Weather-Driven Agricultural Decision-Making Under Imperfect Conditions

Our research demonstrates the usefulness of data analytics using digital twin for detecting inconsistencies in weather measurements, which are critical to various agricultural decision-making and automation tasks. By leveraging digital twin technology, we develop a modular framework named Cerealia that allows end-users to check for data inconsistencies when perfect weather feeds are unavailable. Cerealia uses neural network models to check anomalies and aids end-users in informed decision-making. We develop a prototype of Cerealia using the NVIDIA Jetson Orin platform and test it with an operational weather network established in a commercial orchard.