2026-06-17T21:14:47Zhttps://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 Ruiz González, Rubén Martínez-Martínez, Víctor Medeiros, Artur Mendes Santos, Fábio Sandro dos Rêgo, Elizanilda Ramalho do Pimenta, Samy Sudré, Cláudia Pombo Bento, Cintia dos Santos Cambra Baseca, Carlos Barroso, Priscila Alves Ornamental plants RGB image analysis Vision Transformer (ViT) XGBoost Morphological measurements Plant breeding Phenotypic prediction 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. 2025-09-15 2025-09-15 2025-07 info:eu-repo/semantics/article 2076-3417 https://hdl.handle.net/10259/10875 10.3390/app15147801 2076-3417 eng Applied sciences. 2025, V. 15, n. 14, 7801 https://doi.org/10.3390/app15147801 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI