Mark Mueller

This project supports sustainable agriculture by using AI to reduce fertilizers, pesticides, and water while maintaining crop quality and yield through improved modeling and precision application. It focuses on soil and hydroponic crops. Part A covers modeling, simulation, and data integration, optimizing resource use, while Part B focuses on field data hardware.

This project is paired with a Part A. In Part B (this proposal) we consider the hardware capable of gathering the required data in the field, including sensors and robotics. The complementary Part A focuses on modeling and simulation for water, pesticides, and fertilizers in fields.

This UC Berkeley and UC Davis project develops low-cost wireless soil sensors and AI algorithms, tested in controlled settings and commercial fields using a UAV-equipped wireless readout system, with the goal of delivering affordable real-time soil monitoring tools for improved agricultural practices and environmental stewardship.