2020-03-242020-03-242020Apolo Apolo, O.E., Pérez Ruiz, M., Martínez Guanter, J. y Egea Cegarra, G. (2020). A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials. Agronomy, 2020 (10) (2020 (2)), 1 p.-21 p..2073-4395https://hdl.handle.net/11441/94472Remote 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.application/pdf21 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Crop monitoringPlant phenotypingLeaf areaIndex estimationArtificial intelligenceWheatBreedinginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessdoi.org/10.3390/agronomy1002017513382