PATHFINDER: Precision Agriculture Through High-Fidelity Flight Navigation and Exploration Routing

The PATHFINDER project leverages UAV technology and deep learning advancements for efficient agricultural mapping, aiming to overcome current UAV limitations through an adaptive, high-resolution data capture methodology. Traditional UAV flight plans are rigid and lack responsiveness to real-time changes, but PATHFINDER introduces an online planning algorithm using a Gaussian Process (GP) regressor for real-time data integration and uncertainty mapping. This approach optimizes UAV paths, focusing on high-information areas and varying altitudes to maximize efficiency and information gain. The project combines desk-based algorithm development with practical in-field testing, ensuring robust, applicable solutions, and moves away from orthophoto representation, exploring alternative formats and a discretized approach to reduce computational complexity. The student involved in PATHFINDER will gain hands-on experience in developing and testing advanced algorithms, conducting simulated and real-world data collection, enhancing their skills in computer science, robotics, agricultural science, and data analytics, and solving real-world agricultural mapping challenges, providing a unique blend of theoretical knowledge and practical application to equip them for future careers in UAV technology and agricultural remote sensing.