Digital Future Farm

A digital twin will be built for arable and dairy farming systems: the Digital Future Farm (DFF). The DFF will provide researchers and farmers with an engine to model the nitrogen cycle of mixed farm systems with two aims:

• reducing the nitrogen use of the farm while maintaining or increasing crop yields, and
• minimizing the nitrogen losses on the farm itself.

The digital twin will employ modular components, each component representing a biophysical aspect of the farm (e.g. soil, crops, animals). Each component can be made custom to a farm and linking these components will result in a full-inclusive farm-specific digital twin. A machine learning approach will be used for the twinning process.

Related publications:

• C. Pylianidis, I. N. Athanasiadis, Learning latent representations for operational nitrogen response rate prediction, Computing Research Repository, AI for Earth Sciences Workshop at 10th Int'l Conf Learning Representations (ICLR 2022), 2022, doi:10.48550/arXiv.2205.09025.
• C. Pylianidis, V. Snow, H. Overweg, S. Osinga, J. Kean, I. N. Athanasiadis, Simulation-assisted machine learning for operational digital twins, Environmental Modelling & Software, 148:105274, 2022, doi:10.1016/j.envsoft.2021.105274.
• H. Overweg, H. N. C. Berghuijs, I. N. Athanasiadis, CropGym: a Reinforcement Learning Environment for Crop Management, Computing Research Repository, AIMOCC 2021 Workshop at 9th Int'l Conf Learning Representations (ICLR 2021), 2021, doi:10.48550/arXiv.2104.04326.
• C.Pylianidis, S. Osinga, I. N. Athanasiadis, Introducing digital twins to agriculture, Computers and Electronics in Agriculture, 184:105942, 2021, doi:10.1016/j.compag.2020.105942.
• C. Pylianidis, V.Snow, D. Holzworth, J. Bryant, I. N. Athanasiadis, Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate, Lecture Notes in Computer Science (Pattern Recognition. ICPR International Workshops and Challenges), vol. 12666, pg. 96-109, 2021, Springer, doi:10.1007/978-3-030-68780-9_5.
• A. Kamilaris, V. Wohlgemuth, K. Karatzas, I.N. Athanasiadis (eds.), Advances and New Trends in Environmental Informatics: Digital Twins for Sustainability, Proceedings of the 34th EnviroInfo Conference, Progress in IS, 2021, Springer, doi:10.1007/978-3-030-61969-5.