Estimating stomatal conductance in maize from 3D plant scans

Given conflicts for blue water are projected to exacerbate, optimising irrigation will be increasingly crucial. Despite stomatal conductance (gs) being among the variables with the greatest potential to quantify crop water status, the difficulties and the cost of performing measurements have prevented its use in operational contexts. A model is proposed for estimating gs in maize from smartphone-based 3D leaf scans, as a function of leaf insertion angle of the penultimate leaf and the degree of leaf curvature in the top canopy layers. The model was evaluated – against gs measurements from an infrared gas analyser (IRGA) – for three maize hybrids using data from a dedicated pot experiment where different irrigation treatments were applied. The agreement between gs values from IRGA and from the proposed model was satisfactory for two hybrids (R2 = 0.78 and 0.73), whereas slightly poorer results were achieved for the third one (R2 = 0.51). The three hybrids responded to water stress by adopting different behaviours in terms of reducing/increasing insertion angles and of straightening/curving leaf blades, leading to genotype-specific coefficients for the two predictors. The relationships between gs and canopy architectural indicators could be implemented in monitoring platforms based on LIDAR or multi-view stereo imaging, opening new opportunities for developing improved systems to optimise irrigation under operational farming conditions.