Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in
the last few decades as the direct and indirect methods available are laborious and
time-consuming. The recent emergence of high-throughput plant phenotyping platforms has
increased the need to develop new phenotyping tools for better decision-making by breeders. In
this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue
(RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The
model mixes numerical data collected in a wheat breeding field and visual features extracted from
the images to make rapid and accurate LAI estimations. Model-based LAI estimations were
validated against LAI measurements determined non-destructively using an allometric
relationship obtained in this study. The model performance was also compared with LAI estimates
obtained by other classical indirect methods based on bottom-up hemispherical images and gaps
fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The
model performance was slightly better than that of the hemispherical image-based method, which
tended to underestimate LAI. These results show the great potential of the developed model for
near real-time LAI estimation, which can be further improved in the future by increasing the
dataset used to train the model
