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.
