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<dc:creator>Nascimento, Antonia Maiara Marques do</dc:creator>
<dc:creator>Ruiz González, Rubén</dc:creator>
<dc:creator>Martínez-Martínez, Víctor</dc:creator>
<dc:creator>Medeiros, Artur Mendes</dc:creator>
<dc:creator>Santos, Fábio Sandro dos</dc:creator>
<dc:creator>Rêgo, Elizanilda Ramalho do</dc:creator>
<dc:creator>Pimenta, Samy</dc:creator>
<dc:creator>Sudré, Cláudia Pombo</dc:creator>
<dc:creator>Bento, Cintia dos Santos</dc:creator>
<dc:creator>Cambra Baseca, Carlos</dc:creator>
<dc:creator>Barroso, Priscila Alves</dc:creator>
<dc:date>2025-07</dc:date>
<dc:description>Anticipating the ornamental quality of plants is of significant importance for genetic breeding&#xd;
programs. This study investigated the potential of predicting and classifying whether&#xd;
ornamental pepper plants will exhibit desirable ornamental traits based on RGB images,&#xd;
comparing these results with an approach relying on morphological measurements. To&#xd;
achieve this, pepper plants from fifteen accessions were cultivated, and photographs were&#xd;
taken weekly throughout their growth cycle until fruit maturation. A Vision Transformer&#xd;
(ViT)-based model was employed to predict the suitability of the plants for ornamental purposes,&#xd;
and its predictions were validated against assessments conducted by eight experts.&#xd;
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&#xd;
of plants can be accurately estimated and predicted up to seven weeks in advance from&#xd;
photos, with accuracy over 80%. Interestingly, higher-resolution RGB images did not significantly&#xd;
improve the accuracy of the ViT model. Furthermore, the estimation of ornamental&#xd;
potential using morphological measurements and RGB images yielded similar accuracy,&#xd;
indicating that a single photograph can effectively replace costly and time-consuming&#xd;
morphological measurements. As far as the authors are aware, this work is the first to&#xd;
forecast the ornamental potential of pepper plants (Capsicum spp.) multiple weeks ahead&#xd;
of time using image-based deep learning models.</dc:description>
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<dc:identifier>https://hdl.handle.net/10259/10875</dc:identifier>
<dc:language>eng</dc:language>
<dc:publisher>MDPI</dc:publisher>
<dc:title>Ornamental Potential Classification and Prediction for Pepper Plants (Capsicum spp.): A Comparison Using Morphological Measurements and RGB Images as Data Source</dc:title>
<dc:type>info:eu-repo/semantics/article</dc:type>
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