2026-05-07T19:02:33Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/108752025-09-16T00:05:35Zcom_10259_8983com_10259_5086com_10259_2604com_10259_3847col_10259_8984col_10259_3848

Nascimento, Antonia Maiara Marques do 927 600 0000-0002-9878-2084 Ruiz González, Rubén 735 600 0000-0001-7006-2395 Martínez-Martínez, Víctor c75ff194-252f-46a2-b777-baddf6f4674e 600 Medeiros, Artur Mendes 50908126-d649-49fb-879f-dec64a23c6fa 600 Santos, Fábio Sandro dos 94c8b1ed-ec6b-4c6e-8fcb-98f7d6a6b91e 600 Rêgo, Elizanilda Ramalho do f2c53d6d-feea-4b9c-b026-b0d62ad60300 600 Pimenta, Samy a25a8086-2201-4d1b-a5c7-8030cd49a19f 600 Sudré, Cláudia Pombo f2649542-31bf-4554-9276-ac7900de48d8 600 Bento, Cintia dos Santos 02385810-6dfb-45b4-b5d8-d49c186fb1a1 600 Cambra Baseca, Carlos 743 600 0000-0001-5567-9194 Barroso, Priscila Alves 80d4aa30-9e12-4f17-b2e0-2e12f1ae7417 500 2025-09-15T11:04:17Z 2025-09-15T11:04:17Z 2025-07 2076-3417 https://hdl.handle.net/10259/10875 10.3390/app15147801 2076-3417 Anticipating the ornamental quality of plants is of significant importance for genetic breeding programs. This study investigated the potential of predicting and classifying whether ornamental pepper plants will exhibit desirable ornamental traits based on RGB images, comparing these results with an approach relying on morphological measurements. To achieve this, pepper plants from fifteen accessions were cultivated, and photographs were taken weekly throughout their growth cycle until fruit maturation. A Vision Transformer (ViT)-based model was employed to predict the suitability of the plants for ornamental purposes, and its predictions were validated against assessments conducted by eight experts. An XGBoost-based classifier was employed as well for estimations based on morphological measurements with an accuracy over 92%. The results showed that the ornamental suitability of plants can be accurately estimated and predicted up to seven weeks in advance from photos, with accuracy over 80%. Interestingly, higher-resolution RGB images did not significantly improve the accuracy of the ViT model. Furthermore, the estimation of ornamental potential using morphological measurements and RGB images yielded similar accuracy, indicating that a single photograph can effectively replace costly and time-consuming morphological measurements. As far as the authors are aware, this work is the first to forecast the ornamental potential of pepper plants (Capsicum spp.) multiple weeks ahead of time using image-based deep learning models. This research was funded by CNPq (Conselho Nacional de Desenvolvimento Cient\u00EDfico e Tecnol\u00F3gico), grant number 408444/2021-5 application/pdf eng MDPI Applied sciences. 2025, V. 15, n. 14, 7801 https://doi.org/10.3390/app15147801 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Ornamental plants RGB image analysis Vision Transformer (ViT) XGBoost Morphological measurements Plant breeding Phenotypic prediction Pimientos (Plantas) Peppers Ornamental Potential Classification and Prediction for Pepper Plants (Capsicum spp.): A Comparison Using Morphological Measurements and RGB Images as Data Source info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Applied Sciences 15 14 7801 THUMBNAIL Nascimento-ac_2025.pdf.jpg Nascimento-ac_2025.pdf.jpg IM Thumbnail image/jpeg 4707 https://riubu.ubu.es/bitstream/10259/10875/4/Nascimento-ac_2025.pdf.jpg 8a55c89f7e240561c5e47a8abf969f22 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/10875/3/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/10875/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Nascimento-ac_2025.pdf Nascimento-ac_2025.pdf application/pdf 8472352 https://riubu.ubu.es/bitstream/10259/10875/1/Nascimento-ac_2025.pdf 71cfa61b78ee1dd69bfe8dfab43c2efa MD5 1 10259/10875 oai:riubu.ubu.es:10259/10875 2025-09-16 02:05:35.013 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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