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Leaf area index estimations by deep learning models using RGB images and data fusion in maize
dc.creator | Castro Valdecantos, Pedro | es |
dc.creator | Apolo Apolo, Orly Enrique | es |
dc.creator | Pérez Ruiz, Manuel | es |
dc.creator | Egea, G. | es |
dc.date.accessioned | 2023-12-27T12:47:35Z | |
dc.date.available | 2023-12-27T12:47:35Z | |
dc.date.issued | 2022-08-05 | |
dc.identifier.citation | Castro Valdecantos, P., Apolo Apolo, O.E., Pérez Ruiz, M. y Egea, G. (2022). Leaf area index estimations by deep learning models using RGB images and data fusion in maize. Precision Agriculture, 23, 1949-1966. https://doi.org/10.1007/s11119-022-09940-0. | |
dc.identifier.issn | 1573-1618 | es |
dc.identifier.uri | https://hdl.handle.net/11441/152832 | |
dc.description.abstract | The leaf area index (LAI) is a biophysical crop parameter of great interest for agronomists and plant breeders. Direct methods for measuring LAI are normally destructive, while indi rect methods are either costly or require long pre- and post-processing times. In this study, a novel deep learning-based (DL) model was developed using RGB nadir-view images taken from a high-throughput plant phenotyping platform for LAI estimation of maize. The study took place in a commercial maize breeding trial during two consecutive grow ing seasons. Ground-truth LAI values were obtained non-destructively using an allometric relationship that was derived to calculate the leaf area of individual leaves from their main leaf dimensions (length and maximum width). Three convolutional neural network (CNN)- based DL model approaches were proposed using RGB images as input. One of the models tested is a classifcation model trained with a set of RGB images tagged with previously measured LAI values (classes). The second model provides LAI estimates from CNN based linear regression and the third one uses a combination of RGB images and numeri cal data as input of the CNN-based model (multi-input model). The results obtained from the three approaches were compared against ground-truth data and LAI estimations from a classic indirect method based on nadir-view image analysis and gap fraction theory. All DL approaches outperformed the classic indirect method. The multi-input_model showed the least error and explained the highest proportion of the observed LAI variance. This work represents a major advance for LAI estimation in maize breeding plots as compared to pre vious methods, in terms of processing time and equipment costs. | es |
dc.format | application/pdf | es |
dc.format.extent | 18 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Precision Agriculture, 23, 1949-1966. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | LAI | es |
dc.subject | Neural network | es |
dc.subject | Nadir-view images | es |
dc.subject | Phenotyping platform | es |
dc.subject | Zea mays | es |
dc.title | Leaf area index estimations by deep learning models using RGB images and data fusion in maize | 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 Aerospacial y Mecánica de Fluidos | es |
dc.identifier.doi | 10.1007/s11119-022-09940-0 | es |
dc.journaltitle | Precision Agriculture | es |
dc.publication.volumen | 23 | es |
dc.publication.initialPage | 1949 | es |
dc.publication.endPage | 1966 | es |
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Aportación 06.pdf | 1.171Mb | ![]() | Ver/ | |