Water deficit is one of the most important environmental factors limiting
sustainable crop yields and it requires a reliable tool for fast and precise
quantification. In this work we use simultaneously recorded signals of
photoinduced prompt fluorescence (PF) and delayed fluorescence (DF) as well as
modulated reflection (MR) of light at 820 nm for analysis of the changes in
the photosynthetic activity in detached bean leaves during drying. Depending
on the severity of the water deficit we identify different changes in the
primary photosynthetic processes. When the relative water content (RWC) is
decreased to 60% there is a parallel decrease in the ratio between the rate of
excitation trapping in the Photosystem (PS) II reaction center and the rate of
reoxidation of reduced PSII acceptors. A further decrease of RWC to 20%
suppresses the electron transfer from the reduced plastoquinone pool to the
PSI reaction center. At RWC below values 15%, the reoxidation of the
photoreduced primary quinone acceptor of PSII, QA–, is inhibited and at less
than 5%, the primary photochemical reactions in PSI and II are inactivated.
Using the collected sets of PF, DF and MR signals, we construct and train an
artificial neural network, capable of recognizing the RWC in a series of
“unknown” samples with a correlation between calculated and gravimetrically
determined RWC values of about R2 ≈ 0.98. Our results demonstrate that this is
a reliable method for determination of RWC in detached leaves and after
further development it could be used for quantifying of drought stress of crop
plants in situ. This article is part of a Special Issue entitled:
Photosynthesis Research for Sustainability: from Natural to Artificial