| Introducing digital twins to agriculture C Pylianidis, S Osinga, IN Athanasiadis Computers and Electronics in Agriculture 184, 105942, 2021 | 418 | 2021 |
| Machine learning for large-scale crop yield forecasting D Paudel, H Boogaard, A de Wit, S Janssen, S Osinga, C Pylianidis, ... Agricultural Systems 187, 103016, 2021 | 237 | 2021 |
| Big data in agriculture: Between opportunity and solution SA Osinga, D Paudel, SA Mouzakitis, IN Athanasiadis Agricultural Systems 195, 103298, 2022 | 105 | 2022 |
| Machine learning for regional crop yield forecasting in Europe D Paudel, H Boogaard, A de Wit, M van der Velde, M Claverie, L Nisini, ... Field Crops Research 276, 108377, 2022 | 74 | 2022 |
| Road extraction from multi-temporal satellite images by an evidential reasoning approach J Van Cleynenbreugel, SA Osinga, F Fierens, P Suetens, A Oosterlinck Pattern Recognition Letters 12 (6), 371-380, 1991 | 58 | 1991 |
| Interpretability of deep learning models for crop yield forecasting D Paudel, A De Wit, H Boogaard, D Marcos, S Osinga, IN Athanasiadis Computers and Electronics in Agriculture 206, 107663, 2023 | 44 | 2023 |
| Simulation-assisted machine learning for operational digital twins C Pylianidis, V Snow, H Overweg, S Osinga, J Kean, IN Athanasiadis Environmental Modelling & Software 148, 105274, 2022 | 32 | 2022 |
| What we want to know about our food: consumer values across countries SA Osinga, GJ Hofstede | 19 | 2004 |
| Transparency in the pork supply chain: comparing China and the Netherlands SA Osinga, GJ Hofstede | 15 | 2006 |
| Emergent results of artificial economics (series: lecture notes in economics and mathematical systems) S OSINGA, GJ HOFSTEDE, T VERWAART | 11* | 2011 |
| IN Athanasiadis Introducing digital twins to agriculture., 2021, 184 C Pylianidis, S Osinga DOI: https://doi. org/10.1016/j. compag, 105942, 2020 | 10 | 2020 |
| Investigation of common big data analytics and decision-making requirements across diverse precision agriculture and livestock farming use cases S Mouzakitis, G Tsapelas, S Pelekis, S Ntanopoulos, D Askounis, ... Environmental Software Systems. Data Science in Action: 13th IFIP WG 5.11 …, 2020 | 10 | 2020 |
| Simulating compliance behaviour for effective inspection strategies using agent based modelling E van Asselt, S Osinga, H Bremmers British Food Journal 118 (4), 809-823, 2016 | 10 | 2016 |
| Normative, cultural and cognitive aspects of modelling policies V Dignum, F Dignum, SA Osinga, GJ Hofstede Proceedings of the 2010 Winter Simulation Conference, 720-732, 2010 | 9 | 2010 |
| An agent-based model of information management in the Chinese pig sector: top-down versus bottom-up SA Osinga, O Roozmand, MR Kramer, GJ Hofstede 9th Wageningen Int. Conf. on Chain and Network Management (WICaNeM 2010 …, 2010 | 8* | 2010 |
| Combining Telecom Data with Heterogeneous Data Sources for Traffic and Emission Assessments—An Agent-Based Approach N Grujić, S Brdar, S Osinga, GJ Hofstede, IN Athanasiadis, M Pljakić, ... ISPRS International Journal of Geo-Information 11 (7), 366, 2022 | 5 | 2022 |
| The knowledge management arena: Agent-based modelling of the pig sector SA Osinga PQDT-Global, 2015 | 5 | 2015 |
| Agent Based Modeling: Het simuleren van nalevingsgedrag ED Asselt, SA Osinga, M Asselman, P Sterrenburg Beleidsonderzoek Online 2012 (19), 1-6, 2012 | 5* | 2012 |
| How to support GMP in model-based DSS H Scholten, SA Osinga 15th JISR-IIASA Workshop on Methodologies and Tools for Complex System …, 2001 | 4 | 2001 |
| Introducing digital twins to agriculture C Pylianidis, S Osinga, IN Athanasiadis Computers and Electronics in Agriculture 184, 2020 | 3 | 2020 |
