Khalid Mosalam

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.

The project aims to develop digital twin and machine learning algorithms for optimizing LED-driven pod-based hydroponic farming, with a focus on addressing plant pathogen challenges, offering potential impacts on predicting pathogen spread, decontamination, simulating hydroponic systems, and revolutionizing the food processing industry if successful.