Publikation
Graph Signal Processing (GSP) Unearths the Best Locations for Soil Moisture Sensors
Jurgen van den Hoogen; Dan Hudson; Martin Atzmueller
In: Proc. International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications (ICMLA-2024), Pages 1-6, IEEE, 2024.
Zusammenfassung
In this paper, we apply graph signal processing to optimise a soil moisture sensor network by identifying and re- moving redundant sensors. We evaluated which of seven proposed graph construction techniques best models the relationships between soil moisture measurements at different places in an agricultural field. Here, we consider a sensor location to be redundant if the moisture value can be imputed from information elsewhere in the graph. We gradually remove redundant sensors from the network in a top-down manner, imputing the masked sensors using Tikhonov minimisation – looking for the graph structure that gives us the most accurate imputed values. Our results indicate that the thresholded Gaussian kernel has the best performance in terms of error, while Delaunay triangulation, a parameter-free method, performs similarly. Furthermore, as expected, it seems that the edge sensors are most important while sensors close-by each other or in the centre of the field seem to be less relevant