Biosensor for improved crop light use efficiency

Chlorophyll fluorescence (CF) is a signal that is intimately connected to photosynthesis, and hundreds of thousands of scientific publications attest to its fundamental importance to plant biology. Despite this, its use in crop monitoring applications is very limited, largely because conventional approaches of recording CF rely on high-intensity flashes of light, which are difficult to apply in a crop production setting. We propose to explore a relatively overlooked property of CF, namely the transient behavior of CF after small step changes in light intensity. Parameters derived from CF transients have previously been shown to correlate well with photosynthesis rate, and are sensitive to abiotic stresses. Other preliminary data indicate that the ratio of CF measured in the red and far-red range is an indicator of crop biomass, suggesting that close monitoring of biomass increments may be possible, but this needs to be confirmed in practice. We aim to (i) train a machine learning based model to predict photosynthesis rate from CF, (ii) use CF measured at several wavelengths to estimate crop growth rate, (iii) use CF as a signal in supplemental lighting control with the aim of improving light use efficiency of the growth system, and (iv) test the validity of our approach in several genotypes. The results obtained may help revolutionize the field of crop monitoring, which is highly relevant for future energy savings in greenhouses and vertical farms. Results from this project may innovate monitoring applications in the prevention of abiotic stress conditions and early detection of diseases.

Chlorophyll fluorescence (CF) is a signal that is intimately connected to photosynthesis, and hundreds of thousands of scientific publications attest to its fundamental importance to plant biology. Despite this, its use in crop monitoring applications is very limited, largely because conventional approaches of recording CF rely on high-intensity flashes of light, which are difficult to apply in a crop production setting. We propose to explore a relatively overlooked property of CF, namely the transient behavior of CF after small step changes in light intensity. Parameters derived from CF transients have previously been shown to correlate well with photosynthesis rate, and are sensitive to abiotic stresses. Other preliminary data indicate that the ratio of CF measured in the red and far-red range is an indicator of crop biomass, suggesting that close monitoring of biomass increments may be possible, but this needs to be confirmed in practice. We aim to (i) train a machine learning based model to predict photosynthesis rate from CF, (ii) use CF measured at several wavelengths to estimate crop growth rate, (iii) use CF as a signal in supplemental lighting control with the aim of improving light use efficiency of the growth system, and (iv) test the validity of our approach in several genotypes. The results obtained may help revolutionize the field of crop monitoring, which is highly relevant for future energy savings in greenhouses and vertical farms. Results from this project may innovate monitoring applications in the prevention of abiotic stress conditions and early detection of diseases.