Upon absorbing light, a plant channels the photons' energy to fuel photosynthesis. Yet, a fraction of this energy is lost, radiated back by the chlorophyll in the form of fluorescence. This "wasted" energy carries information on the well-being of the plant, akin to the way the content of a trash bin can tell something about its owner. When a complex light pattern is shone upon the plant, the signature of the fluorescence response to the pattern reflects the state of the photosynthetic chain. My current main focus is to extract from plant fluorescence the most relevant information for phenotyping tasks: how do we ask questions with light? How do we interpret the signatures of the responses? What knowledge can we uncover about the plant? Armed with replicable Open Source instrumentation and machine learning, I aim to explore this photonic language.

Keywords

Plant phenotyping
Fluorescence
Open-Source
Machine Learning
Sustainable