Artículo
Leaf area index estimations by deep learning models using RGB images and data fusion in maize
Autor/es | Castro Valdecantos, Pedro
Apolo Apolo, Orly Enrique Pérez Ruiz, Manuel Egea, G. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería Aerospacial y Mecánica de Fluidos |
Fecha de publicación | 2022-08-05 |
Fecha de depósito | 2023-12-27 |
Publicado en |
|
Resumen | 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 ... 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. |
Cita | 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. |
Ficheros | Tamaño | Formato | Ver | Descripción |
---|---|---|---|---|
Aportación 06.pdf | 1.171Mb | [PDF] | Ver/ | |