Advances in monitoring vineyard with multiscale and multiplatform data for precision agriculture systems

We propose a machine learning approach that works as a predictive model for soil ECa, involving spatially predicting ECa based on discrete measurements obtained from a network of Time Domain Reflectometry (TDR) probes capable of measuring ECa. This methodology enables the spatial prediction of ECa values across the surveyed area. The main purpose is to create a process that using multiscale and multiplatform measurements helps the farmer monitoring and interacting with the crop in a better way, reducing resources and improving the crop productivity.