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Open AccessArticle

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Area of Agroforestry Engineering, Technical School of Agricultural Engineering (ETSIA), Universidad de Sevilla. Ctra. Utrera km 1, 41013 Sevilla, Spain
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Agronomy 2020, 10(2), 175; https://doi.org/10.3390/agronomy10020175
Received: 27 November 2019 / Revised: 15 January 2020 / Accepted: 16 January 2020 / Published: 26 January 2020
(This article belongs to the Special Issue Selected Papers from 10th Iberian Agroengineering Congress)
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. View Full-Text
Keywords: plant phenotyping; leaf area; index estimation; artificial intelligence; wheat; breeding; crop monitoring plant phenotyping; leaf area; index estimation; artificial intelligence; wheat; breeding; crop monitoring
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MDPI and ACS Style

Apolo-Apolo, O.E.; Pérez-Ruiz, M.; Martínez-Guanter, J.; Egea, G. A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials. Agronomy 2020, 10, 175.

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