Digital twins provide a new paradigm for the integrated use of sensor data, process-based and data-driven modelling, and user interaction, to explore the behaviour of individual objects and processes. Digital twins originate from an engineering context and were developed for machines and mainly physical and chemical processes. In this paper, we further develop an understanding of digital twins for the green life sciences, which also include biological and social processes. We report on three use cases, in precision farming, greenhouse control and personalized dietary advice, focusing on practical benefits and challenges of digital twins compared with other research methods. This research extends earlier more conceptual research on digital twins in this domain. We find benefits in increased accuracy and impact because of the real-time data connection of digital twins to their real-life counterparts. Specification, availability and accuracy of relevant data sources are still major challenges. Specifically, when using digital twins for personalized advice, further research is needed on nontechnical aspects so that users will comply with the advice from the digital twins. We have outlined four directions of future research and expect that further research will include data-driven modelling to simulate the complex character of living objects and processes and at the same time develop approaches to limit the amount of required input data.