2026-04-28T11:35:15Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/97592024-12-06T01:05:20Zcom_10259_8983com_10259_5086com_10259_2604col_10259_8984

2024-12-05T12:23:31Z urn:hdl:10259/9759 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 Ruiz González, Rubén Nascimento, Antonia Maiara Marques do Santos, Marcos Bruno da Costa Porto, Rutten Kécio Soares de Brito Medeiros, Artur Mendes Santos, Fábio Sandro dos Martínez-Martínez, Víctor Barroso, Priscila Alves Image-based temporal prediction Morphological parameters Ornamental potential Precision agriculture Solanaceae Thermal stress 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. 2024-12-05T12:23:31Z 2024-12-05T12:23:31Z 2024 info:eu-repo/semantics/article 0941-0643 http://hdl.handle.net/10259/9759 10.1007/s00521-024-10502-w 1433-3058 eng Neural Computing and Applications. 2024, https://doi.org/10.1007/s00521-024-10502-w http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Springer