# Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions

arXiv: https://arxiv.org/abs/2508.08326 · HTML: https://mlsanity.com/paper/2508.08326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 68 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [Why ResNet Works? Residuals Generalize](https://mlsanity.com/paper/1904.01367/) (2019)
- [Crop Yield Prediction Using Deep Neural Networks](https://mlsanity.com/paper/1902.02860/) (2019)
- [Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning](https://mlsanity.com/paper/2011.06296/) (2020)
- [Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT](https://mlsanity.com/paper/1812.04872/) (2018)
- [Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems](https://mlsanity.com/paper/2309.15995/) (2023)
- [Grape Cold Hardiness Prediction via Multi-Task Learning](https://mlsanity.com/paper/2209.10585/) (2022)
- [A Review of Cybersecurity Incidents in the Food and Agriculture Sector](https://mlsanity.com/paper/2403.08036/) (2024)

## Built on by (most cited first, 1 of 1)

- [AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection](https://mlsanity.com/paper/2608.11679/) (2026)

## Abstract (arXiv)

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.