By offering a dynamic, real-time virtual representation of physical systems, digital twin technology can enhance data-driven decision-making in digital agriculture. Our research shows how digital twins are useful for detecting inconsistencies in agricultural weather data measurements, which are key attributes for various agricultural decision-making and automation tasks. 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 as well as publicly available weather datasets.
@article{arxiv.2508.08326,
title = {Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions},
author = {Tamim Ahmed and Monowar Hasan},
journal= {arXiv preprint arXiv:2508.08326},
year = {2026}
}