Ornamental Potential Classification and Prediction for Pepper Plants (<i>Capsicum</i> spp.): A Comparison Using Morphological Measurements and RGB Images as Data Source

Ornamental Potential Classification and Prediction for Pepper Plants (<i>Capsicum</i> spp.): A Comparison Using Morphological Measurements and RGB Images as Data Source

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...

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Main Authors: Antonia Maiara Marques do Nascimento, Ruben Ruiz-Gonzalez, Víctor Martínez-Martínez, Artur Mendes Medeiros, Fábio Sandro dos Santos, Elizanilda Ramalho do Rêgo, Samy Pimenta, Cláudia Pombo Sudré, Cintia dos Santos Bento, Carlos Cambra, Priscila Alves Barroso
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
ornamental plants
RGB image analysis
Vision Transformer (ViT)
XGBoost
morphological measurements
plant breeding
Online Access:https://www.mdpi.com/2076-3417/15/14/7801
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Summary: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 (<i>Capsicum</i> spp.) multiple weeks ahead of time using image-based deep learning models.
ISSN:2076-3417

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