RT info:eu-repo/semantics/article
T1 Temporal forecasting of plant height and canopy diameter from RGB images using a CNN-based regression model for ornamental pepper plants (Capsicum spp.) growing under high-temperature stress
A1 Ruiz González, Rubén
A1 Nascimento, Antonia Maiara Marques do
A1 Santos, Marcos Bruno da Costa
A1 Porto, Rutten Kécio Soares de Brito
A1 Medeiros, Artur Mendes
A1 Santos, Fábio Sandro dos
A1 Martínez-Martínez, Víctor
A1 Barroso, Priscila Alves
K1 Image-based temporal prediction
K1 Morphological parameters
K1 Ornamental potential
K1 Precision agriculture
K1 Solanaceae
K1 Thermal stress
K1 Redes neuronales artificiales
K1 Neural networks (Computer science)
K1 Agricultura
K1 Agriculture
AB Being capable of accurately predicting morphological parameters of the plant weeks before achieving fruit maturation is of great importance in the production and selection of suitable ornamental pepper plants. The objective of this article is evaluating the feasibility and assessing the performance of CNN-based models using RGB images as input to forecast two morphological parameters: plant height and canopy diameter. To this end, four CNN-based models are proposed to predict these morphological parameters in four different scenarios: first, using as input a single image of the plant; second, using as input several images from different viewpoints of the plant acquired on the same date; third, using as input two images from two consecutive weeks; and fourth, using as input a set of images consisting of one image from each week up to the current date. The results show that it is possible to accurately predict both plant height and canopy diameter. The RMSE for a forecast performed 6 weeks in advance to the actual measurements was below 4.5 cm and 4.2 cm, respectively. When information from previous weeks is added to the model, better results can be achieved and as the prediction date gets closer to the assessment date the accuracy improves as well.
PB Springer
SN 0941-0643
YR 2024
FD 2024
LK http://hdl.handle.net/10259/9759
UL http://hdl.handle.net/10259/9759
LA eng
NO Financiación en Acceso Abierto gracias al convenio CRUE-CSIC con Springer Nature. Esta investigación fue financiada por el CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico), subvención número 408444/2021–5.
DS Repositorio Institucional de la Universidad de Burgos
RD 06-ene-2025