Antonia Maiara Marques Do Nascimento

High temperatures can significantly affect plant growth, development, and physiology. The identification of genotypes with desirable morphological and physiological responses under contrasting growing conditions is therefore relevant for ornamental pepper breeding. This study aimed to characterize the phenotypic responses of ten ornamental pepper ( Capsicum spp.) accessions grown under controlled (27.15 ± 1.16 °C; VPD = 2.1 ± 0.23 kPa) and high-temperature/high-VPD (39.03 ± 1.98 °C; VPD = 6.23 ± 0.85 kPa) greenhouse conditions using morphological, phenological, and physiological traits and self-organizing maps (SOMs). Differences in plant architecture and physiological responses were observed among accessions within each growth condition, while descriptive differences were also observed between conditions. In general, plant height and canopy diameter were lower under the high-temperature/high-VPD condition, while the magnitude of gas-exchange responses varied among accessions. SOM sensitivity analysis supported a 2 × 3 topology with six neurons for both conditions, based on the balance among training quality, topological preservation, and stability across 100 independent runs. Three superclasses were identified under each condition, although their composition and multivariate profiles differed between environments. CPCE 005 and CPCE 007 remained associated under both conditions and, under high-temperature/high-VPD, were characterized by compact plant architecture combined with higher relative values of gas-exchange traits. CPCE 014 showed a distinct multivariate profile under the contrasting conditions and formed an individual superclass. Overall, SOM analysis revealed a reorganization of the relationships among morphological and physiological traits between growing conditions and identified distinct accession-specific response patterns. These results highlight CPCE 005, CPCE 007, and CPCE 014 as promising accessions for further validation in breeding studies targeting ornamental peppers grown under high-temperature and high atmospheric evaporative demand.

The ornamental potential of Portulaca umbraticola Kunth. stems from variation in phenotypic characteristics, enabling the selection of genotypes with desirable characters for breeding. In this context, the aim of this work was to study the ornamental potential in purslane Kunth. and to select genotypes collected across the states of Ceará, Rio Grande do Norte, and Paraíba, using the multi-characteristic genotype–ideotype distance index for simultaneous selection, comparing it with the Smith–Hazel index. The experiment was carried out in a greenhouse using a completely randomized design, comprising 22 treatments with three replications. Evaluations were performed 40 days after planting, at full flowering stage, and ten quantitative traits were measured. The dataset was then submitted to multivariate analysis to emphasize distinctions among the genotypes. The P. umbraticola genotypes were classified into three distinct groups, indicating genetic variability for the phenotypic traits assessed. Accessions AC16 and AC19 combined favorable traits associated with ornamental value, making them promising candidates for further selection and evaluation. Future studies should evaluate their combining ability and hybrid performance to support the development of superior ornamental cultivars. The genotypes AC16 and AC19 exhibited ornamental potential and are recommended for selection based on multivariate criteria to support the ongoing purslane breeding program.

Water stress compromises morphological and physiological aspects of pepper plants (Capsicum spp.), affecting their development. Polyethylene glycol (PEG) is widely used to simulate drought conditions in vitro, making it possible to assess crop tolerance to water restriction and assist in the selection of more resilient varieties. This study aimed to evaluate the in vitro germination, initial growth, and stomatal characteristics of ornamental pepper accessions subjected to different levels of water stress induced by PEG 6000. The study assessed seven pepper accessions and five concentrations of PEG 6000 (0%, 5%, 10%, 15%, and 20%) in Murashigue and Skoog media. Under PEG-induced water deficit conditions, no germination occurred at concentrations above 5%, unlike other studies observed with Capsicum. This result highlights the effect of genotype on tolerance to PEG levels. Water deficit resulted in a 13.1% reduction in germination compared to the control (p<0.05), in addition to significantly affecting the percentage of dry matter in the shoot and roots. A decrease in stomatal density (51.6 to 35.4 stomata mm⁻²) and in the width of the stomatal midpoint (from 14.16 µm to 12.62 µm) was also observed. Among the genotypes evaluated, accessions CPCE 020, CPCE 018, and CPCE 011 showed better performance under stress, demonstrating greater tolerance to water deficit.

