Plant-Machine Interactions

Plant research and research-based innovation is largely limited by the lack of equipment on the market that can subject plants to multiple simultaneous changes in environmental conditions in a controlled manner and measure plant performance at the same time. We have developed a Minibiosphere platform that, in addition to controlling conditions and measuring plant responses, also enables intelligent AI-based feedback control of conditions. The project combining photosynthesis, IoT and AI reserch uses the platform for research on AI-based optimisation of indoor agriculture and micro algae-based photobiological conditions, and as well for novel AI-assisted research aiming at deep understanding of photosynthesis related gene-environment interactions.


Currently, there is no cost-effective lab-scale technology to test a range of dynamic conditions, such as light, temperature and drought, or to use machine learning and AI for feedback control. The lack of technology makes it difficult to test how different genes affect plant performance under different conditions, and prevents or slows the transfer of knowledge from the lab to the development of more productive and sustainable crops. The lack of appropriate technology also hinders research into the energy efficiency of indoor farming and the photobiological production of proteins and chemicals by algae. The project aims to address bottlenecks in plant biology research and innovation, and to maximise the efficiency of indoor growing and photobiological production.


The project develops hardware, data systems and methods for plant phenotyping and exploit the technology to be developed for (i) automatic spectral optimisation of LED lamps for research and commercial use (ii) holistic AI-based optimisation of indoor growing conditions (iii) AI-based phenotyping of mutant plants (iv) machine learning-based interpretation of fluorescence and other data. The project aims to deepen understanding of the regulation of photosynthesis, develop equipment to enable interaction between photosynthetic organisms and AI, and commercialise research equipment and solutions to maximise the energy efficiency of indoor farming.


The lack of enabling technology currently limits plant research and innovation. The technology developed in this project has the potential to revolutionise plant research by understanding how a particular gene affects plant function in a particular environment. Secondly, Europe's anti-GMO policy has made it difficult to apply plant research and the knowledge to be exploited leaks across borders. However, the rapid development of indoor farming technologies and the growing need to adapt to climate change and unpredictable energy prices are opening up a whole new way of applying photosynthesis and plant science knowledge to technology research and development.