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Artículo
A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
dc.creator | Apolo Apolo, Orly Enrique | es |
dc.creator | Pérez Ruiz, Manuel | es |
dc.creator | Martínez Guanter, Jorge | es |
dc.creator | Egea Cegarra, Gregorio | es |
dc.date.accessioned | 2020-03-24T16:29:58Z | |
dc.date.available | 2020-03-24T16:29:58Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Apolo 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.. | |
dc.identifier.issn | 2073-4395 | es |
dc.identifier.uri | https://hdl.handle.net/11441/94472 | |
dc.description.abstract | 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 21 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Crop monitoring | es |
dc.subject | Plant phenotyping | es |
dc.subject | Leaf area | es |
dc.subject | Index estimation | es |
dc.subject | Artificial intelligence | es |
dc.subject | Wheat | es |
dc.subject | Breeding | es |
dc.title | A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos | es |
dc.relation.publisherversion | 10.3390/agronomy10020175 | es |
dc.identifier.doi | doi.org/10.3390/agronomy10020175 | es |
dc.contributor.group | Universidad de Sevilla. AGR278: Smart Biosystems Laboratory AGR278 | es |
dc.journaltitle | Agronomy | es |
dc.publication.volumen | 2020 (10) | es |
dc.publication.issue | 2020 (2) | es |
dc.publication.initialPage | 1 p. | es |
dc.publication.endPage | 21 p. | es |
dc.identifier.sisius | 13382 | es |
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