2026-04-28T11:39:37Zhttps://riubu.ubu.es/oai/request

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

Ruiz González, Rubén 735 600 0000-0001-7006-2395 Nascimento, Antonia Maiara Marques do 3221df8c-62e4-4172-9953-0ab2fb113875 600 Santos, Marcos Bruno da Costa edc8910c-39d6-4c4f-95e2-486f4db5ecc6 Porto, Rutten Kécio Soares de Brito ae51edef-7663-43f2-a913-fca780038208 Medeiros, Artur Mendes 50908126-d649-49fb-879f-dec64a23c6fa Santos, Fábio Sandro dos 94c8b1ed-ec6b-4c6e-8fcb-98f7d6a6b91e Martínez-Martínez, Víctor 7ca8d8b7-0ebf-4cac-bd98-50c192735635 600 Barroso, Priscila Alves 80d4aa30-9e12-4f17-b2e0-2e12f1ae7417 2024-12-05T12:23:31Z 2024-12-05T12:23:31Z 2024 0941-0643 http://hdl.handle.net/10259/9759 10.1007/s00521-024-10502-w 1433-3058 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. 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. application/pdf eng Springer Neural Computing and Applications. 2024, https://doi.org/10.1007/s00521-024-10502-w Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Image-based temporal prediction Morphological parameters Ornamental potential Precision agriculture Solanaceae Thermal stress Redes neuronales artificiales Agricultura Neural networks (Computer science) Agriculture 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 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Neural Computing and Applications THUMBNAIL Ruiz-cna_2024.pdf.jpg Ruiz-cna_2024.pdf.jpg IM Thumbnail image/jpeg 4569 https://riubu.ubu.es/bitstream/10259/9759/4/Ruiz-cna_2024.pdf.jpg 2f70a61daa8d9eb78fd0a61a16646ca9 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/9759/3/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/9759/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Ruiz-cna_2024.pdf Ruiz-cna_2024.pdf application/pdf 2040322 https://riubu.ubu.es/bitstream/10259/9759/1/Ruiz-cna_2024.pdf 5cc8c1a401be2b2816bd1b89fd462eb2 MD5 1 10259/9759 oai:riubu.ubu.es:10259/9759 2024-12-06 02:05:20.337 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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