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.

Kalanchoe daigremontiana is an ornamental species propagated exclusively asexually. However, no in vitro studies have been conducted to assess the relationship between changes in the culture environment and genetic variations of K. daigremontiana. Therefore, this study aimed to determine the optimal concentration of salts in the Murashige & Skoog (MS) medium and the optimal concentration of sucrose in the culture medium, in addition to the quality of light, in the development of in vitro plantlets from leaf explants of K. daigremontiana. Additionally, a genetic evaluation of the resulting plantlets was conducted to study phenotypic changes that could be attributed to somaclonal variation using Random Amplified Polymorphic DNA (RAPD) markers. For this purpose, leaf explants of K. daigremontiana were disinfected and inoculated in different salt concentrations (25, 50, and 100%) of MS medium without sucrose. The cultures were transferred to a growth chamber and subjected to three light conditions (white, blue, and red) with a 16-hour photoperiod, plus the absence of light, at a temperature of 27 ± 1 °C. Explants were also inoculated in 50% salt concentrations of MS medium supplemented with various concentrations of sucrose (control - without sucrose, 1.5, 3.0, 4.5, and 6.0%). K. daigremontiana leaf explants showed optimal development under white light and in any MS medium salt concentrations. A sucrose-free culture medium was viable, and no genetic variation was observed in the plantlets compared to the parent plants under the tested conditions.

Being capable of accurately predicting morphological parameters of the plant weeks before achieving fruit maturation is of great importance in the production and selection of suitable ornamental pepper plants. The objective of this article is evaluating the feasibility and assessing the performance of CNN-based models using RGB images as input to forecast two morphological parameters: plant height and canopy diameter. To this end, four CNN-based models are proposed to predict these morphological parameters in four different scenarios: first, using as input a single image of the plant; second, using as input several images from different viewpoints of the plant acquired on the same date; third, using as input two images from two consecutive weeks; and fourth, using as input a set of images consisting of one image from each week up to the current date. The results show that it is possible to accurately predict both plant height and canopy diameter. The RMSE for a forecast performed 6 weeks in advance to the actual measurements was below 4.5 cm and 4.2 cm, respectively. When information from previous weeks is added to the model, better results can be achieved and as the prediction date gets closer to the assessment date the accuracy improves as well.

Fruit colour diversity within different ripening stages confers ornamental value for pepper plants. Using images can be helpful in analysing the fruit colour‐related genetic diversity and enable selecting accessions for ornamental purposes by avoiding subjectiveness. This study aimed to evaluate the phenotypic diversity among accessions of ornamental pepper using image processing techniques to identify fruit colours. Photos from 40 fruits from each of 12 accessions were captured by a camera from a smartphone. Separate channels and colour indices were extracted from the images to analyse the accessions using both dendrogram and principal components analysis. Dendrogram analysis allowed separating accessions with predominance of dark fruits from those whose fruits showed lighter and more vivid colours, with a cophenetic correlation coefficient of 0.811. The VARI and NGRDI vegetation indices were efficient in discriminating fruits with a predominance of green colour, and the BGI could be used to discriminate accessions with predominantly reddish‐brown fruits. The proposed method can be used in small‐scale breeding programs for accurately assessing the development of varieties regarding their fruit colour diversity.

Being capable of accurately predicting morphological parameters of the plant weeks before achieving fruit maturation is of great importance in the production and selection of suitable ornamental pepper plants. The objective of this article is evaluating the feasibility and assessing the performance of CNN-based models using RGB images as input to forecast two morphological parameters: plant height and canopy diameter. To this end, four CNN-based models are proposed to predict these morphological parameters in four different scenarios: first, using as input a single image of the plant; second, using as input several images from different viewpoints of the plant acquired on the same date; third, using as input two images from two consecutive weeks; and fourth, using as input a set of images consisting of one image from each week up to the current date. The results show that it is possible to accurately predict both plant height and canopy diameter. The RMSE for a forecast performed 6 weeks in advance to the actual measurements was below 4.5 cm and 4.2 cm, respectively. When information from previous weeks is added to the model, better results can be achieved and as the prediction date gets closer to the assessment date the accuracy improves as well.