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 (g_s) 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 g_s 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 g_s 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 g_s 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 g_s 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.