This interdisciplinary project demands a blend of knowledge and expertise in wireless communications, signal processing, microwave/RF imaging, and applied ML. The SoilX Project aims to develop a technology that facilitates efficient megafarm irrigation. It redesigns a radar integrates it with drones and equips it with Artificial intelligence (AI) for soil root-zone moisture classification. Farm irrigation designers will use the classification to optimally allocate irrigation equipment and water resources to different portions of the megafarm. The technology will create a high-speed 3D soil moisture map. The radar signal waveform design, processing, and AI algorithms will be investigated via our developed radar and in-situ lab-based measurements.  More information about this project can be found on the SoilX project page. 

My responsibilities:

• Led a team of 6 PhD and 5 undergraduate students, defining research projects, managing their tasks, and tracking progress.

• Collaborated on the prototype implementation of an Intelligent Ground Penetrating Radar (GPR).

• Authored data science-related Grant Proposals focusing on 3D Non-Invasive Subsurface Imaging.

• Led the design and implementation of machine learning-based signal feature extraction for GPR.

• Mentored team members on GPR Data Analysis and end-to-end software development processes.

• Directed the GPR/moisture probe data measurement campaign.

• Coordinated the drafting of a review paper with multiple collaborators.

• Managed the process of purchasing equipment and supplies, including Moisture Probe, Radar Components, Drone, and EMC absorbers.

• Supported the establishment of the SoilX Lab, including setting up indoor and outdoor components.