We exploit the endogenous fluorescence of photosynthetic organisms as a reporter of photosynthesis. Several instruments have been developed to measure and image this fluorescence but the interpretation of the outputs as well as the possibility of implementation outdoors is still limited. Most of these methods work in the time domain, by sending a pulse train of light flashes, and require the plant to be kept in the dark in order to have a stable reference to interpret the levels of fluorescence.

Most engineers would prefer working in the frequency domain where we can finely tune parameters such as the modulation frequency (over 6 orders of magnitude), the constant offset of the light, the amplitude of the modulation, and where we can use standard signal analysis techniques such as studying resonance and phase shifts, or harmonics due to the system’s nonlinearity and so on. So that is what we are going to do. In addition, we will couple the instrument to a flexible machine learning back-end to retrieve signatures that are characteristic of the health status of the photosynthetic organisms.

With this new method, which is inspired from dynamical chemistry protocols, we can collect data with a higher signal-over-noise ratio, in the presence of background noise such as ambient light, and without dark-adapting the plants. All major advantages over the previous instruments that should speed up its adoption in practical outdoor applications.