Cybele

Fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics

CYBELE aspires at demonstrating how the convergence of HPC, Big Data, Cloud Computing and IoT can revolutionize farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits. CYBELE generates innovation and creates value in the domain of agri-food, and its verticals in the sub-domains of precision agriculture, livestock and fish farming, as demonstrated by nine real-life industrial cases to be supported, empowering capacity building within the industrial and research community.

CYBELE intends to safeguard that stakeholders have integrated, unmediated access to a vast amount of large-scale datasets of diverse types from a variety of sources. Stakeholders are capable of generating value and extracting insights because of the secure and unmediated access to large-scale HPC infrastructures, supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power.

Our team at Wageningen University and Research leads research activities for the definition of the technical specifications of the CYBELE platform towards the execution of all nine demonstrators, and how data-driven decision making may lead to scalable implementations using scientific workflows.

Related publications:

• D. Paudel, D. Marcos, A. de Wit, H. Boogaard, I.N. Athanasiadis, A weakly supervised framework for high-resolution crop yield forecasts, Environmental Research Letters, 18:094062, 2023, doi:10.1088/1748-9326/acf50e.
• D. Paudel, A. de Wit, H. Boogaard, D. Marcos, S. Osinga, I.N. Athanasiadis, Interpretability of deep learning models for crop yield forecasting, Computers and Electronics in Agriculture, 206:107663, 2023, doi:10.1016/j.compag.2023.107663.
• D.R. Paudel, D. Marcos, A. de Wit, H. Boogaard, I. N. Athanasiadis, A weakly supervised framework for high-resolution crop yield forecasts, Computing Research Repository, AI for Earth Sciences Workshop at 10th Int'l Conf Learning Representations (ICLR 2022), 2022, doi:10.48550/arXiv.2205.09016.
• D. Paudel, H. Boogaard, A. de Wit, M. van der Velde, M. Claverie, L. Nisini, S. Janssen, S. Osinga, I. N. Athanasiadis, Machine learning for regional crop yield forecasting in Europe, Field Crops Research, 276:108377, 2022, doi:10.1016/j.fcr.2021.108377.
• S. Osinga, D.Paudel, S. A. Mouzakitis, I. N. Athanasiadis, Big data in agriculture: Between opportunity and solution, Agricultural Systems, 195:103298, 2022, doi:10.1016/j.agsy.2021.103298.
• M. van der Voort, D. Jensen, C. Kamphuis, I. N. Athanasiadis, A. De Vries, H.Hogeveen, Invited review: Toward a common language in data-driven mastitis detection research, Journal of Diary Science, 104(10):10449-10461, 2021, doi:10.3168/jds.2021-20311.
• D.Paudel, H. Boogaard, A. de Wit, S.Janssen, S. Osinga, C. Pylianidis, I. N.Athanasiadis, Machine learning for large-scale crop yield forecasting, Agricultural Systems, 187:103016, 2021, doi:10.1016/j.agsy.2020.103016.
• D. Schokker, I. N. Athanasiadis, B. Visser, R. F. Veerkamp, C. Kamphuis, Storing, combining and analysing turkey experimental data in the Big Data era, Animal, 14(11):2397-2403, 2020, doi:10.1017/S175173112000155X.
• S. Mouzakitis, G. Tsapelas, S. Pelekis, S. Ntanopoulos, D. Askounis, S. Osinga, I. N. Athanasiadis, Investigation of common big data analytics and decision-making requirements across diverse precision agriculture and livestock farming use cases, Environmental Software Systems. Data Science in Action, IFIP Advances in Information and Communication Technology, vol. 554, pg. 139–150, 2020, Springer Verlag, doi:10.1007/978-3-030-39815-6_14.