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Research Project: Development of Pathogen- and Plant-Based Genetic Tools and Disease Mitigation Methods for Tropical Perennial Crops

Location: Sustainable Perennial Crops Laboratory

Title: Cacao floral traits are shaped by the interplay of flower position and genotype

Author
item LIM, SEUNGHYUN - Orise Fellow
item Baek, Insuck
item HONG, SEOK MIN - Ulsan National Institute Of Science And Technology (UNIST)
item LEE, YOONJUNG - University Of Minnesota
item Kirubakaran, Silvas
item Kim, Moon
item Meinhardt, Lyndel
item Park, Sunchung
item Ahn, Ezekiel

Submitted to: iScience
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/30/2025
Publication Date: 2/3/2025
Citation: Lim, S., Baek, I., Hong, S., Lee, Y., Kirubakaran, S.J., Kim, M.S., Meinhardt, L.W., Park, S., Ahn, E.J. 2025. Cacao floral traits are shaped by the interplay of flower position and genotype. iScience. https://doi.org/10.1016/j.heliyon.2025.e42407.
DOI: https://doi.org/10.1016/j.heliyon.2025.e42407

Interpretive Summary: We conducted a study on cacao flowers to explore how their position on the tree and the cacao genotype affect their characteristics. Our meticulous measurements included the size, shape, and blooming patterns of the flowers at different heights on the tree. The data we collected was then analyzed using advanced computer programs in an attempt to predict the type of cacao tree based solely on flower measurements. The implications of this research are profound. For scientists, it offers valuable insights into the factors that influence flower development in cacao plants. This knowledge can be used to develop new cacao varieties that yield more flowers, leading to increased production and a more sustainable supply of cacao beans. For farmers, understanding how flower position and genotype impact flower traits can help optimize cultivation practices, potentially resulting in larger harvests and improved livelihoods.

Technical Abstract: This study investigated the influence of vertical flower position on cacao flower morphology and abundance in two genotypes, CCN51 and SCA6. Flower traits, including size (lateral area size, length, width, and perimeter), shape (circularity, length-to-width ratio, and the distance between the intersection of length and width (IS) and the center of gravity (CG)), and developmental stage, were measured at different tree heights under controlled greenhouse conditions. We observed significant variations in these traits both between the two genotypes and across vertical positions, indicating the impact of both genetic background and environmental factors on cacao reproductive biology. Leveraging machine learning, we predicted genotypes based on the flower measurements, with Support Vector Machine (SVM) demonstrating the highest accuracy. These findings provide crucial insights into the phenotypic diversity of cacao flowers and the potential of machine learning for genotype identification, informing future breeding and cultivation strategies for optimized cacao productivity.