## 1 Introduction  
The ornamental horticulture industry has witnessed a significant increase in demand for unique and aesthetically pleasing plant varieties, particularly in urban [1] and residential landscapes [2]. Among these, pepper plants (_Capsicum_ spp.) have garnered attention not only for their culinary uses but also for their ornamental potential due to their vibrant fruits and diverse foliage characteristics [3; 4; 5; 6]. As the market for ornamental plants continues to expand [7; 8; 9], there is a pressing need for effective classification and prediction methods to identify and promote the most desirable varieties [10; 11]. Anticipating the ornamental value of pepper plants is crucial for genetic breeding programs and genotype selection because it enables breeders to focus on traits that enhance the aesthetic appeal and marketability of plants [12]. By evaluating characteristics such as plant habit, leaf density, fruit color, size, and arrangement at early developmental stages, breeders can identify promising genotypes with high ornamental potential. This early selection reduces time, effort, and resources spent on less desirable lines, streamlining the breeding process. Additionally, it allows breeders to cater to specific consumer preferences or landscing needs, such as compact growth for container gardening or vibrant fruit colors for decorative purposes. Accurate anticipation of ornamental traits supports the development of unique and competitive cultivars, ensuring the success and sustainability of breeding programs in meeting market demands [13]. Recent advancements in imaging technology and physiological measurement techniques have engendered novel opportunities for plant phenotyping, enabling researchers to assess ornamental traits more efficiently than traditional methods [14; 15; 16]. Physiological and morphological measurements, such as chlorophyll content, leaf area, gas exchange rates, plant height, number of fruits, among others, provide critical insights into the health and vigor of plants, which are essential for ornamental quality assessment [17; 18; 19]. On the other hand, RGB imaging has emerged as a powerful tool for non-destructive monitoring of plant traits, allowing for rapid assessment of morphology, color, and spatial distribution of features [20; 21; 22]. The integration of these two approaches could enhance the accuracy of classification and prediction models for ornamental potential. RGB images also allow for rapid assessment of morphology, color, and spatial distribution of organs such as flowers and fruits [20; 21; 22]. In rapeseed (Brassica napus), models for counting the number of flowers, as well as estimating pod length and width, have been developed [23]. RGB images combined with simple color index transformations have also been effective in quantifying color diversity in ornamental pepper fruits, providing useful results for breeding programs [21]. Despite many studies in horticulture having used deep learning [24], especially Convolutional Neural Networks (CNNs) such as ResNet, VGG, and EfficientNet, little work has been conducted regarding ornamental potential estimation from RGB images [25]. This study aimed to compare the efficacy of morphological measurements and RGB images as data sources for classifying the ornamental potential of various pepper plant genotypes. In addition, the capability of undertaking predictions based on photos taken weeks before assessment is studied too. This comparison aims at identifying the feasibility of getting rid of costly measurements and replacing them by simply taking photos and further processing them. By employing machine learning algorithms, the capability of each data source to accurately distinguish between ornamental and non-ornamental varieties will be evaluated. This research not only highlights the ornamental potential of pepper plants but also contributes to the broader field of plant phenotyping, facilitating the selection of superior cultivars for ornamental purposes.