Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions
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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.
Key facts. Cited by 1 paper 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
11 of 68 references.
References · 11
- 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
- 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
- 2020A Transformer-based Framework for Multivariate Time Series Representation Learningcited by 453
- 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
- 2019Why ResNet Works? Residuals Generalizecited by 39
- 2019Crop Yield Prediction Using Deep Neural Networkscited by 29
- 2020Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learningcited by 17
- 2018Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoTcited by 16
- 2023Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systemscited by 6
- 2022Grape Cold Hardiness Prediction via Multi-Task Learningcited by 4
- 2024A Review of Cybersecurity Incidents in the Food and Agriculture Sectorcited by 1
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Citing papers per year: 2026: 1.
1 paper.