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[2508.08326] Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions
Summary
This article explores the use of digital twin technology in agriculture, focusing on its ability to enhance decision-making under imperfect weather data conditions through a framework called Cerealia.
Why It Matters
As climate change impacts agricultural practices, the ability to make informed decisions based on accurate weather data becomes crucial. This research highlights how digital twins can mitigate data inconsistencies, ultimately improving agricultural productivity and sustainability.
Key Takeaways
- Digital twins provide real-time virtual representations to enhance decision-making in agriculture.
- Cerealia framework detects inconsistencies in weather data, crucial for agricultural automation.
- The prototype was tested in a commercial orchard, demonstrating practical applications.
Computer Science > Machine Learning arXiv:2508.08326 (cs) [Submitted on 10 Aug 2025 (v1), last revised 14 Feb 2026 (this version, v2)] Title:Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions Authors:Tamim Ahmed, Monowar Hasan View a PDF of the paper titled Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions, by Tamim Ahmed and Monowar Hasan View PDF HTML (experimental) Abstract: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. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2508.08326 [cs.LG] (or arXiv:2508.08326v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2508.08326 Focus to learn more arXiv-issued D...
