. Precision Agriculture Technologies for Specialty Crops. SWFREC Foundation Board Meeting. Southwest Florida Research and Education Center (SWFREC), Immokalee, April 23, 2019. 4. Smart Technologies and UAV applications in Citrus. Citrus Field Day, SWFREC, Immokalee, April 18, 2019. Conference Talk and Publication: 1. Derival M., Ampatzidis Y., Kakarla S.C., Xiuhua Zhang, and Albrecht U., 2018. Evaluation of HLB-Infected Citrus Rootstocks Using Ground Penetrating Radar. 14Th International Conference on Precision Agriculture (ICPA), Montreal, Canada. Journal Peer-Reviewed Publication: 1. Zhang X., Derival M., Albrecht U., and Ampatzidis Y., 2019. Evaluation of a Ground Penetrating Radar to Map Root Architecture of HLB-infected Citrus Trees. Agronomy (Special Issue: Precision Agr.), 9(7), 354. https://doi.org/10.3390/agronomy9070354. Furthermore, we are preparing two more manuscripts, which we plan to submit in high quality referred journals: A multi-point layered calibration method for citrus root depth measurement using ground penetrating radar Development and evaluation of a remote-controlled GPR system Conclusions: Because of Covid-19, we were not able to demonstrate this novel technology in the field to citrus growers and allied industry. However, the results of this project were presented in several venues as listed above.