Chlorophyll Fluorescence Biofeedback System


van Iersel, M. W., Weaver, G., Martin, M. T., Ferrarezi, R. S., Mattos, E., & Haidekker, M.

(2016). A Chlorophyll Fluorescence-based Biofeedback System to Control Photosynthetic Lighting in Controlled Environment Agriculture, Journal of the American Society for Horticultural Science J. Amer. Soc. Hort. Sci.141(2), 169-176.


Controlled environment agriculture (heavily managed, indoor production facilities that include greenhouses, growing facilities, and indoor farms) has become an increasingly important part of agriculture in the world.  But, it is expensive as electricity and resources in these systems can quickly push up produce price. It makes sense to optimize energy costs to produce the most food (edible biomass) possible. By reducing inefficiency, it might be able to optimize the system to maximize the economics of this industry.

The authors chose to examine chlorophyll fluorescence as a way to address this problem. When light energy encounters a plant, the leaves absorb the light to power the electron transport chain (ETC – a system that produces sugar, the primary food for plants). As this happens, some of the light is reflected or absorbed and does not help power the ETC. One of the byproducts is a fluorescent excitation of molecules that can be measured and accurately predict how well the plant incorporates light into the ETC versus how much light is lost due to efficiency. Research has shown that measuring this can help predict the efficiency of photosystem II, one of the integral parts of the ETC. Written as ɸPSII, this efficiency reduces over the course of a day due to some molecules being degraded and then losing functionality (D1 proteins). Another way to reduce ɸPSII is through non-photochemical quenching (NPQ). NPQ results from too much heat in the leaf that cannot be dissipated. So, the plant makes molecules (called Xanthophylls) to deal with the excess heat and help dissipate it. The authors think that they should measure and target a certain level of ETC efficiency with a certain level of light (photosynthetic photon flux density, or PPFD in µmol m-2 s-1) and attain a specific electron transport rate (ETR) by using a feedback system that automatically adjusts light. If the model works, they believe it can be scaled up to be used in a larger setting and reduce cost.

Materials and Methods

The study revolves around the setup and use of a biofeedback control system that measures ɸPSII and calculates ETR using a chlorophyll fluorometer called a MINI-PAM. This device is clamped onto one portion of the leaf, and the established plants are tested in a growth chamber. The biofeedback system takes fluorescence measurements, calculates ETR, compares it to the target ETR (ETRT), and adjusts the LED duty cycle (on/off rate) to increase or decrease the light available to the plant. This is all done automatically based on short term averages. The MINI-PAM applies a saturating pulse of light and then measures the reflected light to estimate various metrics including fluorescence, ETR, NPQ, and ɸPSII.

The authors selected three plants with varying levels of light requirements: Pothos (low light), Lettuce (intermediate light), and Sweetpotato (high light).

They used two different methods to adjust ETR: 1) Maintain constant ETR for 16 h and 2) Increase ETR from 0 to maximum ETR in 7 steps (for 1 h each), and then back down to 0. The ETRT for each plant species was selected based on preliminary data not shown here.



Maintaining a Stable ETR:


Lettuce was the only plant examined for stable ETR. It was tested at ETRT of 70 and 100 µmol m-2 s-1 (unit is related to amount of light applied to the plant), with 70 being the maximum ETRT determined for the crop. It was found that ɸPSII was initially low (around 0.5, while healthy leaves should be close to 0.7 or 0.8), but that was the case due to the need for reaction centers to open and produce ATP and NADP. These molecules assist portions of the ETC and are produced by the Calvin Cycle, which begins running off of byproducts of photosynthesis. So, as these molecules were replenished by the Calvin Cycle, ɸPSII quickly increased to ~0.7 and ~0.6 for ETRT of 70 and 100 µmol m-2 s-1 respectively. The ETR at 70 µmol m-2 s-1 was much more tightly controlled than at 100 µmol m-2 s-1. This is partially due to the biofeedback control system only needing to adjust the lower ETRT slightly, as the higher ETRT was more unstable. The increased variation in the higher ETRT was a result of increased NPQ that reduced ɸPSII and caused a PPFD increase over time (20 µmol m-2 s-1). So, the higher ETRT caused instability over the course of the day that increased NPQ, reduced ɸPSII, and caused a need for increased PPFD that increased cost.

Stepwise ETR

This stepwise experiment was conducted using all three plants (lettuce, pothos, and sweetpotato). The maximum ETR was achieved by seven equal increases in ETRT from 0, and seven equal decreases in ETRT back to 0. Each step lasted 1 h. During the initial steps up, the ETR was more tightly controlled by the PPFD. Little variation occurred when compared to the decreasing steps (which also required a higher PPFD, due to the decrease in ɸPSII that occurs over the course of the day). There was a decrease in ɸPSII as ETRand PPFD increased. In all three species, this decrease was associated with an increase in NPQ, which is typical. In lettuce and pothos, NPQ increased throughout the trial, but also decreased as ETRT decreased. In sweetpotato, it increased differently, meaning that the xanthophyll production is variable between species. Due to the differing relationship between NPQ and ɸPSII, it indicated that the period of decreasing ETRT was caused by photoinhibition and not NPQ, or else several measures of chlorophyll fluorescence would have responded differently. This was confirmed as ɸPSII was restored after a period of darkness that allowed the D1 protein to be re-synthesized.


Chlorophyll fluorescence is a tool that can be used to assess the efficiency of lighting and plant environmental conditions in order to optimize a system. Based on the physiological metrics of the plant, the lighting conditions can be controlled in greenhouses, vertical farms, and other production systems in order to increase efficiency and limit wasted energy and cost. More biofeedback systems can be designed an implemented in order to create an ideal growing condition.