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

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 phenoty...

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Main Authors: Orly Enrique Apolo-Apolo, Manuel Pérez-Ruiz, Jorge Martínez-Guanter, Gregorio Egea
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Agronomy
Subjects:
Online Access:https://www.mdpi.com/2073-4395/10/2/175
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spelling doaj-f4bb3e222c9144c08a6592501e3700242021-04-02T08:40:39ZengMDPI AGAgronomy2073-43952020-01-0110217510.3390/agronomy10020175agronomy10020175A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding TrialsOrly Enrique Apolo-Apolo0Manuel Pérez-Ruiz1Jorge Martínez-Guanter2Gregorio Egea3Area of Agroforestry Engineering, Technical School of Agricultural Engineering (ETSIA), Universidad de Sevilla. Ctra. Utrera km 1, 41013 Sevilla, SpainArea of Agroforestry Engineering, Technical School of Agricultural Engineering (ETSIA), Universidad de Sevilla. Ctra. Utrera km 1, 41013 Sevilla, SpainArea of Agroforestry Engineering, Technical School of Agricultural Engineering (ETSIA), Universidad de Sevilla. Ctra. Utrera km 1, 41013 Sevilla, SpainArea of Agroforestry Engineering, Technical School of Agricultural Engineering (ETSIA), Universidad de Sevilla. Ctra. Utrera km 1, 41013 Sevilla, SpainRemote 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.https://www.mdpi.com/2073-4395/10/2/175plant phenotypingleaf areaindex estimationartificial intelligencewheatbreedingcrop monitoring
collection DOAJ
language English
format Article
sources DOAJ
author Orly Enrique Apolo-Apolo
Manuel Pérez-Ruiz
Jorge Martínez-Guanter
Gregorio Egea
spellingShingle Orly Enrique Apolo-Apolo
Manuel Pérez-Ruiz
Jorge Martínez-Guanter
Gregorio Egea
A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
Agronomy
plant phenotyping
leaf area
index estimation
artificial intelligence
wheat
breeding
crop monitoring
author_facet Orly Enrique Apolo-Apolo
Manuel Pérez-Ruiz
Jorge Martínez-Guanter
Gregorio Egea
author_sort Orly Enrique Apolo-Apolo
title A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
title_short A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
title_full A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
title_fullStr A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
title_full_unstemmed A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
title_sort mixed data-based deep neural network to estimate leaf area index in wheat breeding trials
publisher MDPI AG
series Agronomy
issn 2073-4395
publishDate 2020-01-01
description 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.
topic plant phenotyping
leaf area
index estimation
artificial intelligence
wheat
breeding
crop monitoring
url https://www.mdpi.com/2073-4395/10/2/175
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